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1. Statistical Analysis of Spatial Network Characteristics in Relation to COVID-19 Transmission Risks in US Counties

   https://doi.org/10.1109/EMBC46164.2021.9629892  

   Zhang, Siqi; Yang, Sihan; Yang, Hui (November 2021, 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC))

   Since the pandemic of COVID-19 began in January 2020, the world has witnessed drastic social-economic changes. To harness the virus spread, several studies have been done to study contributing factors that are pertinent to COVID-19 transmission risks. However, little has been done to investigate how human activities on the spatial network are correlated to the virus transmission and spread. This paper performs a statistical analysis to examine interrelationships between spatial network characteristics and cumulative cases of COVID-19 in US counties. Specifically, both county-level transportation profiles (e.g., the total number of commute workers, route miles of freight railroad) and road network characteristics of US counties are considered. Then, the lasso regression model is utilized to identify a sparse set of significant variables that are sensitive to the response variable of COVID-19 cases. Finally, the fixed-effect model is built to capture the relationship between the selected set of predictors and the response variable. This work helps identify and determine salient features from spatial network characteristics and transportation profiles, thereby improving the understanding of COVID-19 spread dynamics. These significant variables can also be utilized to develop simulation models for the prediction of real-time positions of virus spread and the optimization of intervention strategies. 

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2. Methods, Challenges, and Practical Issues of COVID-19 Projection: A Data Science Perspective

   https://doi.org/10.6339/21-JDS1013  

   Kim, Myungjin; Gu, Zhiling; Yu, Shan; Wang, Guannan; Wang, Li (April 2021, Journal of Data Science)

   null (Ed.)

   The coronavirus disease 2019 (COVID-19) pandemic caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has placed epidemic modeling at the center of attention of public policymaking. Predicting the severity and speed of transmission of COVID-19 is crucial to resource management and developing strategies to deal with this epidemic. Based on the available data from current and previous outbreaks, many efforts have been made to develop epidemiological models, including statistical models, computer simulations, mathematical representations of the virus and its impacts, and many more. Despite their usefulness, modeling and forecasting the spread of COVID-19 remains a challenge. In this article, we give an overview of the unique features and issues of COVID-19 data and how they impact epidemic modeling and projection. In addition, we illustrate how various models could be connected to each other. Moreover, we provide new data science perspectives on the challenges of COVID-19 forecasting, from data collection, curation, and validation to the limitations of models, as well as the uncertainty of the forecast. Finally, we discuss some data science practices that are crucial to more robust and accurate epidemic forecasting. 

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3. Importance of social inequalities to contact patterns, vaccine uptake, and epidemic dynamics

   https://doi.org/10.1038/s41467-024-48332-y  

   Manna, Adriana; Koltai, Júlia; Karsai, Márton (December 2024, Nature Communications)

   Abstract Individuals’ socio-demographic and economic characteristics crucially shape the spread of an epidemic by largely determining the exposure level to the virus and the severity of the disease for those who got infected. While the complex interplay between individual characteristics and epidemic dynamics is widely recognised, traditional mathematical models often overlook these factors. In this study, we examine two important aspects of human behaviour relevant to epidemics: contact patterns and vaccination uptake. Using data collected during the COVID-19 pandemic in Hungary, we first identify the dimensions along which individuals exhibit the greatest variation in their contact patterns and vaccination uptake. We find that generally higher socio-economic groups of the population have a higher number of contacts and a higher vaccination uptake with respect to disadvantaged groups. Subsequently, we propose a data-driven epidemiological model that incorporates these behavioural differences. Finally, we apply our model to analyse the fourth wave of COVID-19 in Hungary, providing valuable insights into real-world scenarios. By bridging the gap between individual characteristics and epidemic spread, our research contributes to a more comprehensive understanding of disease dynamics and informs effective public health strategies. 

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4. COVID-19 Screening Using Residual Attention Network an Artificial Intelligence Approach

   https://doi.org/10.1109/ICMLA51294.2020.00211  

   Sharma, Vishal; Dyreson, Curtis (December 2020, 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA))

   null (Ed.)

   Coronavirus Disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 virus (SARS-CoV-2). The virus transmits rapidly; it has a basic reproductive number (R0) of 2.2-2.7. In March 2020, the World Health Organization declared the COVID-19 outbreak a pandemic. COVID-19 is currently affecting more than 200 countries with 6M active cases. An effective testing strategy for COVID-19 is crucial to controlling the outbreak but the demand for testing surpasses the availability of test kits that use Reverse Transcription Polymerase Chain Reaction (RT-PCR). In this paper, we present a technique to screen for COVID-19 using artificial intelligence. Our technique takes only seconds to screen for the presence of the virus in a patient. 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. Unsatisfied with these models, we then designed and built a Residual Attention Network that was able to screen COVID-19 with a testing accuracy of 98% and a validation accuracy of 100%. A feature maps visual of our model show areas in a chest X-ray which are important for classification. Our work can help to increase the adaptation of AI-assisted applications in clinical practice. The code and dataset used in this project are available at https://github.com/vishalshar/covid-19-screening-using-RAN-on-X-ray-images. 

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5. Advancing infection profiling under data uncertainty through contagion potential

   https://doi.org/10.1371/journal.pone.0329828  

   Roy, Satyaki; Biswas, Preetom; Ghosh, Preetam (August 2025, PLOS One)

   Arunachalam, Viswanathan (Ed.)

   During the COVID-19 pandemic, the prevalence of asymptomatic cases challenged the reliability of epidemiological statistics in policymaking. To address this, we introducedcontagion potential(CP) as a continuous metric derived from sociodemographic and epidemiological data to quantify the infection risk posed by the asymptomatic within a region. However, CP estimation is hindered by incomplete or biased incidence data, where underreporting and testing constraints make direct estimation infeasible. To overcome this limitation, we employ a hypothesis-testing approach to infer CP from sampled data, allowing for robust estimation despite missing information. Even within the sample collected from spatial contact data, individuals possess partial knowledge of their neighborhoods, as their awareness is restricted to interactions captured by available tracking data. We introduce an adjustment factor that calibrates the sample CPs so that the sample is a reasonable estimate of the population CP. Further complicating estimation, biases in epidemiological and mobility data arise from heterogeneous reporting rates and sampling inconsistencies, which we address throughinverse probability weightingto enhance reliability. Using a spatial model for infection spread through social mixing and an optimization framework based on the SIRS epidemic model, we analyze real infection datasets from Italy, Germany, and Austria. Our findings demonstrate that statistical methods can achieve high-confidence CP estimates while accounting for variations in sample size, confidence level, mobility models, and viral strains. By assessing the effects of bias, social mixing, and sampling frequency, we propose statistical corrections to improve CP prediction accuracy. Finally, we discuss how reliable CP estimates can inform outbreak mitigation strategies despite the inherent uncertainties in epidemiological data. 

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