Quantitative analysis of the impact of various urban socioeconomic indicators on search-engine-based estimation of COVID-19 prevalence

Wang, Ligui; Lin, Mengxuan; Wang, Jiaojiao; Chen, Hui; Yang, Mingjuan; Qiu, Shaofu; Zheng, Tao; Li, Zhenjun; Song, Hongbin

doi:10.1016/j.idm.2022.04.003

Cited by 4 publications

(3 citation statements)

References 17 publications

(18 reference statements)

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“…In this work, we present a different approach, a simulation tool that follows the daily contact network history of each individual in a largely populated area, the province of Barcelona. A precise knowledge of the morphology of these networks has been shown to be of the greatest importance to capture the salient characteristics of the outbreak ( Chung & Chew, 2021 ; Small & Cavanagh, 2020 ; Wang et al, 2022 ). For each individual, we consider their corresponding microenvironment: home location and the co-residence structure, the employment situation and the mobility routine with its resulting pattern of contacts.…”

Section: Introductionmentioning

confidence: 99%

Stochastic simulation of successive waves of COVID-19 in the province of Barcelona

Bosman

Esteve

Gabbanelli

et al. 2023

Infectious Disease Modelling

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Section: Introductionmentioning

confidence: 99%

Stochastic simulation of successive waves of COVID-19 in the province of Barcelona

Bosman

Esteve

Gabbanelli

et al. 2023

Infectious Disease Modelling

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“…Naively, in the present era of abundant and widely available data, one could expect that most of these questions could be settled down by systematically comparing the COVID-19 numbers with various demographic and environmental parameters. However, while much progress in this direction has been made (e.g., (Adhikari and Yin, 2020; Allel et al, 2020; An et al, 2020; Gupta and Gharehgozli, 2020; Pan et al, 2020; Djordjevic et al, 2021b; Hradsky and Komarek, 2021; Lorenzo et al, 2021; Markovic et al, 2021; Perone, 2021; Rontos et al, 2021; Salom et al, 2021; Singh et al, 2021; Wang et al, 2022)), many methodological obstacles complicate this type of research and often lead to conflicting conclusions of otherwise similar studies.…”

Section: Introductionmentioning

confidence: 99%

A large-scale machine learning study of sociodemographic factors contributing to COVID-19 severity

Tumbas

Marković

Salom

et al. 2023

Preprint

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Understanding sociodemographic factors behind COVID-19 severity relates to significant methodological difficulties, such as differences in testing policies and epidemics phase, as well as a large number of predictors that can potentially contribute to severity. To account for these difficulties, we assemble 115 predictors for more than 3000 US counties and employ a well-defined COVID-19 severity measure derived from epidemiological dynamics modeling. We then use a number of advanced feature selection techniques from machine learning to determine which of these predictors significantly impact the disease severity. We obtain a surprisingly simple result, where only two variables are clearly and robustly selected - population density and proportion of African Americans. Possible causes behind this result are discussed. We argue that the approach may be useful whenever significant determinants of disease progression over diverse geographic regions should be selected from a large number of potentially important factors.

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“…Naively, in the present era of abundant and widely available data, one could expect that most of these questions could be settled down by systematically comparing the COVID-19 numbers with various demographic and environmental parameters. However, while much progress in this direction has been made (e.g., Adhikari and Yin, 2020;Allel et al, 2020;Gupta and Gharehgozli, 2020;Pan et al, 2020;Djordjevic et al, 2021b;Hradsky and Komarek, 2021;Lorenzo et al, 2021;Markovic et al, 2021;Perone, 2021;Rontos et al, 2021;Salom et al, 2021;Singh et al, 2021;Wang et al, 2022), many methodological obstacles complicate this type of research and often lead to conflicting conclusions of otherwise similar studies.…”

Section: Introductionmentioning

confidence: 99%

A large-scale machine learning study of sociodemographic factors contributing to COVID-19 severity

et al. 2023

View full text Add to dashboard Cite

Understanding sociodemographic factors behind COVID-19 severity relates to significant methodological difficulties, such as differences in testing policies and epidemics phase, as well as a large number of predictors that can potentially contribute to severity. To account for these difficulties, we assemble 115 predictors for more than 3,000 US counties and employ a well-defined COVID-19 severity measure derived from epidemiological dynamics modeling. We then use a number of advanced feature selection techniques from machine learning to determine which of these predictors significantly impact the disease severity. We obtain a surprisingly simple result, where only two variables are clearly and robustly selected—population density and proportion of African Americans. Possible causes behind this result are discussed. We argue that the approach may be useful whenever significant determinants of disease progression over diverse geographic regions should be selected from a large number of potentially important factors.

show abstract

Quantitative analysis of the impact of various urban socioeconomic indicators on search-engine-based estimation of COVID-19 prevalence

Cited by 4 publications

References 17 publications

Stochastic simulation of successive waves of COVID-19 in the province of Barcelona

Stochastic simulation of successive waves of COVID-19 in the province of Barcelona

A large-scale machine learning study of sociodemographic factors contributing to COVID-19 severity

A large-scale machine learning study of sociodemographic factors contributing to COVID-19 severity

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