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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.
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Modeling Epidemic Spread among a Commuting Population Using Transport Schemes
Understanding the dynamics of the spread of COVID-19 between connected communities is fundamental in planning appropriate mitigation measures. To that end, we propose and analyze a novel metapopulation network model, particularly suitable for modeling commuter traffic patterns, that takes into account the connectivity between a heterogeneous set of communities, each with its own infection dynamics. In the novel metapopulation model that we propose here, transport schemes developed in optimal transport theory provide an efficient and easily implementable way of describing the temporary population redistribution due to traffic, such as the daily commuter traffic between work and residence. Locally, infection dynamics in individual communities are described in terms of a susceptible-exposed-infected-recovered (SEIR) compartment model, modified to account for the specific features of COVID-19, most notably its spread by asymptomatic and presymptomatic infected individuals. The mathematical foundation of our metapopulation network model is akin to a transport scheme between two population distributions, namely the residential distribution and the workplace distribution, whose interface can be inferred from commuter mobility data made available by the US Census Bureau. We use the proposed metapopulation model to test the dynamics of the spread of COVID-19 on two networks, a smaller one comprising 7 counties in the Greater Cleveland area in Ohio, and a larger one consisting of 74 counties in the Pittsburgh–Cleveland–Detroit corridor following the Lake Erie’s American coastline. The model simulations indicate that densely populated regions effectively act as amplifiers of the infection for the surrounding, less densely populated areas, in agreement with the pattern of infections observed in the course of the COVID-19 pandemic. Computed examples show that the model can be used also to test different mitigation strategies, including one based on state-level travel restrictions, another on county level triggered social distancing, as well as a combination of the two.
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- PAR ID:
- 10292749
- Date Published:
- Journal Name:
- Mathematics
- Volume:
- 9
- Issue:
- 16
- ISSN:
- 2227-7390
- Page Range / eLocation ID:
- 1861
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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null (Ed.)The COVID-19 pandemic severely changed the way of life in the United States (US). From early scattered regional outbreaks to current country-wide spread, and from rural areas to highly populated cities, the contagion exhibits diverse patterns at various timescales and locations. We thus conduct a graph frequency analysis to inves- tigate the spread patterns of COVID-19 in different US counties. The commute flows between all 3142 US counties were used to construct a graph capturing the population mobility. The numbers of daily confirmed COVID-19 cases per county were collected and represented as graph signals, which were then mapped into the frequency domain via the graph Fourier transform. The concept of graph frequency in Graph Signal Processing (GSP) enables the decomposition of graph signals (i.e., daily confirmed cases) into modes with smooth or rapid variations with respect to the underlying mobility graph. These different modes of variability are shown to relate to COVID-19 spread patterns within and across counties. Changes in the nature of spread within geographical regions are also revealed by graph frequency analysis at finer temporal scales. Overall, our GSP-based approach leverages case count and mobility data to unveil spatio-temporal contagion patterns of COVID-19 incidence for each US county. Results here support the promising prospect of using GSP tools for epidemiology knowledge discovery on graphs.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/math9161861
