Forecasting Multi-Wave Epidemics Through Bayesian Inference

Blonigan, Patrick; Ray, Jaideep; Safta, Cosmin

doi:10.1007/s11831-021-09603-9

Forecasting Multi-Wave Epidemics Through Bayesian Inference

Journal Article · Wed Jul 28 00:00:00 EDT 2021 · Archives of Computational Methods in Engineering

DOI:https://doi.org/10.1007/s11831-021-09603-9· OSTI ID:1810763

Blonigan, Patrick ^[1]; Ray, Jaideep ^[1]; Safta, Cosmin ^[1]

Sandia National Lab. (SNL-CA), Livermore, CA (United States)

We present a simple, near-real-time Bayesian method to infer and forecast a multiwave outbreak, and demonstrate it on the COVID-19 pandemic. The approach uses timely epidemiological data that has been widely available for COVID-19. It provides short-term forecasts of the outbreak’s evolution, which can then be used for medical resource planning. The method postulates one- and multiwave infection models, which are convolved with the incubation-period distribution to yield competing disease models. The disease models’ parameters are estimated via Markov chain Monte Carlo sampling and information-theoretic criteria are used to select between them for use in forecasting. The method is demonstrated on two- and three-wave COVID-19 outbreaks in California, New Mexico and Florida, as observed during Summer-Winter 2020. We find that the method is robust to noise, provides useful forecasts (along with uncertainty bounds) and that it reliably detected when the initial single-wave COVID-19 outbreaks transformed into successive surges as containment efforts in these states failed by the end of Spring 2020.

Research Organization:: Sandia National Laboratories (SNL-CA), Livermore, CA (United States)

Sponsoring Organization:: Coronavirus CARES Act; USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC). National Virtual Biotechnology Laboratory

Grant/Contract Number:: AC04-94AL85000; NA0003525

OSTI ID:: 1810763

Report Number(s):: SAND--2021-5198J; 695831

Journal Information:: Archives of Computational Methods in Engineering, Journal Name: Archives of Computational Methods in Engineering Journal Issue: 6 Vol. 28; ISSN 1134-3060

Publisher:: Springer NatureCopyright Statement

Country of Publication:: United States

Language:: English

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59 BASIC BIOLOGICAL SCIENCES
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