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A sliding window approach to optimize the time-varying parameters of a spatially-explicit and stochastic model of COVID-19

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  • Saikanth Ratnavale
  • Crystal Hepp
  • Eck Doerry
  • Joseph R Mihaljevic

Abstract

The implementation of non-pharmaceutical public health interventions can have simultaneous impacts on pathogen transmission rates as well as host mobility rates. For instance, with SARS-CoV-2, masking can influence host-to-host transmission, while stay-at-home orders can influence mobility. Importantly, variations in transmission rates and mobility patterns can influence pathogen-induced hospitalization rates. This poses a significant challenge for the use of mathematical models of disease dynamics in forecasting the spread of a pathogen; to create accurate forecasts in spatial models of disease spread, we must simultaneously account for time-varying rates of transmission and host movement. In this study, we develop a statistical model-fitting algorithm to estimate dynamic rates of SARS-CoV-2 transmission and host movement from geo-referenced hospitalization data. Using simulated data sets, we then test whether our method can accurately estimate these time-varying rates simultaneously, and how this accuracy is influenced by the spatial population structure. Our model-fitting method relies on a highly parallelized process of grid search and a sliding window technique that allows us to estimate time-varying transmission rates with high accuracy and precision, as well as movement rates with somewhat lower precision. Estimated parameters also had lower precision in more rural data sets, due to lower hospitalization rates (i.e., these areas are less data-rich). This model-fitting routine could easily be generalized to any stochastic, spatially-explicit modeling framework, offering a flexible and efficient method to estimate time-varying parameters from geo-referenced data sets.

Suggested Citation

  • Saikanth Ratnavale & Crystal Hepp & Eck Doerry & Joseph R Mihaljevic, 2022. "A sliding window approach to optimize the time-varying parameters of a spatially-explicit and stochastic model of COVID-19," PLOS Global Public Health, Public Library of Science, vol. 2(9), pages 1-15, September.
    • Handle: RePEc:plo:pgph00:0001058
    DOI: 10.1371/journal.pgph.0001058
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    References listed on IDEAS

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    1. Fernández-Villaverde, Jesús & Arias, Jonas & Rubio-Ramírez, Juan Francisco & Shin, Minchul, 2021. "Bayesian Estimation of Epidemiological Models: Methods, Causality, and Policy Trade-Offs," CEPR Discussion Papers 15951, C.E.P.R. Discussion Papers.
    2. Jonas E. Arias & Jesús Fernández-Villaverde & Juan F. Rubio-Ramirez & Minchul Shin, 2021. "Bayesian Estimation of Epidemiological Models: Methods, Causality, and Policy Trade-Offs," Working Papers 21-18, Federal Reserve Bank of Philadelphia.
    3. Per Block & Marion Hoffman & Isabel J. Raabe & Jennifer Beam Dowd & Charles Rahal & Ridhi Kashyap & Melinda C. Mills, 2020. "Social network-based distancing strategies to flatten the COVID-19 curve in a post-lockdown world," Nature Human Behaviour, Nature, vol. 4(6), pages 588-596, June.
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