SynthesisTransboundary and emerging diseases2022
Systematic review and meta-analyses of superspreading of SARS-CoV-2 infections.
Synthesis in Transboundary and emerging diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
16 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Superspreading of SARS-CoV-2: a systematic review and meta-analysis of event attack rates and individual transmission patterns.Epidemiology and infection · 2024Pooled it
- 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
- Reconstructing the early spatial spread of pandemic respiratory viruses in the United States.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- A Bayesian modelling framework with model comparison for epidemics with super-spreading.Infectious Disease Modelling · 2025Article
- Reconstructing the early spatial spread of pandemic respiratory viruses in the United States.medRxiv : the preprint server for health sciences · 2025Article
- Inferring pathogen superspreading potential using early spatial spread patterns.medRxiv : the preprint server for health sciences · 2025Article
- Analysis of the prevalence, spread, and mutation patterns of SARS-CoV-2 variants: data from COVID-19 testing expansion strategy study in salvador, Northeast Brazil.Scientific reports · 2025Article
- Integrating Contact Tracing Data to Enhance Outbreak Phylodynamic Inference: A Deep Learning Approach.Molecular biology and evolution · 2024Article
- Estimating the reproduction number and transmission heterogeneity from the size distribution of clusters of identical pathogen sequences.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Article
- Genetic Predisposition to SARS-CoV-2 Infection: Cytokine Polymorphism and Disease Transmission within Households.Biology · 2023Article
- Resurgence of Omicron BA.2 in SARS-CoV-2 infection-naive Hong Kong.Nature communications · 2023Article
- Exploring the role of superspreading events in SARS-CoV-2 outbreaks.Journal of theoretical biology · 2023Article
- Characterizing Human Collective Behaviors During COVID-19 - Hong Kong SAR, China, 2020.China CDC weekly · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Superspreading, or overdispersion in transmission, is a feature of SARS-CoV-2 transmission which results in surging epidemics and large clusters of infection. The dispersion parameter is a statistical parameter used to characterize and quantify heterogeneity. In the context of measuring transmissibility, it is analogous to measures of superspreading potential among populations by assuming that collective offspring distribution follows a negative-binomial distribution. We conducted a systematic review and meta-analysis on globally reported dispersion parameters of SARS-CoV-2 infection. All searches were carried out on 10 September 2021 in PubMed for articles published from 1 January 2020 to 10 September 2021. Multiple estimates of the dispersion parameter have been published for 17 studies, which could be related to where and when the data were obtained, in 8 countries (e.g. China, the United States, India, Indonesia, Israel, Japan, New Zealand and Singapore). High heterogeneity was reported among the included studies. The mean estimates of dispersion parameters range from 0.06 to 2.97 over eight countries, the pooled estimate was 0.55 (95% CI: 0.30, 0.79), with changing means over countries and decreasing slightly with the increasing reproduction number. The expected proportion of cases accounting for 80% of all transmissions is 19% (95% CrI: 7, 34) globally. The study location and method were found to be important drivers for diversity in estimates of dispersion parameters. While under high potential of superspreading, larger outbreaks could still occur with the import of the COVID-19 virus by traveling even when an epidemic seems to be under control.
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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.