Lies, Gosh Darn Lies, and not enough good statistics: why epidemic model parameter estimation fails

Abstract We sought to investigate whether epidemiological parameters that define epidemic models could be determined from the epidemic trajectory of infections, recovery, and hospitalizations prior to peak, and also to evaluate the comparability of data between jurisdictions reporting their statisti...

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Autores principales:	Daniel E. Platt, Laxmi Parida, Pierre Zalloua
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e965e9fa0f8d4353acfdd8ea9a6694ea
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spelling	oai:doaj.org-article:e965e9fa0f8d4353acfdd8ea9a6694ea2021-12-02T14:01:38ZLies, Gosh Darn Lies, and not enough good statistics: why epidemic model parameter estimation fails10.1038/s41598-020-79745-62045-2322https://doaj.org/article/e965e9fa0f8d4353acfdd8ea9a6694ea2021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-79745-6https://doaj.org/toc/2045-2322Abstract We sought to investigate whether epidemiological parameters that define epidemic models could be determined from the epidemic trajectory of infections, recovery, and hospitalizations prior to peak, and also to evaluate the comparability of data between jurisdictions reporting their statistics. We found that, analytically, the pre-peak growth of an epidemic underdetermines the model variates, and that the rate limiting variables are dominated by the exponentially expanding eigenmode of their equations. The variates quickly converge to the ratio of eigenvector components of the positive growth mode, which determines the doubling time. Without a sound epidemiological study framework, measurements of infection rates and other parameters are highly corrupted by uneven testing rates, uneven counting, and under reporting of relevant values. We argue that structured experiments must be performed to estimate these parameters in order to perform genetic association studies, or to construct viable models accurately predicting critical quantities such as hospitalization loads.Daniel E. PlattLaxmi ParidaPierre ZallouaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Daniel E. Platt Laxmi Parida Pierre Zalloua Lies, Gosh Darn Lies, and not enough good statistics: why epidemic model parameter estimation fails
description	Abstract We sought to investigate whether epidemiological parameters that define epidemic models could be determined from the epidemic trajectory of infections, recovery, and hospitalizations prior to peak, and also to evaluate the comparability of data between jurisdictions reporting their statistics. We found that, analytically, the pre-peak growth of an epidemic underdetermines the model variates, and that the rate limiting variables are dominated by the exponentially expanding eigenmode of their equations. The variates quickly converge to the ratio of eigenvector components of the positive growth mode, which determines the doubling time. Without a sound epidemiological study framework, measurements of infection rates and other parameters are highly corrupted by uneven testing rates, uneven counting, and under reporting of relevant values. We argue that structured experiments must be performed to estimate these parameters in order to perform genetic association studies, or to construct viable models accurately predicting critical quantities such as hospitalization loads.
format	article
author	Daniel E. Platt Laxmi Parida Pierre Zalloua
author_facet	Daniel E. Platt Laxmi Parida Pierre Zalloua
author_sort	Daniel E. Platt
title	Lies, Gosh Darn Lies, and not enough good statistics: why epidemic model parameter estimation fails
title_short	Lies, Gosh Darn Lies, and not enough good statistics: why epidemic model parameter estimation fails
title_full	Lies, Gosh Darn Lies, and not enough good statistics: why epidemic model parameter estimation fails
title_fullStr	Lies, Gosh Darn Lies, and not enough good statistics: why epidemic model parameter estimation fails
title_full_unstemmed	Lies, Gosh Darn Lies, and not enough good statistics: why epidemic model parameter estimation fails
title_sort	lies, gosh darn lies, and not enough good statistics: why epidemic model parameter estimation fails
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/e965e9fa0f8d4353acfdd8ea9a6694ea
work_keys_str_mv	AT danieleplatt liesgoshdarnliesandnotenoughgoodstatisticswhyepidemicmodelparameterestimationfails AT laxmiparida liesgoshdarnliesandnotenoughgoodstatisticswhyepidemicmodelparameterestimationfails AT pierrezalloua liesgoshdarnliesandnotenoughgoodstatisticswhyepidemicmodelparameterestimationfails
_version_	1718392115589808128

Lies, Gosh Darn Lies, and not enough good statistics: why epidemic model parameter estimation fails

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