Quantifying superspreading for COVID-19 using Poisson mixture distributions

Abstract The number of secondary cases, i.e. the number of new infections generated by an infectious individual, is an important parameter for the control of infectious diseases. When individual variation in disease transmission is present, like for COVID-19, the distribution of the number of second...

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Autores principales:	Cécile Kremer, Andrea Torneri, Sien Boesmans, Hanne Meuwissen, Selina Verdonschot, Koen Vanden Driessche, Christian L. Althaus, Christel Faes, Niel Hens
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e9263460cb9b4dd58168af534d42b02c
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spelling	oai:doaj.org-article:e9263460cb9b4dd58168af534d42b02c2021-12-02T15:40:00ZQuantifying superspreading for COVID-19 using Poisson mixture distributions10.1038/s41598-021-93578-x2045-2322https://doaj.org/article/e9263460cb9b4dd58168af534d42b02c2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93578-xhttps://doaj.org/toc/2045-2322Abstract The number of secondary cases, i.e. the number of new infections generated by an infectious individual, is an important parameter for the control of infectious diseases. When individual variation in disease transmission is present, like for COVID-19, the distribution of the number of secondary cases is skewed and often modeled using a negative binomial distribution. However, this may not always be the best distribution to describe the underlying transmission process. We propose the use of three other offspring distributions to quantify heterogeneity in transmission, and we assess the possible bias in estimates of the mean and variance of this distribution when the data generating distribution is different from the one used for inference. We also analyze COVID-19 data from Hong Kong, India, and Rwanda, and quantify the proportion of cases responsible for 80% of transmission, $$p_{80\%}$$ p 80 % , while acknowledging the variation arising from the assumed offspring distribution. In a simulation study, we find that variance estimates may be biased when there is a substantial amount of heterogeneity, and that selection of the most accurate distribution from a set of distributions is important. In addition we find that the number of secondary cases for two of the three COVID-19 datasets is better described by a Poisson-lognormal distribution.Cécile KremerAndrea TorneriSien BoesmansHanne MeuwissenSelina VerdonschotKoen Vanden DriesscheChristian L. AlthausChristel FaesNiel HensNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Cécile Kremer Andrea Torneri Sien Boesmans Hanne Meuwissen Selina Verdonschot Koen Vanden Driessche Christian L. Althaus Christel Faes Niel Hens Quantifying superspreading for COVID-19 using Poisson mixture distributions
description	Abstract The number of secondary cases, i.e. the number of new infections generated by an infectious individual, is an important parameter for the control of infectious diseases. When individual variation in disease transmission is present, like for COVID-19, the distribution of the number of secondary cases is skewed and often modeled using a negative binomial distribution. However, this may not always be the best distribution to describe the underlying transmission process. We propose the use of three other offspring distributions to quantify heterogeneity in transmission, and we assess the possible bias in estimates of the mean and variance of this distribution when the data generating distribution is different from the one used for inference. We also analyze COVID-19 data from Hong Kong, India, and Rwanda, and quantify the proportion of cases responsible for 80% of transmission, $$p_{80\%}$$ p 80 % , while acknowledging the variation arising from the assumed offspring distribution. In a simulation study, we find that variance estimates may be biased when there is a substantial amount of heterogeneity, and that selection of the most accurate distribution from a set of distributions is important. In addition we find that the number of secondary cases for two of the three COVID-19 datasets is better described by a Poisson-lognormal distribution.
format	article
author	Cécile Kremer Andrea Torneri Sien Boesmans Hanne Meuwissen Selina Verdonschot Koen Vanden Driessche Christian L. Althaus Christel Faes Niel Hens
author_facet	Cécile Kremer Andrea Torneri Sien Boesmans Hanne Meuwissen Selina Verdonschot Koen Vanden Driessche Christian L. Althaus Christel Faes Niel Hens
author_sort	Cécile Kremer
title	Quantifying superspreading for COVID-19 using Poisson mixture distributions
title_short	Quantifying superspreading for COVID-19 using Poisson mixture distributions
title_full	Quantifying superspreading for COVID-19 using Poisson mixture distributions
title_fullStr	Quantifying superspreading for COVID-19 using Poisson mixture distributions
title_full_unstemmed	Quantifying superspreading for COVID-19 using Poisson mixture distributions
title_sort	quantifying superspreading for covid-19 using poisson mixture distributions
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/e9263460cb9b4dd58168af534d42b02c
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Quantifying superspreading for COVID-19 using Poisson mixture distributions

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