Intracounty modeling of COVID-19 infection with human mobility: Assessing spatial heterogeneity with business traffic, age, and race

Abstract

The COVID-19 pandemic is a global threat presenting health, economic, and social challenges that continue to escalate. Metapopulation epidemic modeling studies in the susceptible–exposed–infectious–removed (SEIR) style have played important roles in informing public health policy making to mitigate the spread of COVID-19. These models typically rely on a key assumption on the homogeneity of the population. This assumption certainly cannot be expected to hold true in real situations; various geographic, socioeconomic, and cultural environments affect the behaviors that drive the spread of COVID-19 in different communities. What’s more, variation of intracounty environments creates spatial heterogeneity of transmission in different regions. To address this issue, we develop a human mobility flow-augmented stochastic SEIR-style epidemic modeling framework with the ability to distinguish different regions and their corresponding behaviors. This modeling framework is then combined with data assimilation and machine learning techniques to reconstruct the historical growth trajectories of COVID-19 confirmed cases in two counties in Wisconsin. The associations between the spread of COVID-19 and business foot traffic, race and ethnicity, and age structure are then investigated. The results reveal that, in a college town (Dane County), the most important heterogeneity is age structure, while, in a large city area (Milwaukee County), racial and ethnic heterogeneity becomes more apparent. Scenario studies further indicate a strong response of the spread rate to various reopening policies, which suggests that policy makers may need to take these heterogeneities into account very carefully when designing policies for mitigating the ongoing spread of COVID-19 and reopening.

Exploring temporal varying demographic and economic disparities in COVID-19 infections in four U.S. areas: based on OLS, GWR, and random forest models

“…Previous studies concluded that people of color had experienced heightened COVID-19 infections and more deaths on average than white individuals (APM Research Lab, 2021 ). Besides, spatial heterogeneities were significant in COVID-19 infections (Hou et al, 2021 ; Thomas et al, 2020 ). The neighborhoods where COVID-19 gathered were likely where the vulnerable population lived (Hong et al, 2021 ).…”

Risk Assessment and Prediction of COVID-19 Based on Epidemiological Data From Spatiotemporal Geography

“…Therefore, as China is the largest developing country in the world, studies on the transmission, modeling, and understanding of COVID-19 in China should explore the urban environmental factors that influence the distribution and transmission of COVID-19, taking into account urban public health resources. Such research is likely to be of great regional value (Hou et al, 2021).…”

A Multi-perspective Narrative-Based Geovisualization Dashboard for the 2020 US Presidential Election

“…Under the influence of the COVID-19 pandemic, the 2020 US presidential election was destined to be unusual. The spread of the coronavirus disease in the USA embodies significant spatial heterogeneity due to varying population density and structure, social economics, political attitude, and COVID-19 control policies in different places (Hou et al 2021 ; Sun et al 2020 ; Desmet and Wacziarg 2020 ), and partisan divides on state and local COVID-19 response policies have become even starker (Iyengar and Westwood 2015 ; Sides et al 2020 ). Research suggests that the COVID-19 pandemic may have caused potential political shifts (Johnson et al 2020 ), and the COVID-19 related health disparity issues have influenced the 2020 US election and presidential campaigns in multiple aspects (Norris and Gonzalez 2020 ).…”