ArticleScientific reports2021
Quantifying superspreading for COVID-19 using Poisson mixture distributions.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 34 citations in OpenAlex.
- Superspreading, overdispersion and their implications in the SARS-CoV-2 (COVID-19) pandemic: a systematic review and meta-analysis of the literature.BMC public health · 2023Pooled it
- Systematic review and meta-analyses of superspreading of SARS-CoV-2 infections.Transboundary and emerging diseases · 2022Pooled it
- Disentangling the drivers of heterogeneity in SARS-CoV-2 transmission from data on viral load and daily contact rates.PLoS computational biology · 2026Article
- Optimal pandemic control strategies and cost-effectiveness of COVID-19 non-pharmaceutical interventions in the United States.BMC global and public health · 2025Article
- Estimating Re and overdispersion in secondary cases from the size of identical sequence clusters of SARS-CoV-2.PLoS computational biology · 2025Article
- Forecasting the Monkeypox Outbreak Using Limited Data: A Case Study of Thailand.Interdisciplinary perspectives on infectious diseases · 2025Article
- Repetition in social contacts: implications in modelling the transmission of respiratory infectious diseases in pre-pandemic and pandemic settings.Proceedings. Biological sciences · 2024Article
- A beta-Poisson model for infectious disease transmission.PLoS computational biology · 2024Article
- Time series analysis and short-term forecasting of monkeypox outbreak trends in the 10 major affected countries.BMC infectious diseases · 2024Article
- Article
- Hypothesis testing of Poisson rates in COVID-19 offspring distributions.Infectious Disease Modelling · 2023Review
- Replicating superspreader dynamics with compartmental models.Scientific reports · 2023Article
- Quantifying individual-level heterogeneity in infectiousness and susceptibility through household studies.Epidemics · 2023Article
- Extreme value modelling of SARS-CoV-2 community transmission using discrete generalized Pareto distributions.Royal Society open science · 2023Article
- Hierarchical Bayesian spatio-temporal modeling of COVID-19 in the United States.Journal of applied statistics · 2023Article
- Quantifying individual-level heterogeneity in infectiousness and susceptibility through household studies.medRxiv : the preprint server for health sciences · 2022Article
- Impact of cross-border-associated cases on the SARS-CoV-2 epidemic in Switzerland during summer 2020 and 2021.Epidemics · 2022Article
- The role of super-spreaders in modeling of SARS-CoV-2.Infectious Disease Modelling · 2022Article
- Different forms of superspreading lead to different outcomes: Heterogeneity in infectiousness and contact behavior relevant for the case of SARS-CoV-2.PLoS computational biology · 2022Article
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Authors and funding
9 authors at 4 institutions in 3 countries.
Funding
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.