Spatial-Temporal Relationship Between Population Mobility and COVID-19 Outbreaks in South Carolina: Time Series Forecasting Analysis (Preprint)

Zeng, Chengbo; Zhang, Jiajia; Li, Zhenlong; Sun, Xiaowen; Olatosi, Bankole; Weissman, Sharon; Li, Xiaoming

doi:10.2196/preprints.27045

Cited by 3 publications

(1 citation statement)

References 26 publications

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“…The authors demonstrated a case study in a clusters counties of Arizona. In [57], the authors applied the Poisson time series model to explore the connection between population mobility and COVID-19 daily cases. The model was simultaneously applied at the county level and state level in South Carolina.…”

Section: Mobility and Epidemiologymentioning

confidence: 99%

A linear dynamical perspective on epidemiology: interplay between early COVID-19 outbreak and human mobility

et al. 2022

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This paper investigates the impact of human activity and mobility (HAM) in the spreading dynamics of an epidemic. Specifically, it explores the interconnections between HAM and its effect on the early spread of the COVID-19 virus. During the early stages of the pandemic, effective reproduction numbers exhibited a high correlation with human mobility patterns, leading to a hypothesis that the HAM system can be studied as a coupled system with disease spread dynamics. This study applies the generalized Koopman framework with control inputs to determine the nonlinear disease spread dynamics and the input-output characteristics as a locally linear controlled dynamical system. The approach solely relies on the snapshots of spatiotemporal data and does not require any knowledge of the system's underlying physical laws. We exploit the Koopman operator framework by utilizing the Hankel dynamic mode decomposition with Control

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Section: Mobility and Epidemiologymentioning

confidence: 99%

A linear dynamical perspective on epidemiology: interplay between early COVID-19 outbreak and human mobility

et al. 2022

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Identifying the Socioeconomic, Demographic, and Political Determinants of Social Mobility and Their Effects on COVID-19 Cases and Deaths: Evidence From US Counties

Jalali¹,

Tran²,

Sen³

et al. 2022

JMIR Infodemiology

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Background The spread of COVID-19 at the local level is significantly impacted by population mobility. The U.S. has had extremely high per capita COVID-19 case and death rates. Efficient nonpharmaceutical interventions to control the spread of COVID-19 depend on our understanding of the determinants of public mobility. Objective This study used publicly available Google data and machine learning to investigate population mobility across a sample of US counties. Statistical analysis was used to examine the socioeconomic, demographic, and political determinants of mobility and the corresponding patterns of per capita COVID-19 case and death rates. Methods Daily Google population mobility data for 1085 US counties from March 1 to December 31, 2020, were clustered based on differences in mobility patterns using K-means clustering methods. Social mobility indicators (retail, grocery and pharmacy, workplace, and residence) were compared across clusters. Statistical differences in socioeconomic, demographic, and political variables between clusters were explored to identify determinants of mobility. Clusters were matched with daily per capita COVID-19 cases and deaths. Results Our results grouped US counties into 4 Google mobility clusters. Clusters with more population mobility had a higher percentage of the population aged 65 years and over, a greater population share of Whites with less than high school and college education, a larger percentage of the population with less than a college education, a lower percentage of the population using public transit to work, and a smaller share of voters who voted for Clinton during the 2016 presidential election. Furthermore, clusters with greater population mobility experienced a sharp increase in per capita COVID-19 case and death rates from November to December 2020. Conclusions Republican-leaning counties that are characterized by certain demographic characteristics had higher increases in social mobility and ultimately experienced a more significant incidence of COVID-19 during the latter part of 2020.

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Identifying the Socioeconomic, Demographic, and Political Determinants of Social Mobility and Their Effects on COVID-19 Cases and Deaths: Evidence From US Counties (Preprint)

Jalali¹,

Tran²,

Sen³

et al. 2021

Preprint

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BACKGROUND The spread of COVID-19 at the local level is significantly impacted by population mobility. The U.S. has had extremely high per capita COVID-19 case and death rates. Efficient non-pharmaceutical interventions to control the spread of COVID-19 depend on our understanding of the determinants of public mobility. OBJECTIVE This study used social media data and machine learning to investigate population mobility across a sample of U.S. counties. Statistical analysis was used to examine the socioeconomic, demographic, and political determinants of mobility and the corresponding patterns of per capita COVID-19 case and death rates. METHODS Daily Google population mobility data for 1,085 U.S. counties from March 1st, 2020 to December 31st, 2020 were clustered based on differences in mobility patterns using K-means clustering methods. Social mobility indicators (retail, grocery and pharmacy, workplace, and residence) were compared across clusters. Statistical differences in socioeconomic, demographic, and political variables between clusters were explored to identify determinants of mobility. Clusters were matched with daily per capita COVID-19 cases and deaths. RESULTS Our results grouped U.S. counties into four mobility clusters. Clusters with higher population mobility had a higher percentage of the population aged 65 and over, a higher percentage of Whites with less than high school and college education, a larger percentage of the population with less than a college education, a smaller share of the population that is Hispanic, a lower percentage of the population using public transit to work, and a smaller share of voters who voted for Clinton during the 2016 Presidential Election. Furthermore, those clusters with greater social mobility experienced a sharp increase in per capita COVID-19 case and death rates from October to December 2020. CONCLUSIONS These results emphasize the importance of using Google data and machine learning methods in public health data to support the identification of underlying determinants of social mobility patterns and associated COVID-19 cases.

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Spatial-Temporal Relationship Between Population Mobility and COVID-19 Outbreaks in South Carolina: Time Series Forecasting Analysis (Preprint)

Cited by 3 publications

References 26 publications

A linear dynamical perspective on epidemiology: interplay between early COVID-19 outbreak and human mobility

A linear dynamical perspective on epidemiology: interplay between early COVID-19 outbreak and human mobility

Identifying the Socioeconomic, Demographic, and Political Determinants of Social Mobility and Their Effects on COVID-19 Cases and Deaths: Evidence From US Counties

Identifying the Socioeconomic, Demographic, and Political Determinants of Social Mobility and Their Effects on COVID-19 Cases and Deaths: Evidence From US Counties (Preprint)

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