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Adding Space to Disease Models: A Case Study with COVID-19 in Oregon, USA

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  • Nathan H. Schumaker

    (Department of Fisheries and Wildlife, Oregon State University Corvallis, Corvallis, OR 97331, USA)

  • Sydney M. Watkins

    (Computational Ecology Group, Canmore, AB T1W 3L4, Canada)

Abstract

We selected the COVID-19 outbreak in the state of Oregon, USA as a system for developing a general geographically nuanced epidemiological forecasting model that balances simplicity, realism, and accessibility. Using the life history simulator HexSim, we inserted a mathematical SIRD disease model into a spatially explicit framework, creating a distributed array of linked compartment models. Our spatial model introduced few additional parameters, but casting the SIRD equations into a geographic setting significantly altered the system’s emergent dynamics. Relative to the non-spatial model, our simple spatial model better replicated the record of observed infection rates in Oregon. We also observed that estimates of vaccination efficacy drawn from the non-spatial model tended to be higher than those obtained from models that incorporate geographic variation. Our spatially explicit SIRD simulations of COVID-19 in Oregon suggest that modest additions of spatial complexity can bring considerable realism to a traditional disease model.

Suggested Citation

  • Nathan H. Schumaker & Sydney M. Watkins, 2021. "Adding Space to Disease Models: A Case Study with COVID-19 in Oregon, USA," Land, MDPI, vol. 10(4), pages 1-13, April.
    • Handle: RePEc:gam:jlands:v:10:y:2021:i:4:p:438-:d:539589
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    References listed on IDEAS

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    Cited by:

    1. Nathan H. Schumaker & Sydney M. Watkins & Julie A. Heinrichs, 2022. "HexFire: A Flexible and Accessible Wildfire Simulator," Land, MDPI, vol. 11(8), pages 1-16, August.

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