Research Portal

July 1, 2022

What County-level Factors Influence Covid-19 Incidence in the United States? Findings from the First Wave of the Pandemic

Wang, Lan; Zhang, Surong; Yang, Zilin; Zhao, Ziyu; Moudon, Anne Vernez; Feng, Huasen; Liang, Junhao; Sun, Wenyao; Cao, Buyang. (2021). What County-level Factors Influence Covid-19 Incidence in the United States? Findings from the First Wave of the Pandemic. Cities, 118.

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Effective control of the COVID-19 pandemic via appropriate management of the built environment is an urgent issue. This study develops a research framework to explore the relationship between COVID-19 incidence and influential factors related to protection of vulnerable populations, intervention in transmission pathways, and provision of healthcare resources. Relevant data for regression analysis and structural equation modeling is collected during the first wave of the pandemic in the United States, from counties with over 100 confirmed cases. In addition to confirming certain factors found in the existing literature, we uncover six new factors significantly associated with COVID-19 incidence. Furthermore, incidence during the lockdown is found to significantly affect incidence after the reopening, highlighting that timely quarantining and treating of patients is essential to avoid the snowballing transmission over time. These findings suggest ways to mitigate the negative effects of subsequent waves of the pandemic, such as special attention of infection prevention in neighborhoods with unsanitary and overcrowded housing, minimization of social activities organized by neighborhood associations, and contactless home delivery service of healthy food. Also worth noting is the need to provide support to people less capable of complying with the stay-at-home order because of their occupations or socio-economic disadvantage.


Pandemics; Covid-19; Covid-19 Pandemic; Infection Prevention; Stay-at-home Orders; Structural Equation Modeling; United States; Communicable Disease Prevention; Influential Factors; Lockdown; Structural Equation Modeling (sem); Prevalence; Disease; Healthy Food; Social Activities; Counties; Neighborhoods; Housing; Built Environment; Prevention; Minimization; Socioeconomic Factors; Intervention; Health Care; Vulnerability; Occupations; Coronaviruses; Food Service; Disease Transmission; United States--us