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Paper Digest: COVID-19 Related Papers (Math, Statistics & Other)

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In response to the COVID-19 pandemic, Paper Digest Team extracted all COVID-19 papers on our radar, and generated highlight sentences for them. These results are grouped by topics, and sorted by relevance & date.

The article you are reading is on COVID-19 related papers under the topic of “Math, Statistics & Other”. In addition to this ‘static’ page, we also provide a real-time version of this article, which has more coverage and is updated in real time. If you like to read more, we encourage you to check out COVID-19 related papers under the other topics.

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TABLE 1: Paper Digest: COVID-19 Related Papers (Math, Statistics & Other)

Title Author(s) Highlight Category Date
1 Nonparametric Estimation Of Highest Density Regions For COVID-19 PAULA SAAVEDRA-NIEVES In this work, we compare the behavior of classical plug-in methods and a recently proposed hybrid algorithm for highest density regions estimation through an extensive simulation study. STATISTICS.arxiv 2020-10-27
2 E-Commerce Delivery Demand Modeling Framework For An Agent-Based Simulation Platform TAKANORI SAKAI et. al. We propose a novel modeling framework which jointly predicts the average total value of e-commerce purchase, the purchase amount per transaction, and delivery option choices. EESS.arxiv 2020-10-27
3 Triple-view Convolutional Neural Networks For COVID-19 Diagnosis With Chest X-ray JIANJIA ZHANG To address these issues, unlike traditional methods processing each CXR image from a single view, this paper proposes triple-view convolutional neural networks for COVID-19 diagnosis with CXR images. EESS.arxiv 2020-10-27
4 Epidemic Dynamics Via Wavelet Theory And Machine Learning, With Applications To Covid-19 TÔ TAT DAT et. al. We present a novel method for modelling epidemic dynamics by a model selection method using wavelet theory and, for its applications, machine learning based curve fitting techniques. MATH.arxiv 2020-10-26
5 Interpreting Uncertainty In Model Predictions For COVID-19 Diagnosis GAYATHIRI MURUGAMOORTHY et. al. This framework aims to understand the contribution of individual features in the Chest-X-Ray images to predictive uncertainty. EESS.arxiv 2020-10-25
6 Optimal Lockdown For Pandemic Stabilization QIANQIAN MA et. al. We consider the problem of pandemic stabilization through a non-uniform lockdown, where our goal is to reduce economic activity as little as possible while sending the number of infected patients to zero at a prescribed rate. MATH.arxiv 2020-10-24
7 Automated Triage Of COVID-19 From Various Lung Abnormalities Using Chest CT Features DOR AMRAN et. al. In this paper, we propose a fully automated AI based system that takes as input chest CT scans and triages COVID-19 cases. EESS.arxiv 2020-10-24
8 Diverse R-PPG: Camera-Based Heart Rate Estimation For Diverse Subject Skin-Tones And Scenes PRADYUMNA CHARI et. al. In this paper, we present a first attempt to mitigate biases in photoplethysmography performance on darker skin tones by relying on the statistics of imaging physics. EESS.arxiv 2020-10-24
9 Nowcasting COVID-19 Incidence Indicators During The Italian First Outbreak PIERFRANCESCO ALAIMO DI LORO et. al. A novel parametric regression model is proposed to fit incidence data typically collected during epidemics. STATISTICS.arxiv 2020-10-23
10 Comparison Of ARIMA, ETS, NNAR And Hybrid Models To Forecast The Second Wave Of COVID-19 Hospitalizations In Italy GAETANO PERONE In this article, I analysed several time series forecasting methods to predict the spread of COVID-19 second wave in Italy, over the period after October 13, 2020. STATISTICS.arxiv 2020-10-22
11 Robust Estimation Of SARS-CoV-2 Epidemic At US Counties LI HANMO et. al. We propose a robust approach to estimate the heterogeneous progression of SARS-CoV-2 at all US counties having no less than 2 COVID-19 associated deaths, and we develop the daily probability of contracting (PoC) SARS-CoV-2 for a susceptible individual to quantify the risk of SARS-CoV-2 transmission in community. STATISTICS.arxiv 2020-10-22
12 Deep Learning For Distinguishing Normal Versus Abnormal Chest Radiographs And Generalization To Unseen Diseases ZAID NABULSI et. al. In this work, we developed and evaluated an AI system to classify CXRs as normal or abnormal. EESS.arxiv 2020-10-21
13 Excess Deaths, Baselines, Z-scores, P-scores And Peaks LAURIE DAVIES The data in the article is taken from these three sources. STATISTICS.arxiv 2020-10-20
14 Synthesis Of COVID-19 Chest X-rays Using Unpaired Image-to-Image Translation HASIB ZUNAIR et. al. Motivated by the lack of publicly available datasets of chest radiographs of positive patients with Coronavirus disease 2019 (COVID-19), we build the first-of-its-kind open dataset of synthetic COVID-19 chest X-ray images of high fidelity using an unsupervised domain adaptation approach by leveraging class conditioning and adversarial training. EESS.arxiv 2020-10-20
15 Detection Of COVID-19 Through The Analysis Of Vocal Fold Oscillations MAHMOUD AL ISMAIL et. al. Our goal is to validate this hypothesis, and to quantitatively characterize the changes observed to enable the detection of COVID-19 from voice. EESS.arxiv 2020-10-20
16 Forecasting Unemployment Using Internet Search Data Via PRISM DINGDONG YI et. al. To this end, we present a novel method PRISM (Penalized Regression with Inferred Seasonality Module), which uses publicly available online search data from Google. STATISTICS.arxiv 2020-10-19
17 GASNet: Weakly-supervised Framework For COVID-19 Lesion Segmentation ZHANWEI XU et. al. In this paper, we propose a weakly-supervised lesion segmentation framework by embedding the Generative Adversarial training process into the Segmentation Network, which is called GASNet. EESS.arxiv 2020-10-19
18 On Bayesian Sparse Canonical Correlation Analysis Via Rayleigh Quotient Framework QIUYUN ZHU et. al. We propose a semi-parametric Bayesian method for the principal canonical pair that employs the scaled Rayleigh quotient as a quasi-log-likelihood with the spike-and-slab prior as the sparse constraints. STATISTICS.arxiv 2020-10-16
19 CT Image Segmentation For Inflamed And Fibrotic Lungs Using A Multi-Resolution Convolutional Neural Network SARAH E. GERARD et. al. The purpose of this study was to develop a fully-automated segmentation algorithm, robust to various density enhancing lung abnormalities, to facilitate rapid quantitative analysis of computed tomography images. EESS.arxiv 2020-10-16
20 Vehicular Networks For Combating A Worldwide Pandemic: Preventing The Spread Of COVID-19 AHMET M. ELBIR et. al. This article provides an extensive discussion on vehicular networking for combating a pandemic. EESS.arxiv 2020-10-15
21 Early-stage COVID-19 Diagnosis In Presence Of Limited Posteroanterior Chest X-ray Images Via Novel Pinball-OCSVM SANJAY KUMAR SONBHADRA et. al. Inspired by this phenomenon, this article proposes a novel PB-OCSVM model to work in presence of limited COVID-19 positive CXR samples with objectives to maximize the learning efficiency while minimize the false-positive and false-negative predictions. EESS.arxiv 2020-10-15
22 Revisiting SIR In The Age Of COVID-19: Explicit Solutions And Control Problems VIVEK S. BORKAR et. al. The non-population conserving SIR (SIR-NC) model to describe the spread of infections in a community is proposed and studied. MATH.arxiv 2020-10-13
23 An Empirical Study On Detecting COVID-19 In Chest X-ray Images Using Deep Learning Based Methods RAMTIN BABAEIPOUR et. al. In this paper, we have studied the usage of x-ray pictures which are ubiquitous, for the classification of COVID-19 chest Xray images, by the existing convolutional neural networks (CNNs). EESS.arxiv 2020-10-10
24 Assessing Excess Mortality In Times Of Pandemics Based On Principal Component Analysis Of Weekly Mortality Data — The Case Of COVID-19 PATRIZIO VANELLA et. al. We present a blend of classical epidemiological approaches to estimating excess mortality during extraordinary events with an established demographic approach in mortality forecasting, namely a Lee-Carter type model, which covers the named limitations and draws a more realistic picture of the excess mortality. STATISTICS.arxiv 2020-10-10
25 Optimal Control Of Vaccination And Plasma Transfusion With Potential Usefulness For COVID-19 JULIANA COURAS et. al. In this chapter, we introduce two control functions in the compartmental SEIR model representing vaccination and plasma transfusion. MATH.arxiv 2020-10-08
26 M3Lung-Sys: A Deep Learning System For Multi-Class Lung Pneumonia Screening From CT Imaging XUELIN QIAN et. al. Considering the limited training cases and resources (e.g, time and budget), we propose a Multi-task Multi-slice Deep Learning System (M3Lung-Sys) for multi-class lung pneumonia screening from CT imaging, which only consists of two 2D CNN networks, i.e., slice- and patient-level classification networks. EESS.arxiv 2020-10-07
27 Modeling The Commodity Prices Of Base Metals In Indian Commodity Market Using A Higher Order Markovian Approach SURYADEEPTO NAG et. al. Modeling The Commodity Prices Of Base Metals In Indian Commodity Market Using A Higher Order Markovian Approach FINANCE.arxiv 2020-10-07
28 No Increase In COVID-19 Mortality After The 2020 Primary Elections In The USA ERIC M. FELTHAM et. al. We examined the impact of voting on the spread of COVID-19 after the US primary elections held from March 17 to July 11, 2020 (1574 counties across 34 states). STATISTICS.arxiv 2020-10-06
29 COVIDomaly: A Deep Convolutional Autoencoder Approach For Detecting Early Cases Of COVID-19 FARAZ KHOSHBAKHTIAN et. al. To solve this problem, we present COVIDomaly, a convolutional autoencoder framework to detect unseen COVID-19 cases from the chest radiographs. EESS.arxiv 2020-10-06
30 Forecasting COVID-19 Daily Cases Using Phone Call Data BAHMAN ROSTAMI-TABAR et. al. In this study, we propose a simple Multiple Linear Regression model, optimised to use call data to forecast the number of daily confirmed cases. STATISTICS.arxiv 2020-10-05
31 Modeling Of Covid-19 Pandemic In Cyprus SERGIOS AGAPIOU et. al. The aim of this paper is to present the efforts of this new, multidisciplinary research team in modelling the COVID-19 pandemic in the Republic of Cyprus. STATISTICS.arxiv 2020-10-05
32 Correlation Between Air And Urban Travelling With New Confirmed Cases Of COVID-19 A Case Study SOHEIL SHIRVANI et. al. In this study, the correlation between traveling between cities with new confirmed cases of COVID-19 in Iran is demonstrated. STATISTICS.arxiv 2020-10-03
33 COVID-19 Classification Of X-ray Images Using Deep Neural Networks ELISHA GOLDSTEIN et. al. In this retrospective study, a classifier was built using a pre-trained deep learning model (ReNet50) and enhanced by data augmentation and lung segmentation to detect COVID-19 in frontal CXR images collected between January 2018 and July 2020 in four hospitals in Israel. EESS.arxiv 2020-10-03
34 Gold Standard Pairs Trading Rules: Are They Valid? MIROSLAV FIL We employ the most common distance and cointegration methods on US equities from 1990 to 2020 including the Covid-19 crisis. FINANCE.arxiv 2020-10-02
35 COVID-19, Economic Policy Uncertainty And Stock Market Crash Risk ZHIFENG LIU et. al. To this end, we use the GARCH-S (GARCH with skewness) model to estimate daily skewness as a proxy for the stock market crash risk. FINANCE.arxiv 2020-10-02
36 Commuting Network Spillovers And COVID-19 Deaths Across US Counties CHRISTOPHER SETO et. al. This study explored how population mobility flows form commuting networks across US counties and influence the spread of COVID-19. STATISTICS.arxiv 2020-10-02
37 Identification Of Images Of COVID-19 From Chest Computed Tomography (CT) Images Using Deep Learning: Comparing COGNEX VisionPro Deep Learning 1.0 Software With Open Source Convolutional Neural Networks ARJUN SARKAR et. al. In this study, we train the classification model on 82,818 chest CT training and validation images from the COVIDx-CT dataset in 3 classes – normal, pneumonia, and COVID-19 and then test the results of the classification on the 21,191 test images are compared with the results of COVIDNet-CT and various other state of the art Deep Learning models from the open-source community. EESS.arxiv 2020-10-01
38 Model-based Bayesian Inference Of Disease Outbreak Dynamics With Invertible Neural Networks STEFAN T. RADEV et. al. With this work, we introduce a novel coupling between epidemiological models and specialized neural network architectures. STATISTICS.arxiv 2020-10-01
39 Modeling And Analysis Of The Effect Of COVID-19 On The Stock Price: V And L-shape Recovery AJIT MAHATA et. al. We assume that during the crash, the stock price fall is independent of the $\phi$. FINANCE.arxiv 2020-09-28
40 A Hierarchical Spatio-temporal Model To Analyze Relative Risk Variations Of COVID-19: A Focus On Spain, Italy And Germany ABDOLLAH JALILIAN et. al. In this paper, we use a spatio-temporal stochastic model to explain the temporal and spatial variations in the daily number of new confirmed cases in Spain, Italy and Germany from late February to mid September 2020. STATISTICS.arxiv 2020-09-28
41 An Analysis Of Network Filtering Methods To Sovereign Bond Yields During COVID-19 RAYMOND KA-KAY PANG et. al. In this work, we investigate the impact of the COVID-19 pandemic on sovereign bond yields amongst European countries. FINANCE.arxiv 2020-09-28
42 COVID-19 Infection Map Generation And Detection From Chest X-Ray Images AYSEN DEGERLI et. al. This study proposes a novel method for the joint localization, severity grading, and detection of COVID-19 from CXR images by generating the so-called infection maps that can accurately localize and grade the severity of COVID-19 infection. Furthermore, we publicly release the first CXR dataset with the ground-truth segmentation masks of the COVID-19 infected regions. EESS.arxiv 2020-09-26
43 Estimation Of The Incubation Time Distribution For COVID-19 PIET GROENEBOOM We consider nonparametric estimation of the incubation time distribution of COVID-19. STATISTICS.arxiv 2020-09-26
44 Potential Features Of ICU Admission In X-ray Images Of COVID-19 Patients DOUGLAS P. S. GOMES et. al. On this account, this paper presents a methodology for extracting features from a limited data set with outcome label (patient required ICU admission or not) and correlating its significance to an additional, larger data set with hundreds of images. EESS.arxiv 2020-09-26
45 The Impact Of Modelling Choices On Modelling Outcomes: A Spatio-temporal Study Of The Association Between COVID-19 Spread And Environmental Conditions In Catalonia (Spain) ÁLVARO BRIZ-REDÓN In this paper, a COVID-19 dataset containing the number of daily cases registered in the regions of Catalonia (Spain) since the start of the pandemic is analysed. STATISTICS.arxiv 2020-09-26
46 Deep Learning-based Four-region Lung Segmentation In Chest Radiography For COVID-19 Diagnosis YOUNG-GON KIM et. al. To address these, we proposed a four region lung segmentation method to assist accurate quantification of COVID 19 pneumonia. EESS.arxiv 2020-09-26
47 Efficient Calibration For Imperfect Epidemic Models With Applications To The Analysis Of COVID-19 CHIH-LI SUNG et. al. It is shown in this paper that this method is inefficient, particularly when the epidemic models are developed based on certain simplifications of reality, also known as imperfect models which are commonly used in practice. STATISTICS.arxiv 2020-09-26
48 ECOVNet: An Ensemble Of Deep Convolutional Neural Networks Based On EfficientNet To Detect COVID-19 From Chest X-rays NIHAD KARIM CHOWDHURY et. al. This paper proposed an ensemble of deep convolutional neural networks (CNN) based on EfficientNet, named ECOVNet, to detect COVID-19 using a large chest X-ray data set. EESS.arxiv 2020-09-24
49 Attention With Multiple Sources Knowledges For COVID-19 From CT Images DUY M. H. NGUYEN et. al. Attention With Multiple Sources Knowledges For COVID-19 From CT Images EESS.arxiv 2020-09-23
50 An Elementary Approach For Minimax Estimation Of Bernoulli Proportion In The Restricted Parameter Space HEEJUNE SHEEN et. al. We present an elementary mathematical method to find the minimax estimator of the Bernoulli proportion $\theta$ under the squared error loss when $\theta$ belongs to the restricted parameter space of the form $\Omega = [0, \eta]$ for some pre-specified constant $0 \leq \eta \leq 1$. MATH.arxiv 2020-09-23
51 Classification Of COVID-19 In CT Scans Using Multi-Source Transfer Learning ALEJANDRO R. MARTINEZ In this study, we propose the use of Multi-Source Transfer Learning (MSTL) to improve upon traditional Transfer Learning for the classification of COVID-19 from CT scans. EESS.arxiv 2020-09-22
52 Complexity In Economic And Social Systems: Cryptocurrency Market At Around COVID-19 STANISŁAW DROŻDŻ et. al. We approach this subject from all three perspectives based on data recorded between January 2019 and June 2020. FINANCE.arxiv 2020-09-21
53 Improving Automated COVID-19 Grading With Convolutional Neural Networks In Computed Tomography Scans: An Ablation Study COEN DE VENTE et. al. Amidst the ongoing pandemic, several studies have shown that COVID-19 classification and grading using computed tomography (CT) images can be automated with convolutional neural networks (CNNs). EESS.arxiv 2020-09-21
54 CCBlock: An Effective Use Of Deep Learning For Automatic Diagnosis Of COVID-19 Using X-Ray Images ALI AL-BAWI et. al. Materials and Methods: Based on the enhancement of the classical visual geometry group (VGG) network with the convolutional COVID block (CCBlock), an efficient screening model was proposed in this study to diagnose and distinguish patients with the COVID-19 from those with pneumonia and the healthy people through radiography. EESS.arxiv 2020-09-21
55 A Relationship Between SIR Model And Generalized Logistic Distribution With Applications To SARS And COVID-19 HIDEO HIROSE This paper shows that the generalized logistic distribution model is derived from the well-known compartment model, consisting of susceptible, infected and recovered compartments, abbreviated as the SIR model, under certain conditions. STATISTICS.arxiv 2020-09-21
56 Impact Of Lung Segmentation On The Diagnosis And Explanation Of COVID-19 In Chest X-ray Images LUCAS O. TEIXEIRA et. al. This paper’s main objectives are to demonstrate the impact of lung segmentation in COVID-19 automatic identification using CXR images and evaluate which contents of the image decisively contribute to the identification. To evaluate our approach, we built a database named RYDLS-20-v2, following our previous publication and the COVIDx database guidelines. EESS.arxiv 2020-09-21
57 Bias Field Poses A Threat To DNN-based X-Ray Recognition BINYU TIAN et. al. In this paper, we study this problem based on the recent adversarial attack and propose a brand new attack, i.e., the adversarial bias field attack where the bias field instead of the additive noise works as the adversarial perturbations for fooling the DNNs. EESS.arxiv 2020-09-19
58 Epidemiological Short-term Forecasting With Model Reduction Of Parametric Compartmental Models. Application To The First Pandemic Wave Of COVID-19 In France ATHMANE BAKHTA et. al. We propose a forecasting method for predicting epidemiological health series on a two-week horizon at the regional and interregional level. STATISTICS.arxiv 2020-09-19
59 Estimating The Treatment Effect Of The Juvenile Stay-at-home Order On SARS-CoV-2 Infection Spread In Saline County, Arkansas NEIL HWANG et. al. We investigate the treatment effect of the juvenile stay-at-home order (JSAHO) adopted in Saline County, Arkansas, from April 6 to May 7, in mitigating the growth of SARS-CoV-2 infection rates. STATISTICS.arxiv 2020-09-18
60 Functional Data Analysis: An Application To COVID-19 Data In The United States CHEN TANG et. al. In this paper, we use methods from the functional data analysis to look into the COVID-19 data in the US. STATISTICS.arxiv 2020-09-17
61 Variational Disentanglement For Rare Event Modeling ZIDI XIU et. al. So motivated, we propose a variational disentanglement approach to semi-parametrically learn from rare events in heavily imbalanced classification problems. STATISTICS.arxiv 2020-09-17
62 Detection Of Change Points In Piecewise Polynomial Signals Using Trend Filtering REZA V. MEHRIZI et. al. In this paper, we propose a change point detection method, PRUTF, based on trend filtering. STATISTICS.arxiv 2020-09-17
63 Fears For COVID-19: The Crash Risk Of Stock Market ZHIFENG LIU et. al. This paper investigates the impact of COVID-19 epidemic on the Chinese stock market crash risk. FINANCE.arxiv 2020-09-16
64 High-resolution Spatio-temporal Model For County-level COVID-19 Activity In The U.S SHIXIANG ZHU et. al. We present an interpretable high-resolution spatio-temporal model to estimate COVID-19 deaths together with confirmed cases one-week ahead of the current time, at the county-level and weekly aggregated, in the United States. STATISTICS.arxiv 2020-09-15
65 Adaptive Methods For Short-Term Electricity Load Forecasting During COVID-19 Lockdown In France DAVID OBST et. al. In this paper we introduce adaptive generalized additive models using Kalman filters and fine-tuning to adjust to new electricity consumption patterns. STATISTICS.arxiv 2020-09-14
66 Tracking Disease Outbreaks From Sparse Data With Bayesian Inference BRYAN WILDER et. al. We propose a Bayesian framework which accommodates partial observability in a principled manner. STATISTICS.arxiv 2020-09-12
67 Covid-19 Impact On Cryptocurrencies: Evidence From A Wavelet-based Hurst Exponent M. BELÉN AROUXET et. al. Our study covers the pre-Covid-19 and the subsequent pandemia period. FINANCE.arxiv 2020-09-11
68 Dual Encoder Fusion U-Net (DEFU-Net) For Cross-manufacturer Chest X-ray Segmentation LIPEI ZHANG et. al. In this paper, we propose a dual encoder fusion U-Net framework for Chest X-rays based on Inception Convolutional Neural Network with dilation, Densely Connected Recurrent Convolutional Neural Network, which is named DEFU-Net. EESS.arxiv 2020-09-11
69 Probabilistic And Mean-field Model Of COVID-19 Epidemics With User Mobility And Contact Tracing M. AKIAN et. al. We propose a detailed discrete-time model of COVID-19 epidemics coming in two flavours, mean-field and probabilistic. STATISTICS.arxiv 2020-09-11
70 Bayesian Beta-Binomial Prevalence Estimation Using An Imperfect Test JONATHAN BAXTER An efficient Monte Carlo algorithm for approximating the posterior density is presented, and applied to estimating the Covid-19 infection rate in Santa Clara county, CA using the data reported in [Bendavid 2020]. STATISTICS.arxiv 2020-09-11
71 COVID CT-Net: Predicting Covid-19 From Chest CT Images Using Attentional Convolutional Network SHAKIB YAZDANI et. al. In this work we developed a deep learning framework to predict COVID-19 from CT images. EESS.arxiv 2020-09-10
72 Duality Between Coronavirus Transmission And Air-based Macroscopic Molecular Communication MAX SCHURWANZ et. al. This contribution exploits the duality between a viral infection process and macroscopic air-based molecular communication. EESS.arxiv 2020-09-10
73 Estimation For Network Snowball Sampling: Preventing Pandemics STEVE THOMPSON The needed estimation methods are supplied in this paper. STATISTICS.arxiv 2020-09-09
74 Spatial Bayesian Hierarchical Modelling With Integrated Nested Laplace Approximation NICOLETTA D’ANGELO et. al. We consider latent Gaussian fields for modelling spatial dependence in the context of both spatial point patterns and areal data, providing two different applications. STATISTICS.arxiv 2020-09-08
75 Label-Free Segmentation Of COVID-19 Lesions In Lung CT QINGSONG YAO et. al. To alleviate the burden of data annotation, we herein present a label-free approach for segmenting COVID-19 lesions in CT via pixel-level anomaly modeling that mines out the relevant knowledge from normal CT lung scans. EESS.arxiv 2020-09-08
76 Fuzzy Unique Image Transformation: Defense Against Adversarial Attacks On Deep COVID-19 Models ACHYUT MANI TRIPATHI et. al. This paper presents an evaluation of the performance of deep COVID-19 models against adversarial attacks. EESS.arxiv 2020-09-08
77 Simulating Normalising Constants With Referenced Thermodynamic Integration: Application To COVID-19 Model Selection IWONA HAWRYLUK et. al. In this paper we introduce a variation of the TI method, here referred to as referenced TI, which computes a single model’s evidence in an efficient way by using a reference density such as a multivariate normal – where the normalising constant is known. STATISTICS.arxiv 2020-09-08
78 Anomaly Detection In Stationary Settings: A Permutation-Based Higher Criticism Approach IVO V. STOEPKER et. al. In this work we take a non-parametric stance, and propose a permutation-based variant of the higher criticism statistic not requiring knowledge of the null distribution. STATISTICS.arxiv 2020-09-07
79 Information: To Harvest, To Have And To Hold MARIN VAN HEEL et. al. We here propose novel metrics to assess the amount of information harvested in an experiment, information which is measured in bits. EESS.arxiv 2020-09-07
80 Optimization Of High-dimensional Simulation Models Using Synthetic Data THOMAS BARTZ-BEIELSTEIN et. al. We introduce the BuB simulator, which requires only the specification of plausible intervals for the simulation parameters. STATISTICS.arxiv 2020-09-06
81 Using Multiple Data Streams To Estimate And Forecast SARS-CoV-2 Transmission Dynamics, With Application To The Virus Spread In Orange County, California JONATHAN FINTZI et. al. To overcome this obstacle, at least partially, we propose a Bayesian modeling framework that integrates multiple surveillance data streams. STATISTICS.arxiv 2020-09-06
82 COVID-19: Tail Risk And Predictive Regressions WALTER DISTASO et. al. This project focuses on econometrically justified robust analysis of the effects of the COVID-19 pandemic on the World’s financial markets in different countries across the World. EESS.arxiv 2020-09-05
83 Brownian Snails With Removal: Pandemics In A Diffusing Population GEOFFREY R. GRIMMETT et. al. A stochastic model of susceptible/infected/removed (SIR) type, inspired by COVID-19, is introduced for the spread of infection through a spatially-distributed population. MATH.arxiv 2020-09-05
84 Adaptive Preferential Sampling In Phylodynamics LORENZO CAPPELLO et. al. We introduce an approach where the sampling process is modeled as an inhomogeneous Poisson process with rate equal to the product of $N_{e}(t)$ and a time-varying coefficient, making minimal assumptions on their functional shapes via Markov random field priors. STATISTICS.arxiv 2020-09-04
85 Policy Evaluation In COVID-19: A Graphical Guide To Common Design Issues NOAH A HABER et. al. In this paper, we (1) introduce the basic suite of policy impact evaluation designs for observational data, including cross-sectional analyses, pre/post, interrupted time-series, and difference-in-differences analysis, (2) demonstrate key ways in which the requirements and assumptions underlying these designs are often violated in the context of COVID-19, and (3) provide decision-makers and reviewers a conceptual and graphical guide to identifying these key violations. STATISTICS.arxiv 2020-09-03
86 Estimating The Reciprocal Of A Binomial Proportion JIAJIN WEI et. al. Inspired by this, we propose to further advance the literature by developing an optimal estimator for the inverse proportion in a family of shrinkage estimators. STATISTICS.arxiv 2020-09-02
87 Covid 19 And A Wavelet Analysis Of The Total Deaths Per Month In Brazil Since 2015 ALEXANDRE BARBOSA DE LIMA We investigate the historical series of the total number of deaths per month in Brazil since 2015 using the wavelet transform, in order to assess whether the COVID-19 pandemic caused any change point in that series. EESS.arxiv 2020-09-01
88 The Battle Of Infinity: Explosive Demand Surge Vs Gigantic Service Providers HIROSHI TOYOIZUMI We provide a new stability criterion based on the Borel-Cantelli lemma to judge whether the infinite server safely accommodates heavy-tailed demands. MATH.arxiv 2020-08-31
89 Real-time Prediction Of COVID-19 Related Mortality Using Electronic Health Records PATRICK SCHWAB et. al. Here, we present the COVID-19 Early Warning System (CovEWS), a clinical risk scoring system for assessing COVID-19 related mortality risk. STATISTICS.arxiv 2020-08-31
90 DRR4Covid: Learning Automated COVID-19 Infection Segmentation From Digitally Reconstructed Radiographs PENGYI ZHANG et. al. We propose a novel approach, called DRR4Covid, to learn automated COVID-19 diagnosis and infection segmentation on CXRs from digitally reconstructed radiographs (DRRs). EESS.arxiv 2020-08-26
91 Share Price Prediction Of Aerospace Relevant Companies With Recurrent Neural Networks Based On PCA LINYU ZHENG et. al. To improve the prediction of share price for aerospace industry sector and well understand the impact of various indicators on stock prices, we provided a hybrid prediction model by the combination of Principal Component Analysis (PCA) and Recurrent Neural Networks. FINANCE.arxiv 2020-08-26
92 What Can We Learn From The Travelers Data In Detecting Disease Outbreaks — A Case Study Of The COVID-19 Epidemic LE BAO et. al. We discussed how the traveler case reports could aid in a timely detection of a disease outbreak. STATISTICS.arxiv 2020-08-26
93 On The Composition And Limitations Of Publicly Available COVID-19 X-Ray Imaging Datasets BEATRIZ GARCIA SANTA CRUZ et. al. This paper presents an overview of the currently public available COVID-19 chest X-ray datasets. EESS.arxiv 2020-08-26
94 An Exploratory Time Series Analysis Of Total Deaths Per Month In Brazil Since 2015 ALEXANDRE BARBOSA DE LIMA In this article, we investigate the historical series of the total number of deaths per month in Brazil since 2015 using time series analysis techniques, in order to assess whether the COVID-19 pandemic caused any change in the series’ generating mechanism. STATISTICS.arxiv 2020-08-26
95 Quantifying The Impact Of COVID-19 On The US Stock Market: An Analysis From Multi-source Information ASIM KUMER DEY et. al. We develop a novel temporal complex network approach to quantify the US county level spread dynamics of COVID-19. FINANCE.arxiv 2020-08-25
96 Feedback Control Of The COVID-19 Pandemic With Guaranteed Non-exceeding ICU Capacity THOMAS BERGER In this paper we investigate feedback control techniques for the COVID-19 pandemic which are able to guarantee that the capacity of available intensive care unit beds is not exceeded. MATH.arxiv 2020-08-21
97 Comparative Performance Analysis Of The ResNet Backbones Of Mask RCNN To Segment The Signs Of COVID-19 In Chest CT Scans MUHAMMAD ALEEM et. al. This paper aims to identify and monitor the effects of COVID-19 on the human lungs by employing Deep Neural Networks on axial CT (Chest Computed Tomography) scan of lungs. EESS.arxiv 2020-08-21
98 Effect Of COVID-19 On Noise Pollution Change In Dublin, Ireland BIDROHA BASU et. al. This study investigates noise pollution in Dublin, Ireland before and after the lockdown imposed as a result of the COVID-19 pandemic. STATISTICS.arxiv 2020-08-20
99 The Smart Mask: Active Closed-Loop Protection Against Airborne Pathogens NAREN VIKRAM RAJ MASNA et. al. In this paper, we present a new active mask paradigm, in which the wearable device is equipped with smart sensors and actuators to both detect the presence of airborne pathogens in real time and take appropriate action to mitigate the threat. EESS.arxiv 2020-08-20
100 Spatial Homogeneity Learning For Spatially Correlated Functional Data With Application To COVID-19 Growth Rate Curves TIANYU PAN et. al. We propose a geographically detailed functional data grouping method equipped with a functional conditional autoregressive (CAR) prior to fully capture the spatial correlation in the pandemic curves. STATISTICS.arxiv 2020-08-20
101 Bear Markets And Recessions Versus Bull Markets And Expansions ABDULNASSER HATEMI-J This paper examines the dynamic interaction between falling and rising markets for both the real and the financial sectors of the largest economy in the world using asymmetric causality tests. FINANCE.arxiv 2020-08-20
102 Image Quality Assessment For Closed-loop Computer-assisted Lung Ultrasound ZACHARY M C BAUM et. al. We describe a novel, two-stage computer assistance system for lung anomaly detection using ultrasound imaging in the intensive care setting to improve operator performance and patient stratification during coronavirus pandemics. EESS.arxiv 2020-08-20
103 Correcting Data Imbalance For Semi-Supervised Covid-19 Detection Using X-ray Chest Images SAUL CALDERON-RAMIREZ et. al. This is often a limitation when the subject of research is relatively new as in the case of the virus outbreak, where dealing with small labelled datasets is a challenge. EESS.arxiv 2020-08-19
104 Estimating The Effective Reproduction Number And Variables Of Disease Models For The COVID-19 Epidemic MAURICIO C. DE OLIVEIRA In order to overcome these shortcomings a constrained estimation method is proposed that keeps the model variables within pre-specified boundaries and can also promote smoothness of the estimates. MATH.arxiv 2020-08-18
105 A Real-time Robot-based Auxiliary System For Risk Evaluation Of COVID-19 Infection WENQI WEI et. al. In this paper, we propose a real-time robot-based auxiliary system for risk evaluation of COVID-19 infection. EESS.arxiv 2020-08-17
106 Do Face Masks Introduce Bias In Speech Technologies? The Case Of Automated Scoring Of Speaking Proficiency ANASTASSIA LOUKINA et. al. In this paper we explore the impact of wearing face masks on the automated assessment of English language proficiency. EESS.arxiv 2020-08-17
107 Short Term Stress Of Covid-19 On World Major Stock Indices MUHAMMAD REHAN et. al. The main objective of this study is to check short term stress of COVID-19 on the American, European, Asian, and Pacific stock market indices, furthermore, the correlation between all the stock markets during the pandemic. FINANCE.arxiv 2020-08-14
108 Bounding Disease Prevalence By Bounding Selectivity And Accuracy Of Tests: The Case Of COVID-19 JÖRG STOYE I propose novel partial identification bounds on disease prevalence from information on test rate and test yield. EESS.arxiv 2020-08-13
109 MIXCAPS: A Capsule Network-based Mixture Of Experts For Lung Nodule Malignancy Prediction PARNIAN AFSHAR et. al. Capitalizing on their success, in this study, we propose a novel capsule network-based mixture of experts, referred to as the MIXCAPS. EESS.arxiv 2020-08-13
110 Risk Measures Estimation Under Wasserstein Barycenter M. ANDREA ARIAS-SERNA et. al. This paper proposes a new approach for modeling multivariate risk measures under Wasserstein barycenters of probability measures supported on location-scatter families. STATISTICS.arxiv 2020-08-13
111 Sensitivity Analysis Of Error-Contaminated Time Series Data Under Autoregressive Models With Application Of COVID-19 Data QIHUANG ZHANG et. al. We propose two measurement error models to describe different processes of data contamination. STATISTICS.arxiv 2020-08-12
112 The Shapes Of An Epidemic: Using Functional Data Analysis To Characterize COVID-19 In Italy TOBIA BOSCHI et. al. The techniques we describe could capture additional and potentially sharper signals if applied to richer data. STATISTICS.arxiv 2020-08-11
113 Time Fused Coefficient SIR Model With Application To COVID-19 Epidemic In The United States HOU-CHENG YANG et. al. In this paper, we propose a Susceptible-Infected-Removal (SIR) model with time fused coefficients. STATISTICS.arxiv 2020-08-10
114 Individualized Prediction Of COVID-19 Adverse Outcomes With MLHO HOSSEIN ESTIRI et. al. We broadly describe the clusters of features that were utilized in modeling and their relative influence on predicting each outcome. STATISTICS.arxiv 2020-08-09
115 Generalized K-Means In GLMs With Applications To The Outbreak Of COVID-19 In The United States TONGLIN ZHANG et. al. By choosing the dissimilarity measure as the well known likelihood ratio or $F$-statistic, this work proposes a method based on generalized $k$-means to group statistical models. STATISTICS.arxiv 2020-08-09
116 On The Interplay Of Regional Mobility, Social Connectedness, And The Spread Of COVID-19 In Germany CORNELIUS FRITZ et. al. In this paper we therefore investigate the impact of human mobility and social connectivity derived from Facebook activities on the weekly rate of new infections in Germany between March 3rd and June 22nd, 2020. STATISTICS.arxiv 2020-08-07
117 Teaching Economics With Interactive Browser-Based Models JUAN DOMINGUEZ-MORAN et. al. Interactive simulation toolkits come in handy when teaching macroeconomic models by facilitating an easy understanding of underlying economic concepts and offering an intuitive approach to the models’ comparative statics. FINANCE.arxiv 2020-08-06
118 Spatiotemporal Dynamic Of COVID-19 Mortality In The City Of Sao Paulo, Brazil: Shifting The High Risk From The Best To The Worst Socio-economic Conditions PATRICIA MARQUES MORALEJO BERMUDI et. al. We describe mortality rates for each epidemiological week and the entire period by sex and age. STATISTICS.arxiv 2020-08-05
119 A Note On Tools For Prediction Under Uncertainty And Identifiability Of SIR-like Dynamical Systems For Epidemiology CHIARA PIAZZOLA et. al. We provide an overview of the methods that can be used for prediction under uncertainty and data fitting of dynamical systems, and of the fundamental challenges that arise in this context. STATISTICS.arxiv 2020-08-04
120 Identification Of Images Of COVID-19 From Chest X-rays Using Deep Learning: Comparing COGNEX VisionPro Deep Learning 1.0 Software With Open Source Convolutional Neural Networks ARJUN SARKAR et. al. In this study, COGNEXs Deep Learning Software-VisionPro Deep Learning is used to classify these Chest X-rays from the COVIDx dataset. EESS.arxiv 2020-08-02
121 Vector Quantisation And Partitioning Of COVID-19 Temporal Dynamics In The United States CHRIS VON CSEFALVAY This paper outlines an analysis of time series vector quantisation and paritioning of COVID-19 cases in the United States, using a soft-DTW (Dynamic Time Warping) k-means clustering and a k-shape based clustering algorithm to identify internally consistent clusters of case counts over time. STATISTICS.arxiv 2020-08-02
122 Contact Classification In COVID-19 Tracing CHRISTOPH GÜNTHER et. al. Our analysis identifies parameters that result in accurate warnings, at least within the scope of validity of the models. EESS.arxiv 2020-08-02
123 What Can We Learn About SARS-CoV-2 Prevalence From Testing And Hospital Data? DANIEL W. SACKS et. al. We provide and test conditions under which this non-COVID hospitalization bound is valid for the general population. EESS.arxiv 2020-08-01
124 Estimating The Real Burden Of Disease Under A Pandemic Situation: The SARS-CoV2 Case AMANDA FERNÁNDEZ-FONTELO et. al. The present paper introduces a new model used to study and analyse the severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) epidemic-reported-data from Spain. STATISTICS.arxiv 2020-08-01
125 Markovian And Non-Markovian Processes With Active Decision Making Strategies For Addressing The COVID-19 Pandemic HAMID EFTEKHARI et. al. We study and predict the evolution of Covid-19 in six US states from the period May 1 through August 31 using a discrete compartment-based model and prescribe active intervention policies, like lockdowns, on the basis of minimizing a loss function, within the broad framework of partially observed Markov decision processes. STATISTICS.arxiv 2020-08-01
126 Deep Direct Likelihood Knockoffs MUKUND SUDARSHAN et. al. Predictive modeling often uses black box machine learning methods, such as deep neural networks, to achieve state-of-the-art performance. STATISTICS.arxiv 2020-07-31
127 Regional Now- And Forecasting For Data Reported With Delay: A Case Study In COVID-19 Infections GIACOMO DE NICOLA et. al. We apply our model to German data, for which our focus lies in explaining and predicting infectious behaviour by district, age group and gender. STATISTICS.arxiv 2020-07-31
128 Causal Inference Methods For Small Non-randomized Studies: Methods And An Application In COVID-19 SARAH FRIEDRICH et. al. Motivated by a non-randomized open-label study investigating the efficacy of hydroxychloroquine in patients with COVID-19, we describe in a unified fashion various alternative approaches to the analysis of non-randomized studies. STATISTICS.arxiv 2020-07-31
129 Nonparametric Comparison Of Epidemic Time Trends: The Case Of COVID-19 MARINA KHISMATULLINA et. al. In this paper, we devise new inference methods that allow to detect differences in the development of the COVID-19 epidemic across countries in a statistically rigorous way. STATISTICS.arxiv 2020-07-31
130 Equilibrium Oil Market Share Under The COVID-19 Pandemic XIAOJUN CHEN et. al. We describe the uncertainties in the demand and supply by random variables and provide two types of production decisions (here-and-now and wait-and-see). MATH.arxiv 2020-07-30
131 Measuring The Effectiveness Of US Monetary Policy During The COVID-19 Recession MARTIN FELDKIRCHER et. al. To measure these effects at high frequencies, we propose a novel mixed frequency vector autoregressive (MF-VAR) model. EESS.arxiv 2020-07-30
132 PDCOVIDNet: A Parallel-Dilated Convolutional Neural Network Architecture For Detecting COVID-19 From Chest X-Ray Images NIHAD KARIM CHOWDHURY et. al. In this paper, we proposed a parallel-dilated convolutional neural network (CNN) based COVID-19 detection system from chest x-ray images, named as Parallel-Dilated COVIDNet (PDCOVIDNet). EESS.arxiv 2020-07-29
133 Change Sign Detection With Differential MDL Change Statistics And Its Applications To COVID-19 Pandemic Analysis KENJI YAMANISHI et. al. We propose a novel methodology to address this issue. STATISTICS.arxiv 2020-07-29
134 Comparative Study Of Deep Learning Methods For The Automatic Segmentation Of Lung, Lesion And Lesion Type In CT Scans Of COVID-19 Patients SOFIE TILBORGHS et. al. In this study, we compare twelve deep learning algorithms using a multi-center dataset, including both open-source and in-house developed algorithms. EESS.arxiv 2020-07-29
135 COVID-19 CT Image Synthesis With A Conditional Generative Adversarial Network YIFAN JIANG et. al. In order to meet the data requirements for COVID-19 CT imaging, we propose a CT image synthesis approach based on a conditional generative adversarial network that can effectively generate high-quality and realistic COVID-19 CT images for use in deep-learning-based medical imaging tasks. EESS.arxiv 2020-07-29
136 Robust Parameter Estimation In Dynamical Systems Via Statistical Learning With An Application To Epidemiological Models DIEGO MARCONDES We propose a robust parameter estimation method for dynamical systems based on Statistical Learning techniques which aims to estimate a set of parameters that well fit the dynamics in order to obtain robust evidences about the qualitative behaviour of its trajectory. STATISTICS.arxiv 2020-07-28
137 CovMUNET: A Multiple Loss Approach Towards Detection Of COVID-19 From Chest X-ray A. Q. M. SAZZAD SAYYED et. al. In this paper, we propose CovMUNET, which is a multiple loss deep neural network approach to detect COVID-19 cases from CXR images. EESS.arxiv 2020-07-28
138 A Bayesian Hierarchical Network For Combining Heterogeneous Data Sources In Medical Diagnoses CLAIRE DONNAT et. al. Motivated by a concrete application in antibody testing, we devise a Stochastic Expectation-Maximization algorithm that allows the principled integration of heterogeneous, and potentially unreliable, data types. STATISTICS.arxiv 2020-07-27
139 How Robust Are The Estimated Effects Of Nonpharmaceutical Interventions Against COVID-19? MRINANK SHARMA et. al. To answer this question, we investigate 2 state-of-the-art NPI effectiveness models and propose 6 variants that make different structural assumptions. STATISTICS.arxiv 2020-07-27
140 The Causality Inference Of Public Interest In Restaurants And Bars On COVID-19 Daily Cases In The US: A Google Trends Analysis MILAD ASGARI MEHRABADI et. al. The objective of this study was to consider the predictive ability of different search terms (i.e., bars and restaurants) with regards to the increase of daily cases in the US. STATISTICS.arxiv 2020-07-26
141 Study Of Different Deep Learning Approach With Explainable AI For Screening Patients With COVID-19 Symptoms: Using CT Scan And Chest X-ray Image Dataset MD MANJURUL AHSAN et. al. Some studies proposed CT scan or chest X-ray images as an alternative solution. EESS.arxiv 2020-07-24
142 Causal Analysis Of Covid-19 Spread In Germany ATALANTI A. MASTAKOURI et. al. In this work, we study the causal relations among German regions in terms of the spread of Covid-19 since the beginning of the pandemic, taking into account the restriction policies that were applied by the different federal states. STATISTICS.arxiv 2020-07-23
143 COVID TV-UNet: Segmenting COVID-19 Chest CT Images Using Connectivity Imposed U-Net NARGES SAEEDIZADEH et. al. In this work we propose a segmentation framework to detect chest regions in CT images, which are infected by COVID-19. EESS.arxiv 2020-07-23
144 Darwin’s Neural Network: AI-based Strategies For Rapid And Scalable Cell And Coronavirus Screening SANG WON LEE et. al. Here we adapt the theory of survival of the fittest in the field of computer vision and machine perception to introduce a new framework of multi-class instance segmentation deep learning, Darwin’s Neural Network (DNN), to carry out morphometric analysis and classification of COVID19 and MERS-CoV collected in vivo and of multiple mammalian cell types in vitro. EESS.arxiv 2020-07-22
145 Efficiency Of The Financial Markets During The COVID-19 Crisis: Time-varying Parameters Of Fractional Stable Dynamics AYOUB AMMY-DRISS et. al. This method introduces a free parameter, the discount factor, which we select so as to get the best alpha-stable density forecasts for observed price returns. FINANCE.arxiv 2020-07-21
146 Clustering Patterns Connecting COVID-19 Dynamics And Human Mobility Using Optimal Transport FRANK NIELSEN et. al. We described a new computational framework to measure and compare the temporal relationships between human mobility and new cases of COVID-19 across more than 150 cities of the United States with relatively high incidence of the disease. STATISTICS.arxiv 2020-07-21
147 A Comparison Of Aggregation Methods For Probabilistic Forecasts Of COVID-19 Mortality In The United States KATHRYN S. TAYLOR et. al. In this paper, we consider the weekly forecasting of the cumulative mortality due to COVID-19 at the national and state level in the U.S. Optimal decision-making requires a forecast of a probability distribution, rather than just a single point forecast. STATISTICS.arxiv 2020-07-21
148 Integrative Analysis For COVID-19 Patient Outcome Prediction HANQING CHAO et. al. In lieu of only size and volume information of pulmonary abnormalities and features through deep learning based image segmentation, here we combine radiomics of lung opacities and non-imaging features from demographic data, vital signs, and laboratory findings to predict need for intensive care unit (ICU) admission. EESS.arxiv 2020-07-20
149 Inter-Mobile-Device Distance Estimation Using Network Localization Algorithms For Digital Contact Logging Applications LILLIAN CLARK et. al. We survey several approaches and evaluate the performance of each on real and simulated Bluetooth Low Energy (BLE) measurement datasets with respect to both distance estimate accuracy and the proximity detection problem. EESS.arxiv 2020-07-20
150 Assessment Of COVID-19 Hospitalization Forecasts From A Simplified SIR Model P. -A. ABSIL et. al. We propose the SH model, a simplified version of the well-known SIR compartmental model of infectious diseases. STATISTICS.arxiv 2020-07-20
151 COVID-19 Data Analysis And Forecasting: Algeria And The World SAMI BELKACEM The evaluation results show that the time series Prophet model accurately predicts the number of cases and deaths in Algeria with low RMSE scores of 218.87 and 4.79 respectively, while the forecast suggests that the total number of cases and deaths are expected to increase in the coming weeks. STATISTICS.arxiv 2020-07-19
152 Using Deep Convolutional Neural Networks To Diagnose COVID-19 From Chest X-Ray Images YI ZHONG This project utilizes several open-source or public datasets to present an open-source dataset of COVID-19 CXRs, named COVID-19-CXR-Dataset, and introduces a deep convolutional neural network model. EESS.arxiv 2020-07-19
153 Estimating COVID-19 Cases And Reproduction Number In Mexico MICHELLE ANZARUT et. al. In this report we fit a semi-mechanistic Bayesian hierarchical model to describe the Mexican COVID-19 epidemic. STATISTICS.arxiv 2020-07-17
154 Estimation Of Time-varying Kernel Densities And Chronology Of The Impact Of COVID-19 On Financial Markets MATTHIEU GARCIN et. al. We propose to select these parameters so as to minimize a criterion consistent with the traditional requirements of the validation of a probability density forecast. FINANCE.arxiv 2020-07-17
155 An Efficient Mixture Of Deep And Machine Learning Models For COVID-19 And Tuberculosis Detection Using X-Ray Images In Resource Limited Settings ALI H. AL-TIMEMY et. al. In order to help in the detection of COVID-19, we propose the extraction of deep features (DF) from chest X-ray images, a technology available in most hospitals, and their subsequent classification using machine learning methods that do not require large computational resources. EESS.arxiv 2020-07-16
156 COV-ELM Classifier: An Extreme Learning Machine Based Identification Of COVID-19 Using Chest X-Ray Images SHEETAL RAJPAL et. al. In this work, we propose a multiclass classification model COV-ELM, based on the extreme learning machine which classifies the CXR images into one of the three classes, namely COVID-19, normal, and pneumonia. EESS.arxiv 2020-07-16
157 Heterogeneity Learning For SIRS Model: An Application To The COVID-19 GUANYU HU et. al. We propose a Bayesian Heterogeneity Learning approach for Susceptible-Infected-Removal-Susceptible (SIRS) model that allows underlying clustering patterns for transmission rate, recovery rate, and loss of immunity rate for the latest coronavirus (COVID-19) among different regions. STATISTICS.arxiv 2020-07-15
158 Predication Of Inflection Point And Outbreak Size Of COVID-19 In New Epicentres QIBIN DUAN et. al. This study aims to predict the inflection point and outbreak size of these new/potential epicentres at the early phase of the epidemics by borrowing information from more `mature’ curves from other countries. STATISTICS.arxiv 2020-07-15
159 How Does Stock Market Reflect The Change In Economic Demand? A Study On The Industry-specific Volatility Spillover Networks Of China’s Stock Market During The Outbreak Of COVID-19 FU QIAO et. al. Using the carefully selected industry classification standard, we divide 102 industry securities indices in China’s stock market into four demand-oriented sector groups and identify demand-oriented industry-specific volatility spillover networks. FINANCE.arxiv 2020-07-15
160 Use Of Machine Learning And Artificial Intelligence To Predict SARS-CoV-2 Infection From Full Blood Counts In A Population ABHIRUP BANERJEEA et. al. The aim of the study was to use machine learning (ML), an artificial neural network (ANN) and a simple statistical test to identify SARS-CoV-2 positive patients from full blood counts without knowledge of symptoms or history of the individuals. STATISTICS.arxiv 2020-07-14
161 A Simple Correction For Covid-19 Testing Bias DANIEL ANDRÉS DÍAZ-PACHÓN et. al. Here we present a simple bias correction methodology derived and adapted from a correction for publication bias in meta analysis studies. STATISTICS.arxiv 2020-07-14
162 Convergence Of Two-stage Iterative Scheme For $K$-weak Regular Splittings Of Type II With Application To Covid-19 Pandemic Model VAIBHAV SHEKHAR et. al. To this end, we propose convergence theory of two-stage iterative scheme for $K$-weak regular splittings of both types in the proper cone setting. MATH.arxiv 2020-07-12
163 EMIXER: End-to-end Multimodal X-ray Generation Via Self-supervision SIDDHARTH BISWAL et. al. To tackle this joint synthesis challenge, we propose an End-to-end MultImodal X-ray genERative model (EMIXER) for jointly synthesizing x-ray images and corresponding free-text reports, all conditional on diagnosis labels. EESS.arxiv 2020-07-10
164 Inferring Proximity From Bluetooth Low Energy RSSI With Unscented Kalman Smoothers TOM LOVETT et. al. In this paper, we present an approach that infers posterior probabilities over distance given sequences of RSSI values. EESS.arxiv 2020-07-09
165 Time Series Analysis Of COVID-19 Infection Curve: A Change-Point Perspective FEIYU JIANG et. al. In this paper, we model the trajectory of the cumulative confirmed cases and deaths of COVID-19 (in log scale) via a piecewise linear trend model. EESS.arxiv 2020-07-09
166 A Vision-based Social Distancing And Critical Density Detection System For COVID-19 DONGFANG YANG et. al. Against this backdrop, we propose using a monocular camera and deep learning-based real-time object detectors to measure social distancing. EESS.arxiv 2020-07-07
167 Segmentation Of Pulmonary Opacification In Chest CT Scans Of COVID-19 Patients KEEGAN LENSINK et. al. In this work we provide open source models for the segmentation of patterns of pulmonary opacification on chest Computed Tomography (CT) scans which have been correlated with various stages and severities of infection. EESS.arxiv 2020-07-07
168 A Review Of Spatial Causal Inference Methods For Environmental And Epidemiological Applications BRIAN J REICH et. al. In this paper, we review the current literature on spatial causal inference and identify areas of future work. STATISTICS.arxiv 2020-07-06
169 Prediction Regions For Poisson And Over-Dispersed Poisson Regression Models With Applications To Forecasting Number Of Deaths During The COVID-19 Pandemic T. KIM et. al. An over-dispersed Poisson regression model is therefore proposed. STATISTICS.arxiv 2020-07-04
170 A Weakly Supervised Consistency-based Learning Method For COVID-19 Segmentation In CT Images ISSAM LARADJI et. al. Thus, we propose a consistency-based (CB) loss function that encourages the output predictions to be consistent with spatial transformations of the input images. EESS.arxiv 2020-07-04
171 Unified Statistical Inference For A Novel Nonlinear Dynamic Functional/longitudinal Data Model LIXIA HU et. al. In light of recent work studying massive functional/longitudinal data, such as the resulting data from the COVID-19 pandemic, we propose a novel functional/longitudinal data model which is a combination of the popular varying coefficient (VC) model and additive model. STATISTICS.arxiv 2020-07-03
172 An Encoder-decoder-based Method For COVID-19 Lung Infection Segmentation OMAR ELHARROUSS et. al. This paper proposes a multi-task deep-learning-based method for lung infection segmentation using CT-scan images. EESS.arxiv 2020-07-02
173 On The Minimization Of Sobolev Norms Of Time-Varying Graph Signals: Estimation Of New Coronavirus Disease 2019 Cases JHONY H. GIRALDO et. al. In this paper, we model the number of new cases of the Coronavirus Disease 2019 (COVID-19) as a problem of reconstruction of time-varying graph signals. EESS.arxiv 2020-07-01
174 An Early Warning Approach To Monitor COVID-19 Activity With Multiple Digital Traces In Near Real-Time NICOLE E. KOGAN et. al. These efforts represent an initial exploratory framework, and both continued study of the predictive power of digital indicators as well as further development of the statistical approach are needed. STATISTICS.arxiv 2020-07-01
175 Performance Analysis Of Zero Black-Derman-Toy Interest Rate Model In Catastrophic Events: COVID-19 Case Study GRZEGORZ KRZYŻANOWSKI et. al. In this paper we continue the research of our recent interest rate tree model called Zero Black-Derman-Toy (ZBDT) model, which includes the possibility of a jump at each step to a practically zero interest rate. FINANCE.arxiv 2020-07-01
176 A Robust Nonlinear Mixed-effects Model For COVID-19 Deaths Data FERNANDA L. SCHUMACHER et. al. An efficient EM-type algorithm is proposed to carry out maximum likelihood estimation of model parameters. STATISTICS.arxiv 2020-07-01
177 Do Not Forget Interaction: Predicting Fatality Of COVID-19 Patients Using Logistic Regression FENG ZHOU et. al. In this research, we reported an explainable, intuitive, and accurate machine learning model based on logistic regression to predict the fatality rate of COVID-19 patients using only three important blood biomarkers, including lactic dehydrogenase, lymphocyte (%) and high-sensitivity C-reactive protein, and their interactions. STATISTICS.arxiv 2020-06-30
178 Autoregressive Mixture Models For Serial Correlation Clustering Of Time Series Data BENNY REN et. al. We propose a novel method that simultaneously clusters and fits autoregression models for groups of similar individuals. STATISTICS.arxiv 2020-06-30
179 From Predictions To Prescriptions: A Data-driven Response To COVID-19 DIMITRIS BERTSIMAS et. al. Specifically, we propose a comprehensive data-driven approach to understand the clinical characteristics of COVID-19, predict its mortality, forecast its evolution, and ultimately alleviate its impact. STATISTICS.arxiv 2020-06-29
180 Using Mobility Data In The Design Of Optimal Lockdown Strategies For The COVID-19 Pandemic RITABRATA DUTTA et. al. A mathematical model for the COVID-19 pandemic spread, which integrates age-structured Susceptible-Exposed-Infected-Recovered-Deceased dynamics with real mobile phone data accounting for the population mobility, is presented. STATISTICS.arxiv 2020-06-29
181 Modeling And Computation Of High Efficiency And Efficacy Multi-Step Batch Testing For Infectious Diseases HONGSHIK AHN et. al. We propose a mathematical model based on probability theory to optimize COVID-19 testing by a multi-step batch testing approach with variable batch sizes. STATISTICS.arxiv 2020-06-29
182 Estimation Of Covid-19 Prevalence From Serology Tests: A Partial Identification Approach PANOS TOULIS We propose a partial identification method for estimating disease prevalence from serology studies. STATISTICS.arxiv 2020-06-29
183 Individual-level Modeling Of COVID-19 Epidemic Risk ANDRES COLUBRI et. al. Here we propose a novel probabilistic formalism based on Individual-Level Models (ILMs) that offers rigorous formulas for the probability of infection of individuals, which can be parameterised via Maximum Likelihood Estimation (MLE) applied on compartmental models defined at the population level. STATISTICS.arxiv 2020-06-28
184 Using Bluetooth Low Energy (BLE) Signal Strength Estimation To Facilitate Contact Tracing For COVID-19 GARY F. HATKE et. al. This paper describes a method and analysis of determining whether two cell phones, carried by humans, were in persistent contact of no more than 6 feet over 15 minutes using Bluetooth Low Energy signals. EESS.arxiv 2020-06-28
185 End-to-End AI-Based Point-of-Care Diagnosis System For Classifying Respiratory Illnesses And Early Detection Of COVID-19 ABDELKADER NASREDDINE BELKACEM et. al. This paper proposes an end-to-end portable system that can record data from patients with symptom, including coughs (voluntary or involuntary) and translate them into health data for diagnosis, and with the aid of machine learning, classify them into different respiratory illnesses, including COVID-19. EESS.arxiv 2020-06-27
186 COVID-19 Screening Using Residual Attention Network An Artificial Intelligence Approach VISHAL SHARMA et. al. In this paper, we present a technique to screen for COVID-19 using artificial intelligence. We collected a dataset of chest X-ray images and trained several popular deep convolution neural network-based models (VGG, MobileNet, Xception, DenseNet, InceptionResNet) to classify the chest X-rays. EESS.arxiv 2020-06-26
187 4S-DT: Self Supervised Super Sample Decomposition For Transfer Learning With Application To COVID-19 Detection ASMAA ABBAS et. al. In this paper, we propose a novel deep convolutional neural network, we called Self Supervised Super Sample Decomposition for Transfer learning (4S-DT) model. EESS.arxiv 2020-06-26
188 Real-Time Real Economic Activity: Exiting The Great Recession And Entering The Pandemic Recession FRANCIS X. DIEBOLD We study the real-time signals provided by the Aruoba-Diebold-Scotti Index of Business conditions (ADS) for tracking economic activity at high frequency. EESS.arxiv 2020-06-26
189 A Novel And Reliable Deep Learning Web-Based Tool To Detect COVID-19 Infection From Chest CT-Scan ABDOLKARIM SAEEDI et. al. In this work, we introduce a computer aided diagnosis (CAD) web service to detect COVID- 19 online. EESS.arxiv 2020-06-24
190 Inference In Stochastic Epidemic Models Via Multinomial Approximations NICK WHITELEY et. al. We introduce a new method for inference in stochastic epidemic models which uses recursive multinomial approximations to integrate over unobserved variables and thus circumvent likelihood intractability. STATISTICS.arxiv 2020-06-24
191 Data-driven Analytical Models Of COVID-2019 For Epidemic Prediction, Clinical Diagnosis, Policy Effectiveness And Contact Tracing: A Survey YING MAO et. al. In this paper, we review the latest models for analyzing COVID19 related data, conduct post-publication model evaluations and cross-model comparisons, and collect data sources from different projects. STATISTICS.arxiv 2020-06-24
192 Estimation Of COVID-19 Under-reporting In Brazilian States Through SARI BALTHAZAR PAIXÃO et. al. Therefore, the objective of this work is to estimate the under-reporting of cases and deaths of COVID-19 in Brazilian states using data from the Infogripe on notification of Severe Acute Respiratory Infection (SARI). STATISTICS.arxiv 2020-06-23
193 Was There COVID-19 Back In 2012? Challenge For AI In Diagnosis With Similar Indications IMON BANERJEE et. al. Methods: In this paper, we present our argument regarding the efficiency and applicability of existing deep learning models for COVID-19 diagnosis. EESS.arxiv 2020-06-23
194 Examining The Effect Of COVID-19 On Foreign Exchange Rate And Stock Market — An Applied Insight Into The Variable Effects Of Lockdown On Indian Economy INDRAJIT BANERJEE et. al. The study explores whether the causal relationships and directions among the growth rate of confirmed cases (GROWTHC), exchange rate (GEX) and SENSEX value (GSENSEX) are remaining the same across different pre and post-lockdown phases, attempting to capture any potential changes over time via the vector autoregressive (VAR) models. FINANCE.arxiv 2020-06-23
195 The Role Of Swabs In Modeling The COVID-19 Outbreak In The Most Affected Regions Of Italy CLAUDIA FURLAN et. al. In this work, we propose a nonlinear asymmetric diffusion model, which includes information on the daily number of swabs, to describe daily fluctuations in the number of confirmed cases in addition to the main trend of the outbreak evolution. STATISTICS.arxiv 2020-06-23
196 Effectiveness And Compliance To Social Distancing During COVID-19 KRISTI BUSHMAN et. al. In this work, we use a detailed set of mobility data to evaluate the impact that these interventions had on alleviating the spread of the virus in the US as measured through the COVID-19-related deaths. STATISTICS.arxiv 2020-06-22
197 On The Time Trend Of COVID-19: A Panel Data Study CHAOHUA DONG et. al. In this paper, we study the trending behaviour of COVID-19 data at country level, and draw attention to some existing econometric tools which are potentially helpful to understand the trend better in future studies. EESS.arxiv 2020-06-19
198 Proper Scoring Rules For Evaluating Asymmetry In Density Forecasting MATTEO IACOPINI et. al. This paper proposes a novel asymmetric continuous probabilistic score (ACPS) for evaluating and comparing density forecasts. STATISTICS.arxiv 2020-06-19
199 COVID-19 Response Needs To Broaden Financial Inclusion To Curb The Rise In Poverty MOSTAK AHAMED et. al. In this paper, we explore to what extent financial inclusion could help mitigate the increase in poverty using cross-country data across 78 low- and lower-middle-income countries. EESS.arxiv 2020-06-18
200 Sparse HP Filter: Finding Kinks In The COVID-19 Contact Rate SOKBAE LEE et. al. In this paper, we estimate the time-varying COVID-19 contact rate of a Susceptible-Infected-Recovered (SIR) model. EESS.arxiv 2020-06-18
201 Small Area Estimation Of Health Outcomes JON WAKEFIELD et. al. We describe design-based and model-based approaches and models that are specified at the area-level and at the unit-level, focusing on health applications and fully Bayesian spatial models. STATISTICS.arxiv 2020-06-18
202 Conflict In Africa During COVID-19: Social Distancing, Food Vulnerability And Welfare Response ROXANA GUTIÉRREZ-ROMERO We study the effect of social distancing, food vulnerability, welfare and labour COVID-19 policy responses on riots, violence against civilians and food-related conflicts. EESS.arxiv 2020-06-18
203 Uncertainty Quantification For Epidemiological Forecasts Of COVID-19 Through Combinations Of Model Predictions V. E. BOWMAN et. al. In this paper we look at combining epidemiological forecasts for COVID-19 daily deaths, hospital admissions, and hospital and ICU occupancy, in order to improve the predictive accuracy of the short term forecasts. STATISTICS.arxiv 2020-06-18
204 Are You Wearing A Mask? Improving Mask Detection From Speech Using Augmentation By Cycle-consistent GANs NICOLAE-CĂTĂLIN RISTEA et. al. In this paper, we propose a novel data augmentation approach for mask detection from speech. EESS.arxiv 2020-06-17
205 Exact And Computationally Efficient Bayesian Inference For Generalized Markov Modulated Poisson Processes FLAVIO B. GONÇALVES et. al. We present a new class of unidimensional Cox process models in which the intensity function assumes parametric functional forms that switch among them according to a continuous-time Markov chain. STATISTICS.arxiv 2020-06-17
206 COVID-CXNet: Detecting COVID-19 In Frontal Chest X-ray Images Using Deep Learning ARMAN HAGHANIFAR et. al. In this study, research is conducted on efficiently detecting imaging features of this type of pneumonia using deep convolutional neural networks in a large dataset. EESS.arxiv 2020-06-16
207 Accounting For Uncertainty During A Pandemic JON ZELNER et. al. We discuss several issues of statistical design, data collection, analysis, communication, and decision making that have arisen in recent and ongoing coronavirus studies, focusing on tools for assessment and propagation of uncertainty. STATISTICS.arxiv 2020-06-15
208 Using Mobility For Electrical Load Forecasting During The COVID-19 Pandemic YIZE CHEN et. al. In this work, we focus on the problem of load forecasting. EESS.arxiv 2020-06-15
209 Characterization Of SARS-CoV-2 Dynamics In The Host PABLO ABUIN et. al. In this work, using a control theoretical approach, validated mathematical models of SARS-CoV-2 in humans are properly characterized. MATH.arxiv 2020-06-15
210 Support Estimation With Sampling Artifacts And Errors ELI CHIEN et. al. Here we introduce the first known approach to support estimation in the presence of sampling artifacts and errors where each sample is assumed to arise from a Poisson repeat channel which simultaneously captures repetitions and deletions of samples. STATISTICS.arxiv 2020-06-14
211 Synthetic Interventions ANISH AGARWAL et. al. As our main contribution, we introduce synthetic interventions (SI), which helps resolve this open problem by allowing one to produce counterfactual estimates if there are multiple interventions of interest. EESS.arxiv 2020-06-13
212 COVID-19-CT-CXR: A Freely Accessible And Weakly Labeled Chest X-ray And CT Image Collection On COVID-19 From Biomedical Literature YIFAN PENG et. al. We extracted figures, associated captions, and relevant figure descriptions in the article and separated compound figures into subfigures. EESS.arxiv 2020-06-11
213 The Limits To Learning An SIR Process: Granular Forecasting For Covid-19 JACKIE BAEK et. al. So motivated, we provide an analysis of the limits to estimating an SIR process. STATISTICS.arxiv 2020-06-11
214 Optimal Immunity Control By Social Distancing For The SIR Epidemic Model PIERRE-ALEXANDRE BLIMAN et. al. In this article, using a classical SIR model, we determine the (partial or total) containment strategy on a given finite time interval that maximizes the number of susceptible individuals over an infinite horizon, or equivalently that minimizes the total infection burden during the curse of the epidemic. MATH.arxiv 2020-06-10
215 Trading Privacy For The Greater Social Good: How Did America React During COVID-19? ANINDYA GHOSE et. al. In our research, we analyzed massive yet atomic individual-level location data containing over 22 billion records from ten Blue (Democratic) and ten Red (Republican) cities in the U.S., based on which we present, herein, some of the first evidence of how Americans responded to the increasing concerns that government authorities, the private sector, and public health experts might use individual-level location data to track the COVID-19 spread. EESS.arxiv 2020-06-10
216 A Review Of Automated Diagnosis Of COVID-19 Based On Scanning Images DELONG CHEN et. al. In this paper, we present a review of these recently emerging automatic diagnosing models. EESS.arxiv 2020-06-09
217 Quant Bust 2020 ZURA KAKUSHADZE We explain in a nontechnical fashion why dollar-neutral quant trading strategies, such as equities Statistical Arbitrage, suffered substantial losses (drawdowns) during the COVID-19 market selloff. FINANCE.arxiv 2020-06-09
218 Semiparametric Bayesian Inference For The Transmission Dynamics Of COVID-19 With A State-Space Model TIANJIAN ZHOU et. al. An R package BaySIR is made available at https://github.com/tianjianzhou/BaySIR for the public to conduct independent analysis or reproduce the results in this paper. STATISTICS.arxiv 2020-06-09
219 End-to-end Learning For Semiquantitative Rating Of COVID-19 Severity On Chest X-rays ALBERTO SIGNORONI et. al. In this work we design an end-to-end deep learning architecture for predicting, on Chest X-rays images (CXR), a multi-regional score conveying the degree of lung compromise in COVID-19 patients. EESS.arxiv 2020-06-08
220 Determination And Estimation Of Optimal Quarantine Duration For Infectious Diseases With Application To Data Analysis Of COVID-19 RUOYU WANG et. al. In this paper, a policy with optimal quarantine duration is developed. STATISTICS.arxiv 2020-06-08
221 Cross-Domain Segmentation With Adversarial Loss And Covariate Shift For Biomedical Imaging BORA BAYDAR et. al. For this purpose, this manuscript aims to implement a novel model that can learn robust representations from cross-domain data by encapsulating distinct and shared patterns from different modalities. EESS.arxiv 2020-06-08
222 Machine Learning Automatically Detects COVID-19 Using Chest CTs In A Large Multicenter Cohort EDUARDO JOSE MORTANI BARBOSA JR. et. al. Methods: Our retrospective multi-institutional study obtained 2096 chest CTs from 16 institutions (including 1077 COVID-19 patients). EESS.arxiv 2020-06-08
223 Deep Learning To Estimate The Physical Proportion Of Infected Region Of Lung For COVID-19 Pneumonia With CT Image Set WEI WU et. al. Two tasks were studied in this present paper. EESS.arxiv 2020-06-08
224 In-Ear Measurement Of Blood Oxygen Saturation: An Ambulatory Tool Needed To Detect The Delayed Life-Threatening Hypoxaemia In COVID-19 HARRY J. DAVIES et. al. In this study, we set out to establish the feasibility of SpO2 measurement from the ear canal as a convenient site for long term monitoring, and perform a comprehensive comparison with the right index finger – the conventional clinical measurement site. EESS.arxiv 2020-06-07
225 Advance Warning Methodologies For COVID-19 Using Chest X-Ray Images METE AHISHALI et. al. In this study, our first aim is to evaluate the ability of recent state-of-the-art Machine Learning techniques for the early detection of COVID-19 from chest X-ray images. Finally, this study introduces a new benchmark dataset called Early-QaTa-COV19, which consists of 175 early-stage COVID-19 Pneumonia samples (very limited or no infection signs) labelled by the medical doctors and 1579 samples for control (normal) class. EESS.arxiv 2020-06-07
226 The Zoltar Forecast Archive: A Tool To Facilitate Standardization And Storage Of Interdisciplinary Prediction Research NICHOLAS G REICH et. al. We introduce a new data model for probabilistic predictions that encompasses a wide range of forecasting settings. STATISTICS.arxiv 2020-06-06
227 Multi-Task Temporal Shift Attention Networks For On-Device Contactless Vitals Measurement XIN LIU et. al. We present a video-based and on-device optical cardiopulmonary vital sign measurement approach. EESS.arxiv 2020-06-06
228 The Identification Of Spatially Constrained Homogeneous Clusters Of Covid-19 Transmission ROBERTO BENEDETTI et. al. The paper introduces an approach to identify a set of spatially constrained homogeneous areas maximally homogeneous in terms of epidemic trends. STATISTICS.arxiv 2020-06-05
229 Low-Complexity System And Algorithm For An Emergency Ventilator Sensor And Alarm RYAN M. COREY et. al. This work reports a low-cost, easy-to-produce electronic sensor and alarm system for pressure-cycled ventilators that estimates clinically useful metrics such as pressure and respiratory rate and sounds an alarm when the ventilator malfunctions. EESS.arxiv 2020-06-05
230 Human Mobility And COVID-19 Initial Dynamics STEFANO MARIA IACUS et. al. The same approach adopted in this study can be easily extended to other European countries, as soon as reliable data on the spreading of the virus at a suitable level of granularity will be available. STATISTICS.arxiv 2020-06-05
231 Evaluating The Effectiveness Of Regional Lockdown Policies In The Containment Of Covid-19: Evidence From Pakistan HAMZA UMER et. al. Here we examine the impact of regional lockdown strategies on Covid-19 outcomes. EESS.arxiv 2020-06-04
232 Identifiability And Estimation Under The Test-negative Design With Population Controls With The Goal Of Identifying Risk And Preventive Factors For SARS-CoV-2 Infection MIREILLE E. SCHNITZER et. al. In this study, we define a regression parameter related to a prospective risk factor analysis and consider its identifiability under a design where we recruit among all individuals, symptomatic and asymptomatic, being tested for the virus in addition to population controls. STATISTICS.arxiv 2020-06-04
233 COVID-19 Real-Time Tracker And Analytical Report JIAWEI LONG This paper aims to discuss the major methodologies which are utilized to generate the insights displayed on the real-time tracker, which include real-time data retrieval, normalization techniques, ARIMA time-series forecasting, and logistic regression models. STATISTICS.arxiv 2020-06-04
234 Model Selection Criteria For Regression Models With Splines And The Automatic Localization Of Knots ALEX RODRIGO DOS S. SOUSA et. al. In this paper we propose a model selection approach to fit a regression model using splines with a variable number of knots. STATISTICS.arxiv 2020-06-04
235 Exploration Of Interpretability Techniques For Deep COVID-19 Classification Using Chest X-ray Images SOUMICK CHATTERJEE et. al. Thereby, the use of five different deep learning models (ResNet18, ResNet34, InceptionV3, InceptionResNetV2, and DenseNet161) and their Ensemble have been used in this paper, to classify COVID-19, pneumoni{\ae} and healthy subjects using Chest X-Ray. EESS.arxiv 2020-06-03
236 Predicting Mortality Risk In Viral And Unspecified Pneumonia To Assist Clinicians With COVID-19 ECMO Planning HELEN ZHOU et. al. In this work, we leverage machine learning techniques to develop the PEER score, used to highlight critically ill patients with viral or unspecified pneumonia at high risk of mortality or decompensation in a subpopulation eligible for ECMO. STATISTICS.arxiv 2020-06-02
237 CT-based COVID-19 Triage: Deep Multitask Learning Improves Joint Identification And Severity Quantification MIKHAIL GONCHAROV et. al. To consolidate both triage approaches, we employ a multitask learning and propose a convolutional neural network to combine all available labels within a single model. EESS.arxiv 2020-06-02
238 COVIDGR Dataset And COVID-SDNet Methodology For Predicting COVID-19 Based On Chest X-Ray Images S. TABIK et. al. COVIDGR-1.0 contains 377 positive and 377 negative PA (PosteroAnterior) CXR views and (iii) we propose COVID Smart Data based Network (COVID-SDNet) methodology for improving the generalization capacity of COVID-classification models. EESS.arxiv 2020-06-02
239 Controlling Propagation Of Epidemics Via Mean-field Games WONJUN LEE et. al. In this paper, we introduce a mean-field game model in controlling the propagation of epidemics on a spatial domain. MATH.arxiv 2020-06-01
240 Automatic Classification Between COVID-19 Pneumonia, Non-COVID-19 Pneumonia, And The Healthy On Chest X-ray Image: Combination Of Data Augmentation Methods MIZUHO NISHIO et. al. Automatic Classification Between COVID-19 Pneumonia, Non-COVID-19 Pneumonia, And The Healthy On Chest X-ray Image: Combination Of Data Augmentation Methods EESS.arxiv 2020-06-01
241 Comparing And Integrating US COVID-19 Data From Multiple Sources With Anomaly Detection And Repairing GUANNAN WANG et. al. To address these detected issues, we propose the corresponding repairing methods and procedures if corrections are necessary. STATISTICS.arxiv 2020-06-01
242 BIMCV COVID-19+: A Large Annotated Dataset Of RX And CT Images From COVID-19 Patients MARIA DE LA IGLESIA VAYÁ et. al. This paper describes BIMCV COVID-19+, a large dataset from the Valencian Region Medical ImageBank (BIMCV) containing chest X-ray images CXR (CR, DX) and computed tomography (CT) imaging of COVID-19+ patients along with their radiological findings and locations, pathologies, radiological reports (in Spanish), DICOM metadata, Polymerase chain reaction (PCR), Immunoglobulin G (IgG) and Immunoglobulin M (IgM) diagnostic antibody tests. EESS.arxiv 2020-06-01
243 Interpretable Modeling For Short- And Medium-term Electricity Load Forecasting KEI HIROSE In this study, we build a statistical model that resolves this interpretation issue. STATISTICS.arxiv 2020-06-01
244 Hamiltonian Structure Of Compartmental Epidemiological Models ANGEL BALLESTEROS et. al. New interacting compartmental models among different populations, which are endowed with a Hamiltonian structure, are introduced. MATH.arxiv 2020-05-31
245 Lockdown Strategies, Mobility Patterns And COVID-19 NIKOS ASKITAS et. al. We develop a multiple-events model and exploit within and between country variation in the timing, type and level of intensity of various public policies to study their dynamic effects on the daily incidence of COVID-19 and on population mobility patterns across 135 countries. EESS.arxiv 2020-05-31
246 Statistical Decision Properties Of Imprecise Trials Assessing COVID-19 Drugs CHARLES F. MANSKI et. al. To evaluate decision criteria, we use the concept of near-optimality, which jointly considers the probability and magnitude of decision errors. EESS.arxiv 2020-05-30
247 Global Short-Term Forecasting Of Covid-19 Cases THIAGO DE PAULA OLIVEIRA et. al. We introduce a new modelling framework to describe the course of the pandemic with great accuracy, and provide short-term daily forecasts for every country in the world. STATISTICS.arxiv 2020-05-29
248 Causal Impact Of Masks, Policies, Behavior On Early Covid-19 Pandemic In The U.S VICTOR CHERNOZHUKOV et. al. This paper evaluates the dynamic impact of various policies adopted by US states on the growth rates of confirmed Covid-19 cases and deaths as well as social distancing behavior measured by Google Mobility Reports, where we take into consideration people’s voluntarily behavioral response to new information of transmission risks. EESS.arxiv 2020-05-28
249 Clinical Trials Impacted By The COVID-19 Pandemic: Adaptive Designs To The Rescue? CORNELIA URSULA KUNZ et. al. Guidance is provided on (i) where blinded adaptations can help; (ii) how to achieve type I error rate control, if required; (iii) how to deal with potential treatment effect heterogeneity; (iv) how to utilize early readouts; and (v) how to utilize Bayesian techniques. STATISTICS.arxiv 2020-05-28
250 Understanding And Monitoring The Evolution Of The Covid-19 Epidemic From Medical Emergency Calls: The Example Of The Paris Area STÉPHANE GAUBERT et. al. We rely on a transport PDE epidemiological model, and we use methods from Perron-Frobenius theory and tropical geometry. STATISTICS.arxiv 2020-05-28
251 ARIMA Forecasting Of COVID-19 Incidence In Italy, Russia, And The USA GAETANO PERONE This paper aims to forecast the short to medium-term incidence of COVID-19 epidemic through the medium of an autoregressive integrated moving average (ARIMA) model, applied to Italy, Russia, and the USA The analysis is carried out on the number of new daily confirmed COVID-19 cases, collected by Worldometer website. STATISTICS.arxiv 2020-05-28
252 Early Screening Of SARS-CoV-2 By Intelligent Analysis Of X-Ray Images D. GIL et. al. In particular, in this paper we present a general overview of the project, the experimental design and first results of X-ray COVID-19 detection using a classic approach based on HoG and feature selection. EESS.arxiv 2020-05-28
253 SEIRD Model For Qatar Covid-19 Outbreak: A Case Study RYAD GHANAM et. al. The Covid-19 outbreak of 2020 has required many governments to develop mathematical-statistical models of the outbreak for policy and planning purposes. STATISTICS.arxiv 2020-05-26
254 Evaluating Epidemic Forecasts In An Interval Format JOHANNES BRACHER et. al. Specifically, we discuss the computation and interpretation of the weighted interval score, which is a proper score that approximates the continuous ranked probability score. STATISTICS.arxiv 2020-05-26
255 COVIDNet-S: Towards Computer-aided Severity Assessment Via Training And Validation Of Deep Neural Networks For Geographic Extent And Opacity Extent Scoring Of Chest X-rays For SARS-CoV-2 Lung Disease Severity ALEXANDER WONG et. al. In this proof-of-concept study, we assess the feasibility of computer-aided scoring of CXRs of SARS-CoV-2 lung disease severity using a deep learning system. EESS.arxiv 2020-05-26
256 Modelling Provincial Covid-19 Epidemic Data In Italy Using An Adjusted Time-dependent SIRD Model LUISA FERRARI et. al. In this paper we develop a predictive model for the spread of COVID-19 infection at a provincial (i.e. EU NUTS-3) level in Italy by using official data from the Italian Ministry of Health integrated with data extracted from daily official press conferences of regional authorities and from local newspaper websites. STATISTICS.arxiv 2020-05-25
257 Modeling The Dynamics Of The COVID-19 Population In Australia: A Probabilistic Analysis ALI ESHRAGH et. al. This paper presents a novel method of dynamically modeling and forecasting the COVID-19 pandemic in Australia with a high degree of accuracy and in a timely manner using limited data; a valuable resource that can be used to guide government decision-making on societal restrictions on a daily and/or weekly basis. STATISTICS.arxiv 2020-05-25
258 Mixing Properties Of Skellam-GARCH Processes PAUL DOUKHAN et. al. We consider integer-valued GARCH processes, where the count variable conditioned on past values of the count and state variables follows a so-called Skellam distribution. MATH.arxiv 2020-05-25
259 An Ensemble Approach To Short-term Forecast Of COVID-19 Intensive Care Occupancy In Italian Regions ALESSIO FARCOMENI et. al. In this work we show a simple strategy for short-term prediction of Covid-19 ICU beds, that has proved very effective during the Italian outbreak in February to May 2020. STATISTICS.arxiv 2020-05-25
260 Predicting COVID-19 Pneumonia Severity On Chest X-ray With Deep Learning JOSEPH PAUL COHEN et. al. In this study, we present a severity score prediction model for COVID-19 pneumonia for frontal chest X-ray images. EESS.arxiv 2020-05-24
261 Coronavirus: Comparing COVID-19, SARS And MERS In The Eyes Of AI ANAS TAHIR et. al. In this work, authors used deep machine learning algorithms along with innovative image pre-processing techniques to distinguish COVID-19 images from SARS and MERS images. EESS.arxiv 2020-05-23
262 SODA: Detecting Covid-19 In Chest X-rays With Semi-supervised Open Set Domain Adaptation JIELI ZHOU et. al. In an attempt to address these two important issues, we formulate the problem of COVID-19 chest x-ray image classification in a semi-supervised open set domain adaptation setting and propose a novel domain adaptation method, Semi-supervised Open set Domain Adversarial network (SODA). EESS.arxiv 2020-05-22
263 Coswara — A Database Of Breathing, Cough, And Voice Sounds For COVID-19 Diagnosis NEERAJ SHARMA et. al. Towards this, the paper presents an early effort in creating (and analyzing) a database, called Coswara, of respiratory sounds, namely, cough, breath, and voice. EESS.arxiv 2020-05-21
264 The Hypothesis Of Testing: Paradoxes Arising Out Of Reported Coronavirus Case-counts WALTER DEMPSEY In this paper, we attempt to clarify this interaction. STATISTICS.arxiv 2020-05-20
265 A Bayesian – Deep Learning Model For Estimating Covid-19 Evolution In Spain STEFANO CABRAS This work proposes a semi-parametric approach to estimate Covid-19 (SARS-CoV-2) evolution in Spain. STATISTICS.arxiv 2020-05-20
266 A Critical Assessment Of Some Recent Work On COVID-19 JÖRG STOYE A Critical Assessment Of Some Recent Work On COVID-19 STATISTICS.arxiv 2020-05-20
267 Lung Segmentation From Chest X-rays Using Variational Data Imputation RAGHAVENDRA SELVAN et. al. In this work, we focus on segmenting lungs from such abnormal CXRs as part of a pipeline aimed at automated risk scoring of COVID-19 from CXRs. EESS.arxiv 2020-05-20
268 Understanding The Effects Of Tennessee’s Open Covid-19 Testing Policy: Bounding Policy Effects With Nonrandomly Missing Data BRANTLY CALLAWAY et. al. To understand the effect of the policy, we combine standard identifying assumptions with additional weak assumptions to deal with non-random testing that lead to bounds on policy effects of interest. EESS.arxiv 2020-05-19
269 A Note On ‘Collider Bias Undermines Our Understanding Of COVID-19 Disease Risk And Severity’ And How Causal Bayesian Networks Both Expose And Resolve The Problem NORMAN FENTON We extend the work of Griffith et al making more explicit use of graphical causal models to interpret observed data. STATISTICS.arxiv 2020-05-18
270 Disaster Resilience And Asset Prices MARCO PAGANO et. al. This paper investigates whether security markets price the effect of social distancing on firms’ operations. FINANCE.arxiv 2020-05-18
271 Bayesian Adjustment For Preferential Testing In Estimating The COVID-19 Infection Fatality Rate HARLAN CAMPBELL et. al. In this paper we take a partial identifiability approach, formulating clearly where deliberate prior assumptions can be made and presenting a Bayesian model, which pools information from different samples. STATISTICS.arxiv 2020-05-18
272 Inbetweening Auto-animation Via Fokker-Planck Dynamics And Thresholding YUAN GAO et. al. We propose an equilibrium-driven deformation algorithm (EDDA) to simulate the inbetweening transformations starting from an initial image to an equilibrium image, which covers images varying from a greyscale type to a colorful type on plane or manifold. MATH.arxiv 2020-05-18
273 Curating A COVID-19 Data Repository And Forecasting County-level Death Counts In The United States NICK ALTIERI et. al. In this paper, we present our continuous curation of a large data repository containing COVID-19 information from a range of sources. STATISTICS.arxiv 2020-05-16
274 Nowcasting Fatal COVID-19 Infections On A Regional Level In Germany MARC SCHNEBLE et. al. We analyse the temporal and regional structure in mortality rates related to COVID-19 infections. STATISTICS.arxiv 2020-05-15
275 India Growth Forecast For 2020-21 AMARENDRA DAS et. al. We derive the quarterly GVA for 2020-21 by using certain assumptions on the capacity utilisation in different sectors and using the quarterly data of 2019-20. FINANCE.arxiv 2020-05-14
276 Combining Population And Study Data For Inference On Event Rates CHRISTOPH ROTHE We clarify issues with the precise definition of the target parameter in this context, and propose confidence intervals (CIs) based on classical statistical principles that result in good coverage properties. STATISTICS.arxiv 2020-05-14
277 Public Concern And The Financial Markets During The COVID-19 Outbreak MICHELE COSTOLA et. al. Public Concern And The Financial Markets During The COVID-19 Outbreak FINANCE.arxiv 2020-05-14
278 Are Official Confirmed Cases And Fatalities Counts Good Enough To Study The COVID-19 Pandemic Dynamics? A Critical Assessment Through The Case Of Italy KRZYSZTOF BARTOSZEK et. al. In this work we do not fit models but we rather investigate whether this task is possible at all. STATISTICS.arxiv 2020-05-14
279 Simpson’s Paradox In Covid-19 Case Fatality Rates: A Mediation Analysis Of Age-related Causal Effects JULIUS VON KÜGELGEN et. al. Using this as a motivating example, we introduce basic concepts from mediation analysis and show how these can be used to quantify different direct and indirect effects when assuming a coarse-grained causal graph involving country, age, and mortality. STATISTICS.arxiv 2020-05-14
280 Inference For A Test-negative Case-control Study With Added Controls BIKRAM KARMAKAR et. al. We propose two new methods for inference which have better interpretability and higher statistical power for these designs. STATISTICS.arxiv 2020-05-13
281 Crackovid: Optimizing Group Testing LOUIS ABRAHAM et. al. We start by proposing a mathematically principled objective, grounded in information theory. STATISTICS.arxiv 2020-05-13
282 When And How To Lift The Lockdown? Global COVID-19 Scenario Analysis And Policy Assessment Using Compartmental Gaussian Processes ZHAOZHI QIAN et. al. To this end, this paper develops a Bayesian model for predicting the effects of COVID-19 lockdown policies in a global context — we treat each country as a distinct data point, and exploit variations of policies across countries to learn country-specific policy effects. STATISTICS.arxiv 2020-05-13
283 Multi-Channel Transfer Learning Of Chest X-ray Images For Screening Of COVID-19 SAMPA MISRA et. al. To address this challenge of reading chest X-rays by radiologists quickly, we present a multi-channel transfer learning model based on ResNet architecture to facilitate the diagnosis of COVID-19 chest X-ray. EESS.arxiv 2020-05-12
284 Modelling The Extremes Of Seasonal Viruses And Hospital Congestion: The Example Of Flu In A Swiss Hospital SETAREH RANJBAR et. al. In this study, we use three years of daily data from one of the biggest hospitals in Switzerland and focus on modelling the extremes of hospital visits from patients showing flu-like symptoms and the number of positive cases of flu. STATISTICS.arxiv 2020-05-12
285 Digit Analysis For Covid-19 Reported Data JEAN-FRANÇOIS COEURJOLLY In this paper, we examine data available from China at the city and provincial levels and we compare them with Canadian provincial data, US state data and French regional data. STATISTICS.arxiv 2020-05-11
286 Time Varying Markov Process With Partially Observed Aggregate Data; An Application To Coronavirus CHRISTIAN GOURIEROUX et. al. In this paper, we consider parametric time varying Markov processes of Coronavirus propagation and show how to estimate the model parameters and approximate the unobserved counts from daily numbers of infected and detected individuals and total daily death counts. STATISTICS.arxiv 2020-05-09
287 Methods For Forecasting The Effect Of Exogenous Risk On Stock Markets KARINA ARIAS-CALLUARI et. al. Here we present two cases to forecast the S\&P500 index. FINANCE.arxiv 2020-05-08
288 Convolutional Sparse Support Estimator Based Covid-19 Recognition From X-ray Images MEHMET YAMAC et. al. In this study, we propose a novel approach for Covid-19 recognition from chest X-ray images. EESS.arxiv 2020-05-08
289 Joint Prediction And Time Estimation Of COVID-19 Developing Severe Symptoms Using Chest CT Scan XIAOFENG ZHU et. al. In this study, we propose a joint classification and regression method to determine whether the patient would develop severe symptoms in the later time, and if yes, predict the possible conversion time that the patient would spend to convert to the severe stage. EESS.arxiv 2020-05-07
290 Synergistic Learning Of Lung Lobe Segmentation And Hierarchical Multi-Instance Classification For Automated Severity Assessment Of COVID-19 In CT Images KELEI HE et. al. To this end, we propose a synergistic learning framework for automated severity assessment of COVID-19 in 3D CT images, by jointly performing lung lobe segmentation and multi-instance classification. EESS.arxiv 2020-05-07
291 Adaptive Feature Selection Guided Deep Forest For COVID-19 Classification With Chest CT LIANG SUN et. al. In this paper, we propose an Adaptive Feature Selection guided Deep Forest (AFS-DF) for COVID-19 classification based on chest CT images. EESS.arxiv 2020-05-07
292 CovidCTNet: An Open-Source Deep Learning Approach To Identify Covid-19 Using CT Image TAHEREH JAVAHERI et. al. To enhance the accuracy of CT imaging detection, we developed an open-source set of algorithms called CovidCTNet that successfully differentiates Covid-19 from community-acquired pneumonia (CAP) and other lung diseases. EESS.arxiv 2020-05-06
293 A Comparison Of Group Testing Architectures For COVID-19 Testing J. BATSON et. al. In this note, we compare several popular group testing schemes in the context of qPCR testing for COVID-19. STATISTICS.arxiv 2020-05-06
294 Detection Of Line Artefacts In Lung Ultrasound Images Of COVID-19 Patients Via Non-Convex Regularization OKTAY KARAKUŞ et. al. In this paper, we present a novel method for line artefacts quantification in lung ultrasound (LUS) images of COVID-19 patients. EESS.arxiv 2020-05-06
295 Cluster-based Dual Evolution For Multivariate Time Series: Analyzing COVID-19 NICK JAMES et. al. This paper proposes a cluster-based method to analyze the evolution of multivariate time series and applies this to the COVID-19 pandemic. STATISTICS.arxiv 2020-05-05
296 Coronavirus And Sports Leagues: How To Obtain A Fair Ranking If The Season Cannot Resume? LÁSZLÓ CSATÓ Our approach is applied for six major premier European soccer competitions, where the rankings are found to be robust concerning the weight of adjustment for the strength of the opponents. STATISTICS.arxiv 2020-05-05
297 Did COVID-19 Infections Decline Before UK Lockdown? SIMON N. WOOD A Bayesian inverse problem approach applied to UK data on COVID-19 deaths and the disease duration distribution suggests that infections were in decline before full UK lockdown (24 March 2020), and that infections in Sweden started to decline only a day or two later. STATISTICS.arxiv 2020-05-05
298 Stocks Vote With Their Feet: Can A Piece Of Paper Document Fights The COVID-19 Pandemic? J. SU et. al. This paper uses the DCC-GARCH model to fit conditional covariance’s change rule of multi-series. EESS.arxiv 2020-05-05
299 Is Time To Intervention In The COVID-19 Outbreak Really Important? A Global Sensitivity Analysis Approach EMANUELE BORGONOVO et. al. Using publicly available data for the pandemic progression in Italy, we quantitatively assess the effect of the intervention time on the pandemic expansion, with a methodology that combines a generalized susceptible-exposed-infectious-recovered (SEIR) model together with statistical learning methods. STATISTICS.arxiv 2020-05-04
300 Propagation Of Epidemics Along Lines With Fast Diffusion HENRI BERESTYCKI et. al. We propose a new simple model of propagation of epidemics which exhibits this effect and allows for a quantitative analysis. MATH.arxiv 2020-05-04
301 3D Tomographic Pattern Synthesis For Enhancing The Quantification Of COVID-19 SIQI LIU et. al. In this paper, we propose to use synthetic datasets to augment an existing COVID-19 database to tackle these challenges. EESS.arxiv 2020-05-04
302 Deep Convolutional Neural Networks To Diagnose COVID-19 And Other Pneumonia Diseases From Posteroanterior Chest X-Rays PIERRE G. B. MOUTOUNET-CARTAN The article explores different deep convolutional neural network architectures trained and tested on posteroanterior chest X-rays of 327 patients who are healthy (152 patients), diagnosed with COVID-19 (125), and other types of pneumonia (48). EESS.arxiv 2020-05-02
303 Estimation Of COVID-19 Spread Curves Integrating Global Data And Borrowing Information SE YOON LEE et. al. In this paper, we propose a Bayesian hierarchical model that integrates global data for real-time prediction of infection trajectory for multiple countries. STATISTICS.arxiv 2020-05-01
304 Data-Driven Modeling Reveals The Impact Of Stay-at-Home Orders On Human Mobility During The COVID-19 Pandemic In The U.S CHENFENG XIONG et. al. The models developed in the study exhibit high prediction accuracy and can be applied to inform epidemics modeling with empirically verified mobility trends and to support time-sensitive decision-making processes. STATISTICS.arxiv 2020-05-01
305 Integrated Time Series Summarization And Prediction Algorithm And Its Application To COVID-19 Data Mining MOGENS GRAF PLESSEN This paper proposes a simple method to extract from a set of multiple related time series a compressed representation for each time series based on statistics for the entire set of all time series. STATISTICS.arxiv 2020-05-01
306 A Deep Convolutional Neural Network For COVID-19 Detection Using Chest X-Rays PEDRO R. A. S. BASSI et. al. Methods: We fine-tuned neural networks pretrained on ImageNet and applied a twice transfer learning approach, using NIH ChestX-Ray14 dataset as an intermediate step. EESS.arxiv 2020-04-30
307 Two Burning Questions On COVID-19: Did Shutting Down The Economy Help? Can We (partially) Reopen The Economy Without Risking The Second Wave? ANISH AGARWAL et. al. In this memo, we provide an overview of Synthetic Interventions (a natural generalization of Synthetic Control), a data-driven and statistically principled method to perform what-if scenario planning, i.e., for policy makers to understand the trade-offs between different interventions before having to actually enact them. EESS.arxiv 2020-04-30
308 An Early Study On Intelligent Analysis Of Speech Under COVID-19: Severity, Sleep Quality, Fatigue, And Anxiety JING HAN et. al. In this study, we focus on developing some potential use-cases of intelligent speech analysis for COVID-19 diagnosed patients. EESS.arxiv 2020-04-30
309 Spatiotemporal Dynamics, Nowcasting And Forecasting Of COVID-19 In The United States LI WANG et. al. In this paper, we investigate the disease dynamics by working at the interface of theoretical models and empirical data by combining the advantages of mathematical and statistical models. STATISTICS.arxiv 2020-04-29
310 Impacts Of California Proposition 47 On Crime In Santa Monica, CA JENNIFER CRODELLE et. al. We examine crime patterns in Santa Monica, California before and after passage of Proposition 47, a 2014 initiative that reclassified some non-violent felonies to misdemeanors. STATISTICS.arxiv 2020-04-29
311 Predicting Infection Of COVID-19 In Japan: State Space Modeling Approach GENYA KOBAYASHI et. al. This study analyzes the real time data from March 1 to April 22, 2020 by adopting a sophisticated statistical modeling tool based on the state space model combined with the well-known susceptible-exposed-infected (SIR) model. STATISTICS.arxiv 2020-04-28
312 US Equity Risk Premiums During The COVID-19 Pandemic ALAN L. LEWIS We study equity risk premiums in the United States during the COVID-19 pandemic. FINANCE.arxiv 2020-04-28
313 A Critic Evaluation Of Methods For COVID-19 Automatic Detection From X-Ray Images GIANLUCA MAGUOLO et. al. In this paper, we compare and evaluate different testing protocols used for automatic COVID-19 diagnosis from X-Ray images in the recent literature. EESS.arxiv 2020-04-27
314 Robust Screening Of COVID-19 From Chest X-ray Via Discriminative Cost-Sensitive Learning TIANYANG LI et. al. In this paper, we report our solution, discriminative cost-sensitive learning (DCSL), which should be the choice if the clinical needs the assisted screening of COVID-19 from chest X-rays. We collected a large-scale multi-class dataset comprised of 2,239 chest X-ray examples: 239 examples from confirmed COVID-19 cases, 1,000 examples with confirmed bacterial or viral pneumonia cases, and 1,000 examples of healthy people. EESS.arxiv 2020-04-27
315 Towards Efficient COVID-19 CT Annotation: A Benchmark For Lung And Infection Segmentation JUN MA et. al. In this paper, we created a COVID-19 3D CT dataset with 20 cases that contains 1800+ annotated slices and made it publicly available. To promote the development of annotation-efficient deep learning methods, we built three benchmarks for lung and infection segmentation that contain current main research interests, e.g., few-shot learning, domain generalization, and knowledge transfer. To the best of our knowledge, this work presents the largest public annotated COVID-19 CT volume dataset, the first segmentation benchmark, and the most pre-trained models up to now. EESS.arxiv 2020-04-26
316 POCOVID-Net: Automatic Detection Of COVID-19 From A New Lung Ultrasound Imaging Dataset (POCUS) JANNIS BORN et. al. Our contribution is threefold. EESS.arxiv 2020-04-25
317 An Epidemiological Modelling Approach For Covid19 Via Data Assimilation PHILIP NADLER et. al. We propose an epidemiological model for forecasting and policy evaluation which incorporates new data in real-time through variational data assimilation. STATISTICS.arxiv 2020-04-25
318 Inside The Mind Of Investors During The COVID-19 Pandemic: Evidence From The StockTwits Data HASAN FALLAHGOUL We study the investor beliefs, sentiment and disagreement, about stock market returns during the COVID-19 pandemic using a large number of messages of investors on a social media investing platform, \textit{StockTwits}. FINANCE.arxiv 2020-04-24
319 A Cascaded Learning Strategy For Robust COVID-19 Pneumonia Chest X-Ray Screening CHUN-FU YEH et. al. We introduce a comprehensive screening platform for the COVID-19 (a.k.a., SARS-CoV-2) pneumonia. EESS.arxiv 2020-04-24
320 Estimating The Number Of Infections And The Impact Of Non-pharmaceutical Interventions On COVID-19 In European Countries: Technical Description Update SETH FLAXMAN et. al. In this technical update, we extend a semi-mechanistic Bayesian hierarchical model that infers the impact of these interventions and estimates the number of infections over time. STATISTICS.arxiv 2020-04-23
321 The TVBG-SEIR Spline Model For Analysis Of COVID-19 Spread, And A Tool For Prediction Scenarios OGNYAN KOUNCHEV et. al. The main purpose of the present research is to introduce a spline-based SEIR model with Time-varying Beta and Gamma parameters, or abbreviated TVBG-SEIR model, which is used to estimate the practical implications of the public health interventions and measures. MATH.arxiv 2020-04-23
322 Excess Registered Deaths In England And Wales During The COVID-19 Pandemic, March 2020 To May 2020 DREW M THOMAS I address that critique by fitting a generalized additive model to weekly counts of all deaths registered in England and Wales during the 2010s. STATISTICS.arxiv 2020-04-23
323 COVID-19 Chest CT Image Segmentation — A Deep Convolutional Neural Network Solution QINGSEN YAN et. al. In this work, we try to establish a new deep convolutional neural network tailored for segmenting the chest CT images with COVID-19 infections. EESS.arxiv 2020-04-23
324 Automated Diagnosis Of COVID-19 With Limited Posteroanterior Chest X-ray Images Using Fine-tuned Deep Neural Networks NARINDER SINGH PUNN et. al. Following from this context, this article presents the random oversampling and weighted class loss function approach for unbiased fine-tuned learning (transfer learning) in various state-of-the-art deep learning approaches such as baseline ResNet, Inception-v3, Inception ResNet-v2, DenseNet169, and NASNetLarge to perform binary classification (as normal and COVID-19 cases) and also multi-class classification (as COVID-19, pneumonia, and normal case) of posteroanterior CXR images. EESS.arxiv 2020-04-23
325 Optimal Quarantine Strategies For COVID-19 Control Models ELLINA GRIGORIEVA et. al. The optimal solutions are obtained numerically using BOCOP 2.0.5 software. MATH.arxiv 2020-04-22
326 Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images DENG-PING FAN et. al. Our semi-supervised framework can improve the learning ability and achieve a higher performance. EESS.arxiv 2020-04-22
327 Automatic Detection Of Coronavirus Disease (COVID-19) In X-ray And CT Images: A Machine Learning-Based Approach SARA HOSSEINZADEH KASSANI et. al. In this study, we compare popular deep learning-based feature extraction frameworks for automatic COVID-19 classification. EESS.arxiv 2020-04-22
328 Deep Learning For Screening COVID-19 Using Chest X-Ray Images SANHITA BASU et. al. Therefore, we propose a new concept called domain extension transfer learning (DETL). EESS.arxiv 2020-04-22
329 Examining Lead-Lag Relationships In-Depth, With Focus On FX Market As Covid-19 Crises Unfolds KARTIKAY GUPTA et. al. The present research proposes a new technique which helps better identify the lead-lag relationship empirically. FINANCE.arxiv 2020-04-22
330 CovidAID: COVID-19 Detection Using Chest X-Ray ARPAN MANGAL et. al. In this work we propose the use of chest X-Ray to prioritize the selection of patients for further RT-PCR testing. EESS.arxiv 2020-04-21
331 How Much Did The Tourism Industry Lost? Estimating Earning Loss Of Tourism In The Philippines RAFFY S. CENTENO et. al. Several models were considered based on the autocorrelation and partial autocorrelation graphs. STATISTICS.arxiv 2020-04-21
332 Detection And Estimation Of Local Signals XIAO FANG et. al. We study the maximum score statistic to detect and estimate local signals in the form of change-points in the level, slope, or other property of a sequence of observations, and to segment the sequence when there appear to be multiple changes. MATH.arxiv 2020-04-17
333 Estimating And Projecting Air Passenger Traffic During The COVID-19 Coronavirus Outbreak And Its Socio-Economic Impact STEFANO MARIA IACUS et. al. Based on historical data from January 2010 till October 2019, a forecasting model is implemented in order to set a reference baseline. STATISTICS.arxiv 2020-04-17
334 A New Modified Deep Convolutional Neural Network For Detecting COVID-19 From X-ray Images MOHAMMAD RAHIMZADEH et. al. In this paper, we have trained several deep convolutional networks with the introduced training techniques for classifying X-ray images into three classes: normal, pneumonia, and COVID-19, based on two open-source datasets. EESS.arxiv 2020-04-16
335 BETS: The Dangers Of Selection Bias In Early Analyses Of The Coronavirus Disease (COVID-19) Pandemic QINGYUAN ZHAO et. al. We developed a generative model we call BETS for four key epidemiological events—Beginning of exposure, End of exposure, time of Transmission, and time of Symptom onset (BETS)—and derived explicit formulas to correct for the sample selection. STATISTICS.arxiv 2020-04-16
336 Short-Term Covid-19 Forecast For Latecomers MARCELO MEDEIROS et. al. We propose a simple statistical method for short-term real-time forecasting of the number of Covid-19 cases and fatalities in countries that are latecomers — i.e., countries where cases of the disease started to appear some time after others. STATISTICS.arxiv 2020-04-16
337 Radiologist-Level COVID-19 Detection Using CT Scans With Detail-Oriented Capsule Networks ARYAN MOBINY et. al. Motivated by this challenge, our paper proposes a novel learning architecture, called Detail-Oriented Capsule Networks (DECAPS), for the automatic diagnosis of COVID-19 from Computed Tomography (CT) scans. EESS.arxiv 2020-04-15
338 Relational Modeling For Robust And Efficient Pulmonary Lobe Segmentation In CT Scans WEIYI XIE et. al. In this paper, we propose a relational approach (RTSU-Net) that leverages structured relationships by introducing a novel non-local neural network module. EESS.arxiv 2020-04-15
339 Computer Vision For COVID-19 Control: A Survey ANWAAR ULHAQ et. al. It motivated us to review the recent work, collect information about available research resources and an indication of future research directions. EESS.arxiv 2020-04-15
340 Iteratively Pruned Deep Learning Ensembles For COVID-19 Detection In Chest X-rays SIVARAMAKRISHNAN RAJARAMAN et. al. We demonstrate use of iteratively pruned deep learning model ensembles for detecting pulmonary manifestation of COVID-19 with chest X-rays. EESS.arxiv 2020-04-15
341 JCS: An Explainable COVID-19 Diagnosis System By Joint Classification And Segmentation YU-HUAN WU et. al. In this paper, we develop a novel Joint Classification and Segmentation (JCS) system to perform real-time and explainable COVID-19 diagnosis. To train our JCS system, we construct a large scale COVID-19 Classification and Segmentation (COVID-CS) dataset, with 144,167 CT images of 400 COVID-19 patients and 350 uninfected cases. EESS.arxiv 2020-04-15
342 Weakly Supervised Deep Learning For COVID-19 Infection Detection And Classification From CT Images SHAOPING HU et. al. In this study, we propose a weakly supervised deep learning strategy for detecting and classifying COVID-19 infection from CT images. EESS.arxiv 2020-04-14
343 Group Testing For COVID-19: How To Stop Worrying And Test More LAKSHMI N. THEAGARAJAN We present multiple group testing algorithms that could reduce the number of tests performed for COVID-19 diagnosis. STATISTICS.arxiv 2020-04-14
344 An Automatic COVID-19 CT Segmentation Network Using Spatial And Channel Attention Mechanism TONGXUE ZHOU et. al. In this paper, we propose a U-Net based segmentation network using attention mechanism. EESS.arxiv 2020-04-14
345 The Effect Of Stay-at-home Orders On COVID-19 Cases And Fatalities In The United States JAMES H. FOWLER et. al. Here, we combine data on the timing of stay-at-home orders with daily confirmed COVID-19 cases and fatalities at the county level in the United States. STATISTICS.arxiv 2020-04-13
346 Estimating The COVID-19 Infection Rate: Anatomy Of An Inference Problem CHARLES F. MANSKI et. al. This paper explains the logical problem of bounding these rates and reports illustrative findings, using data from Illinois, New York, and Italy. EESS.arxiv 2020-04-13
347 Towards An Effective And Efficient Deep Learning Model For COVID-19 Patterns Detection In X-ray Images EDUARDO LUZ et. al. Thus, the main goal of this work is to propose an accurate yet efficient method in terms of memory and processing time for the problem of COVID-19 screening in chest X-rays. EESS.arxiv 2020-04-12
348 Residual Attention U-Net For Automated Multi-Class Segmentation Of COVID-19 Chest CT Images XIAOCONG CHEN et. al. To this end, we proposed a novel deep learning algorithm for automated segmentation of multiple COVID-19 infection regions. EESS.arxiv 2020-04-12
349 Deep Learning COVID-19 Features On CXR Using Limited Training Data Sets YUJIN OH et. al. To address this problem, here we propose a patch-based convolutional neural network approach with a relatively small number of trainable parameters for COVID-19 diagnosis. EESS.arxiv 2020-04-12
350 Unveiling COVID-19 From Chest X-ray With Deep Learning: A Hurdles Race With Small Data ENZO TARTAGLIONE et. al. In this study we provide insights and also raise warnings on what is reasonable to expect by applying deep-learning to COVID classification of CXR images. EESS.arxiv 2020-04-11
351 Detection Of Covid-19 From Chest X-ray Images Using Artificial Intelligence: An Early Review MUHAMMAD ILYAS et. al. In this article we will discuss the different approaches used for the detection of COVID-19 and the challenges we are facing. EESS.arxiv 2020-04-11
352 CoroNet: A Deep Neural Network For Detection And Diagnosis Of COVID-19 From Chest X-ray Images ASIF IQBAL KHAN et. al. Methods In this study, we propose CoroNet, a Deep Convolutional Neural Network model to automatically detect COVID-19 infection from chest X-ray images. EESS.arxiv 2020-04-10
353 Modeling The Heterogeneity In COVID-19’s Reproductive Number And Its Impact On Predictive Scenarios CLAIRE DONNAT et. al. In this perspective, instead of considering a single, fixed $R$, we model the reproductive number as a distribution sampled from a simple Bayesian hierarchical model. STATISTICS.arxiv 2020-04-10
354 DeepCOVIDExplainer: Explainable COVID-19 Diagnosis Based On Chest X-ray Images MD. REZAUL KARIM et. al. In this paper, we propose an explainable deep neural networks(DNN)-based method for automatic detection of COVID-19 symptoms from CXR images, which we call DeepCOVIDExplainer. EESS.arxiv 2020-04-09
355 COVID-19 In A Social Reinsurance Framework: Forewarned Is Forearmed S. SAHIN et. al. In this paper, we present a statistical analysis of the data related to the COVID-19 outbreak in China, specifically the infection speed, death and fatality rates in Hubei province. STATISTICS.arxiv 2020-04-09
356 A Machine Learning Methodology For Real-time Forecasting Of The 2019-2020 COVID-19 Outbreak Using Internet Searches, News Alerts, And Estimates From Mechanistic Models DIANBO LIU et. al. We present a timely and novel methodology that combines disease estimates from mechanistic models with digital traces, via interpretable machine-learning methodologies, to reliably forecast COVID-19 activity in Chinese provinces in real-time. STATISTICS.arxiv 2020-04-08
357 A Sample Approach To The Estimation Of The Critical Parameters Of The SARS-CoV-2 Epidemics: An Operational Design GIORGIO ALLEVA et. al. Given the urgent informational needs connected with the pandemic diffusion of the Covid-19 infection, in this paper we propose a sample design to build up a continuous-time surveillance system. STATISTICS.arxiv 2020-04-08
358 Learning As We Go: An Examination Of The Statistical Accuracy Of COVID19 Daily Death Count Predictions ROMAN MARCHANT et. al. This paper provides a formal evaluation of the predictive performance of a model (and its various updates) developed by the Institute for Health Metrics and Evaluation (IHME) for predicting daily deaths attributed to COVID19 for each state in the United States. STATISTICS.arxiv 2020-04-08
359 COVID-19: A Survey On Public Medical Imaging Data Resources ROMAN KALKREUTH et. al. The purpose of this survey is to simplify the access to open COVID-19 image data resources for all scientists currently working on the coronavirus crisis. EESS.arxiv 2020-04-07
360 COVID_MTNet: COVID-19 Detection With Multi-Task Deep Learning Approaches MD ZAHANGIR ALOM et. al. In this paper, we propose a fast and efficient way to identify COVID-19 patients with multi-task deep learning (DL) methods. EESS.arxiv 2020-04-07
361 Robust Inference For Nonlinear Regression Models From The Tsallis Score: Application To Covid-19 Contagion In Italy PAOLO GIRARDI et. al. We discuss an approach for fitting robust nonlinear regression models, which can be employed to model and predict the contagion dynamics of the Covid-19 in Italy. STATISTICS.arxiv 2020-04-07
362 Variable Pool Testing For Infection Spread Estimation ITSIK BERGEL We present a method for efficient estimation of the prevalence of infection in a population with high accuracy using only a small number of tests. STATISTICS.arxiv 2020-04-07
363 A SARS-CoV-2 Microscopic Image Dataset With Ground Truth Images And Visual Features CHEN LI et. al. To analyse the visual information of it, we build a SARS-CoV-2 Microscopic Image Dataset (SC2-MID) with 48 electron microscopic images and also prepare their ground truth images. EESS.arxiv 2020-04-07
364 Coronavirus (COVID-19) Classification Using Deep Features Fusion And Ranking Technique UMUT OZKAYA et. al. In this study, a novel method was proposed as fusing and ranking deep features to detect COVID-19 in early phase. EESS.arxiv 2020-04-07
365 COVID-MobileXpert: On-Device COVID-19 Patient Triage And Follow-up Using Chest X-rays XIN LI et. al. In view of this need, we present COVID-MobileXpert: a lightweight deep neural network (DNN) based mobile app that can use chest X-ray (CXR) for COVID-19 case screening and radiological trajectory prediction. EESS.arxiv 2020-04-06
366 Review Of Artificial Intelligence Techniques In Imaging Data Acquisition, Segmentation And Diagnosis For COVID-19 FENG SHI et. al. In this review paper, we thus cover the entire pipeline of medical imaging and analysis techniques involved with COVID-19, including image acquisition, segmentation, diagnosis, and follow-up. EESS.arxiv 2020-04-06
367 Coronavirus Detection And Analysis On Chest CT With Deep Learning OPHIR GOZES et. al. We present our results on a dataset comprised of 110 confirmed COVID-19 patients from Zhejiang province, China. EESS.arxiv 2020-04-06
368 Estimating The Number Of SARS-CoV-2 Infections And The Impact Of Social Distancing In The United States JAMES JOHNDROW et. al. Here we present estimates of the extent to which confirmed cases in the United States undercount the true number of infections, and analyze how effective social distancing measures have been at mitigating or suppressing the virus. STATISTICS.arxiv 2020-04-06
369 Analyzing Initial Stage Of COVID-19 Transmission Through Bayesian Time-varying Model ARKAPRAVA ROY et. al. To model the non-stationary spread, we propose a novel time-varying semiparametric AR$(p)$ model for the count valued time-series of newly affected cases, collected every day and also extend it to propose a novel time-varying INGARCH model. STATISTICS.arxiv 2020-04-05
370 Modelling Death Rates Due To COVID-19: A Bayesian Approach CRISTIAN BAYES et. al. Design: With a priori information obtained from the daily number of deaths due to CODIV-19 in China and data from the Peruvian authorities, we constructed a predictive Bayesian non-linear model for the number of deaths in Peru. STATISTICS.arxiv 2020-04-05
371 Generic Probabilistic Modelling And Non-homogeneity Issues For The UK Epidemic Of COVID-19 ANATOLY ZHIGLJAVSKY et. al. We present a stochastic model that captures the inherently probabilistic nature of contagion between population members. STATISTICS.arxiv 2020-04-04
372 Finding Covid-19 From Chest X-rays Using Deep Learning On A Small Dataset LAWRENCE O. HALL et. al. This paper explores how useful chest X-ray images can be in diagnosing COVID-19 disease. EESS.arxiv 2020-04-04
373 Quantification Of Tomographic Patterns Associated With COVID-19 From Chest CT SHIKHA CHAGANTI et. al. Purpose: To present a method that automatically segments and quantifies abnormal CT patterns commonly present in coronavirus disease 2019 (COVID-19), namely ground glass opacities and consolidations. EESS.arxiv 2020-04-02
374 COVID-19: Should We Test Everyone? GRACE YI et. al. In this paper, we evaluate the situation from the statistical viewpoint by factoring into the considerations of the uncertainty and inaccuracy of the test, an issue that is often overlooked by the general public. STATISTICS.arxiv 2020-04-02
375 AI4COVID-19: AI Enabled Preliminary Diagnosis For COVID-19 From Cough Samples Via An App ALI IMRAN et. al. Building on the prior work on cough-based diagnosis of respiratory diseases, we propose, develop and test an Artificial Intelligence (AI)-powered screening solution for COVID-19 infection that is deployable via a smartphone app. EESS.arxiv 2020-04-02
376 Detecting Suspected Epidemic Cases Using Trajectory Big Data CHUANSAI ZHOU et. al. Based on trajectory provided by the big data and the mean field theory, we establish an aggregated risk mean field that contains information of all risk-spreading particles by proposing a spatio-temporal model named HiRES risk map. STATISTICS.arxiv 2020-04-02
377 Stochastic Modeling And Estimation Of COVID-19 Population Dynamics NIKOLAY M. YANEV et. al. The aim of the paper is to describe a model of the development of the Covid-19 contamination of the population of a country or a region. STATISTICS.arxiv 2020-04-02
378 A Model Of Supply-Chain Decisions For Resource Sharing With An Application To Ventilator Allocation To Combat COVID-19 SANJAY MEHROTRA et. al. This paper presents a stochastic optimization model for allocating and sharing a critical resource in the case of a pandemic. MATH.arxiv 2020-04-02
379 Extracting Possibly Representative COVID-19 Biomarkers From X-Ray Images With Deep Learning Approach And Image Data Related To Pulmonary Diseases IOANNIS D. APOSTOLOPOULOS et. al. In this study, the problem of automatically classifying pulmonary diseases, including the recently emerged COVID-19, from X-Ray images, is considered. EESS.arxiv 2020-04-01
380 An ARIMA Model To Forecast The Spread And The Final Size Of COVID-2019 Epidemic In Italy GAETANO PERONE In this article I estimate an autoregressive integrated moving average (ARIMA) model to forecast the epidemic trend over the period after April 4, 2020, by using the Italian epidemiological data at national and regional level. STATISTICS.arxiv 2020-04-01
381 Grandpa, Grandpa, Tell Me The One About Bitcoin Being A Safe Haven: Evidence From The COVID-19 Pandemics LADISLAV KRISTOUFEK We study the quantile correlations of Bitcoin and two benchmarks — S\&P500 and VIX — and we make comparison with gold as the traditional safe haven asset. FINANCE.arxiv 2020-03-31
382 Diagnosing COVID-19 Pneumonia From X-Ray And CT Images Using Deep Learning And Transfer Learning Algorithms HALGURD S. MAGHDID et. al. In this work, we focus on proposing AI tools that can be used by radiologists or healthcare professionals to diagnose COVID-19 cases in a quick and accurate manner. EESS.arxiv 2020-03-31
383 Automated Methods For Detection And Classification Pneumonia Based On X-Ray Images Using Deep Learning KHALID EL ASNAOUI et. al. In this paper, we present a comparison of recent Deep Convolutional Neural Network (DCNN) architectures for automatic binary classification of pneumonia images based fined tuned versions of (VGG16, VGG19, DenseNet201, Inception_ResNet_V2, Inception_V3, Resnet50, MobileNet_V2 and Xception). EESS.arxiv 2020-03-31
384 Efficient Identification Of Infected Sub-population ANŽE SLOSAR We present two such methods that allow an increase in testing efficiency (in terms of total number of test performed) by a factor of $\approx$ 10 if population infection rate is $10^{-2}$ and a factor of $\approx$50 when it is $10^{-3}$. STATISTICS.arxiv 2020-03-31
385 COVID-ResNet: A Deep Learning Framework For Screening Of COVID19 From Radiographs MUHAMMAD FAROOQ et. al. The goal of this work is to build open source and open access datasets and present an accurate Convolutional Neural Network framework for differentiating COVID19 cases from other pneumonia cases. EESS.arxiv 2020-03-31
386 Viral Pneumonia Screening On Chest X-ray Images Using Confidence-Aware Anomaly Detection JIANPENG ZHANG et. al. In this paper, we formulate the task of differentiating viral pneumonia from non-viral pneumonia and healthy controls into an one-class classification-based anomaly detection problem, and thus propose the confidence-aware anomaly detection (CAAD) model, which consists of a shared feature extractor, an anomaly detection module, and a confidence prediction module. EESS.arxiv 2020-03-27
387 Classification Of COVID-19 In Chest X-ray Images Using DeTraC Deep Convolutional Neural Network ASMAA ABBAS et. al. In this paper, we validate and adapt our previously developed CNN, called Decompose, Transfer, and Compose (DeTraC), for the classification of COVID-19 chest X-ray images. EESS.arxiv 2020-03-26
388 COVID-19 Image Data Collection JOSEPH PAUL COHEN et. al. This paper describes the initial COVID-19 open image data collection. EESS.arxiv 2020-03-25
389 Covid-19: Automatic Detection From X-Ray Images Utilizing Transfer Learning With Convolutional Neural Networks IOANNIS D. APOSTOLOPOULOS et. al. In this study, a dataset of X-Ray images from patients with common pneumonia, Covid-19, and normal incidents was utilized for the automatic detection of the Coronavirus. EESS.arxiv 2020-03-25
390 Automatic Detection Of Coronavirus Disease (COVID-19) Using X-ray Images And Deep Convolutional Neural Networks ALI NARIN et. al. In this study, five pre-trained convolutional neural network based models (ResNet50, ResNet101, ResNet152, InceptionV3 and Inception-ResNetV2) have been proposed for the detection of coronavirus pneumonia infected patient using chest X-ray radiographs. EESS.arxiv 2020-03-24
391 A Time Series Method To Analyze Incidence Pattern And Estimate Reproduction Number Of COVID-19 SOUDEEP DEB et. al. In this study, we propose a time series model to analyze the trend pattern of the incidence of COVID-19 outbreak. STATISTICS.arxiv 2020-03-24
392 A Poisson Kalman Filter For Disease Surveillance DONALD EBEIGBE et. al. We develop both a linear and nonlinear (extended) filter. STATISTICS.arxiv 2020-03-24
393 COVIDX-Net: A Framework Of Deep Learning Classifiers To Diagnose COVID-19 In X-Ray Images EZZ EL-DIN HEMDAN et. al. Therefore, the aim of this article is to introduce a new deep learning framework; namely COVIDX-Net to assist radiologists to automatically diagnose COVID-19 in X-ray images. EESS.arxiv 2020-03-24
394 Finding An Accurate Early Forecasting Model From Small Dataset: A Case Of 2019-nCoV Novel Coronavirus Outbreak SIMON JAMES FONG et. al. In this paper, a methodology that embraces these three virtues of data mining from a small dataset is proposed… STATISTICS.arxiv 2020-03-24
395 Exploring The Effects Of COVID-19 Containment Policies On Crime: An Empirical Analysis Of The Short-term Aftermath In Los Angeles GIAN MARIA CAMPEDELLI et. al. This work investigates whether and how COVID-19 containment policies had an immediate impact on crime trends in Los Angeles. STATISTICS.arxiv 2020-03-23
396 Attention U-Net Based Adversarial Architectures For Chest X-ray Lung Segmentation GUSZTÁV GAÁL et. al. Here we present a novel deep learning approach for lung segmentation, a basic, but arduous task in the diagnostic pipeline. EESS.arxiv 2020-03-23
397 COVID-Net: A Tailored Deep Convolutional Neural Network Design For Detection Of COVID-19 Cases From Chest X-Ray Images LINDA WANG et. al. Motivated by this and inspired by the open source efforts of the research community, in this study we introduce COVID-Net, a deep convolutional neural network design tailored for the detection of COVID-19 cases from chest X-ray (CXR) images that is open source and available to the general public. EESS.arxiv 2020-03-22
398 Estimating Uncertainty And Interpretability In Deep Learning For Coronavirus (COVID-19) Detection BIRAJA GHOSHAL et. al. In this paper, we investigate how drop-weights based Bayesian Convolutional Neural Networks (BCNN) can estimate uncertainty in Deep Learning solution to improve the diagnostic performance of the human-machine team using publicly available COVID-19 chest X-ray dataset and show that the uncertainty in prediction is highly correlates with accuracy of prediction. EESS.arxiv 2020-03-22
399 New Statistical Model For Misreported Data With Application To Current Public Health Challenges DAVID MORIÑA et. al. The main goal of this work is to present a new model able to deal with potentially misreported continuous time series. STATISTICS.arxiv 2020-03-20
400 Epilocal: A Real-time Tool For Local Epidemic Monitoring MARCO BONETTI et. al. We describe Epilocal, a simple R program designed to automatically download the most recent data on reported infected SARS-CoV-2 cases for all Italian provinces and regions, and to provide a simple descriptive analysis. STATISTICS.arxiv 2020-03-17
401 Do COVID-19 And Crude Oil Prices Drive The US Economic Policy Uncertainty? CLAUDIU ALBULESCU This paper investigates the effect of the novel coronavirus and crude oil prices on the United States (US) economic policy uncertainty (EPU). FINANCE.arxiv 2020-03-17
402 Building A COVID-19 Vulnerability Index DAVE DECAPRIO et. al. We present the results for three models predicting such complications, with each model increasing predictive effectiveness at the expense of ease of implementation. STATISTICS.arxiv 2020-03-16
403 Modelling And Predicting The Spatio-temporal Spread Of Coronavirus Disease 2019 (COVID-19) In Italy DIEGO GIULIANI et. al. Three subcomponents characterize the estimated model. STATISTICS.arxiv 2020-03-14
404 Coronavirus And Oil Price Crash CLAUDIU ALBULESCU Against this background, the purpose of this paper is to investigate the impact of COVID-19 numbers on crude oil prices, while controlling for the impact of financial volatility and the United States (US) economic policy uncertainty. FINANCE.arxiv 2020-03-13
405 A Note On Early Epidemiological Analysis Of Coronavirus Disease 2019 Outbreak Using Crowdsourced Data GIUSEPPE ARBIA A Note On Early Epidemiological Analysis Of Coronavirus Disease 2019 Outbreak Using Crowdsourced Data STATISTICS.arxiv 2020-03-13
406 Weather-inspired Ensemble-based Probabilistic Prediction Of COVID-19 ROBERTO BUIZZA The objective of this work is to predict the spread of COVID-19 starting from observed data, using a forecast method inspired by probabilistic weather prediction systems operational today. STATISTICS.arxiv 2020-03-10
407 Coronavirus And Financial Volatility: 40 Days Of Fasting And Fear CLAUDIU ALBULESCU Coronavirus And Financial Volatility: 40 Days Of Fasting And Fear FINANCE.arxiv 2020-03-09
408 Joint Spatio-temporal Analysis Of Multiple Response Types Using The Hierarchical Generalized Transformation Model With Application To Coronavirus Disease 2019 And Social Distancing JONATHAN R. BRADLEY Thus, we introduce a reasonable easy-to-implement all-purpose method that converts a statistical model for continuous responses (the preferred model) into a Bayesian model for multi-response data sets. STATISTICS.arxiv 2020-02-23
409 Forecast Of The Evolution Of The Contagious Disease Caused By Novel Coronavirus (2019-nCoV) In China JAVIER GAMERO et. al. Though the danger of this epidemic is evident, the statistical analysis of data offered in this paper indicates that the increase in new cases in China will stabilize in the coming days or weeks. STATISTICS.arxiv 2020-02-11
410 Shapley Value Confidence Intervals For Attributing Variance Explained DANIEL FRYER et. al. Under a weak assumption that the joint distribution is pseudo-elliptical, we obtain the asymptotic normality of the Shapley values. STATISTICS.arxiv 2020-01-27
411 Quantifying The Trendiness Of Trends ANDREAS KRYGER JENSEN et. al. We propose two measures for quantifying the trendiness of trends. STATISTICS.arxiv 2019-12-26
412 Changes To The Extreme And Erratic Behaviour Of Cryptocurrencies During COVID-19 NICK JAMES et. al. This paper introduces new methods for analysing the extreme and erratic behaviour of time series to evaluate the impact of COVID-19 on cryptocurrency market dynamics. FINANCE.arxiv 2019-12-12
413 Probabilistic $K$-mean With Local Alignment For Clustering And Motif Discovery In Functional Data MARZIA A. CREMONA et. al. We develop a new method to locally cluster curves and discover functional motifs, i.e.~typical “shapes” that may recur several times along and across the curves capturing important local characteristics. STATISTICS.arxiv 2018-08-14
414 Estimation Of Temporal Covariances In Pathogen Dynamics Using Bayesian Multivariate Autoregressive Models COLETTE MAIR et. al. Here we developed a novel statistical framework to identify statistical covariances from the infection time-series of multiple pathogens simultaneously. STATISTICS.arxiv 2016-11-28