Explaining COVID-19 outbreaks with reactive SEIRD models

Abstract COVID-19 epidemics have varied dramatically in nature across the United States, where some counties have clear peaks in infections, and others have had a multitude of unpredictable and non-distinct peaks. Our lack of understanding of how the pandemic has evolved leads to increasing errors i...

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Autores principales:	Kunal Menda, Lucas Laird, Mykel J. Kochenderfer, Rajmonda S. Caceres
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4af6e01c390d4777963f86cf0d7b3f10
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spelling	oai:doaj.org-article:4af6e01c390d4777963f86cf0d7b3f102021-12-02T14:54:50ZExplaining COVID-19 outbreaks with reactive SEIRD models10.1038/s41598-021-97260-02045-2322https://doaj.org/article/4af6e01c390d4777963f86cf0d7b3f102021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97260-0https://doaj.org/toc/2045-2322Abstract COVID-19 epidemics have varied dramatically in nature across the United States, where some counties have clear peaks in infections, and others have had a multitude of unpredictable and non-distinct peaks. Our lack of understanding of how the pandemic has evolved leads to increasing errors in our ability to predict the spread of the disease. This work seeks to explain this diversity in epidemic progressions by considering an extension to the compartmental SEIRD model. The model we propose uses a neural network to predict the infection rate as a function of both time and the disease’s prevalence. We provide a methodology for fitting this model to available county-level data describing aggregate cases and deaths. Our method uses Expectation-Maximization to overcome the challenge of partial observability, due to the fact that the system’s state is only partially reflected in available data. We fit a single model to data from multiple counties in the United States exhibiting different behavior. By simulating the model, we show that it can exhibit both single peak and multi-peak behavior, reproducing behavior observed in counties both in and out of the training set. We then compare the error of simulations from our model with a standard SEIRD model, and show that ours substantially reduces errors. We also use simulated data to compare our methodology for handling partial observability with a standard approach, showing that ours is significantly better at estimating the values of unobserved quantities.Kunal MendaLucas LairdMykel J. KochenderferRajmonda S. CaceresNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kunal Menda Lucas Laird Mykel J. Kochenderfer Rajmonda S. Caceres Explaining COVID-19 outbreaks with reactive SEIRD models
description	Abstract COVID-19 epidemics have varied dramatically in nature across the United States, where some counties have clear peaks in infections, and others have had a multitude of unpredictable and non-distinct peaks. Our lack of understanding of how the pandemic has evolved leads to increasing errors in our ability to predict the spread of the disease. This work seeks to explain this diversity in epidemic progressions by considering an extension to the compartmental SEIRD model. The model we propose uses a neural network to predict the infection rate as a function of both time and the disease’s prevalence. We provide a methodology for fitting this model to available county-level data describing aggregate cases and deaths. Our method uses Expectation-Maximization to overcome the challenge of partial observability, due to the fact that the system’s state is only partially reflected in available data. We fit a single model to data from multiple counties in the United States exhibiting different behavior. By simulating the model, we show that it can exhibit both single peak and multi-peak behavior, reproducing behavior observed in counties both in and out of the training set. We then compare the error of simulations from our model with a standard SEIRD model, and show that ours substantially reduces errors. We also use simulated data to compare our methodology for handling partial observability with a standard approach, showing that ours is significantly better at estimating the values of unobserved quantities.
format	article
author	Kunal Menda Lucas Laird Mykel J. Kochenderfer Rajmonda S. Caceres
author_facet	Kunal Menda Lucas Laird Mykel J. Kochenderfer Rajmonda S. Caceres
author_sort	Kunal Menda
title	Explaining COVID-19 outbreaks with reactive SEIRD models
title_short	Explaining COVID-19 outbreaks with reactive SEIRD models
title_full	Explaining COVID-19 outbreaks with reactive SEIRD models
title_fullStr	Explaining COVID-19 outbreaks with reactive SEIRD models
title_full_unstemmed	Explaining COVID-19 outbreaks with reactive SEIRD models
title_sort	explaining covid-19 outbreaks with reactive seird models
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/4af6e01c390d4777963f86cf0d7b3f10
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Explaining COVID-19 outbreaks with reactive SEIRD models

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