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Quantifying superspreading for COVID-19 using Poisson mixture distributions.
Kremer, Cécile; Torneri, Andrea; Boesmans, Sien; Meuwissen, Hanne; Verdonschot, Selina; Vanden Driessche, Koen; Althaus, Christian L; Faes, Christel; Hens, Niel.
  • Kremer C; Interuniversity Institute for Biostatistics and statistical Bioinformatics, Data Science Institute, Hasselt University, Hasselt, Belgium. cecile.kremer@uhasselt.be.
  • Torneri A; Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium.
  • Boesmans S; Faculty of Sciences, Hasselt University, Hasselt, Belgium.
  • Meuwissen H; Faculty of Sciences, Hasselt University, Hasselt, Belgium.
  • Verdonschot S; Faculty of Sciences, Hasselt University, Hasselt, Belgium.
  • Vanden Driessche K; Division of Pulmonology, Department of Pediatrics, Antwerp University Hospital, Edegem, Belgium.
  • Althaus CL; Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, The Netherlands.
  • Faes C; Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.
  • Hens N; Interuniversity Institute for Biostatistics and statistical Bioinformatics, Data Science Institute, Hasselt University, Hasselt, Belgium.
Sci Rep ; 11(1): 14107, 2021 07 08.
Article in English | MEDLINE | ID: covidwho-1303788
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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, [Formula see text], 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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Infectious Disease Transmission, Vertical / SARS-CoV-2 / COVID-19 Type of study: Observational study Limits: Humans Country/Region as subject: Africa / Asia Language: English Journal: Sci Rep Year: 2021 Document Type: Article Affiliation country: S41598-021-93578-x

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Infectious Disease Transmission, Vertical / SARS-CoV-2 / COVID-19 Type of study: Observational study Limits: Humans Country/Region as subject: Africa / Asia Language: English Journal: Sci Rep Year: 2021 Document Type: Article Affiliation country: S41598-021-93578-x