ArticleThe Lancet. Infectious diseases2020
Early dynamics of transmission and control of COVID-19: a mathematical modelling study.
Article in The Lancet. Infectious diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1,129 papers, 3 of them syntheses that pooled it.
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Who cites it
1,129 citing papers in PubMed, 3 syntheses or guidelines pooled it, 2,643 citations in OpenAlex.
- Association Between Gut Microbiota and Pneumonia Risk: A Systematic Review and Mendelian Randomization.International journal of medical sciences · 2025Pooled it
- Modelling the impact of behavioural interventions during pandemics: A systematic review.PloS one · 2025Pooled it
- The methodologies to assess the effectiveness of non-pharmaceutical interventions during COVID-19: a systematic review.European journal of epidemiology · 2022Pooled it
- Acute respiratory infection (COVID-19) risk prediction in travelers: A random forest model.Infectious Disease Modelling · 2026Article
- The impact of China's non-pharmaceutical interventions on economic growth during COVID-19: a provincial-level panel analysis.BMC public health · 2026Article
- Machine learning and probabilistic approaches for forecasting infectious disease transmission and cases.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2026Article
- Integrating Kolmogorov-Arnold networks with ordinary differential equations for efficient, interpretable, and robust deep learning: Epidemiology of infectious diseases as a case study.Infectious Disease Modelling · 2026Article
- Mechanistic Understanding of Pandemic Dynamics: A Multiscale Algorithmic Framework.Life (Basel, Switzerland) · 2026Article
- Escalating transmissibility of severe fever with thrombocytopenia syndrome in a high-endemic region of China.Nature communications · 2026Article
- Modeling the COVID-19 epidemic in kurdistan province using the SEIR model and time series analysis of hospitalized patients during the first wave.BMC infectious diseases · 2026Observational
- The role of receptor binding and immunity in SARS-CoV-2 fitness landscape: A modeling study.iScience · 2026Article
- A compartmental model of variant coexistence, dynamics and dominance in infectious diseases: Case for SARS-CoV-2 in Abu Dhabi.Infectious Disease Modelling · 2026Article
- A global twitter sentiment analysis model for COVID-vaccination.Scientific reports · 2026Article
- Real-time spatiotemporal tracking of infectious outbreaks in confined environments with a host-pathogen agent-based system.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Accelerated inference for stochastic compartmental models with over-dispersed partial observations.Statistics and computing · 2026Article
- Article
- Review
- Social, mobility and contact networks in shaping health behaviours and infectious disease dynamics: a scoping review.Infectious diseases of poverty · 2025Article
- China's post-zero-COVID Omicron wave: A Bayesian analysis.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- A discrete SIR epidemic model incorporating media impact, resource limitaions and threshold switching strategies.Infectious Disease Modelling · 2025Article
1,069 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
backgroundAn outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to 95 333 confirmed cases as of March 5, 2020. Understanding the early transmission dynamics of the infection and evaluating the effectiveness of control measures is crucial for assessing the potential for sustained transmission to occur in new areas. Combining a mathematical model of severe SARS-CoV-2 transmission with four datasets from within and outside Wuhan, we estimated how transmission in Wuhan varied between December, 2019, and February, 2020. We used these estimates to assess the potential for sustained human-to-human transmission to occur in locations outside Wuhan if cases were introduced.
methodsWe combined a stochastic transmission model with data on cases of coronavirus disease 2019 (COVID-19) in Wuhan and international cases that originated in Wuhan to estimate how transmission had varied over time during January, 2020, and February, 2020. Based on these estimates, we then calculated the probability that newly introduced cases might generate outbreaks in other areas. To estimate the early dynamics of transmission in Wuhan, we fitted a stochastic transmission dynamic model to multiple publicly available datasets on cases in Wuhan and internationally exported cases from Wuhan. The four datasets we fitted to were: daily number of new internationally exported cases (or lack thereof), by date of onset, as of Jan 26, 2020; daily number of new cases in Wuhan with no market exposure, by date of onset, between Dec 1, 2019, and Jan 1, 2020; daily number of new cases in China, by date of onset, between Dec 29, 2019, and Jan 23, 2020; and proportion of infected passengers on evacuation flights between Jan 29, 2020, and Feb 4, 2020. We used an additional two datasets for comparison with model outputs: daily number of new exported cases from Wuhan (or lack thereof) in countries with high connectivity to Wuhan (ie, top 20 most at-risk countries), by date of confirmation, as of Feb 10, 2020; and data on new confirmed cases reported in Wuhan between Jan 16, 2020, and Feb 11, 2020.
findingsWe estimated that the median daily reproduction number (R
interpretationOur results show that COVID-19 transmission probably declined in Wuhan during late January, 2020, coinciding with the introduction of travel control measures. As more cases arrive in international locations with similar transmission potential to Wuhan before these control measures, it is likely many chains of transmission will fail to establish initially, but might lead to new outbreaks eventually.
fundingWellcome Trust, Health Data Research UK, Bill & Melinda Gates Foundation, and National Institute for Health Research.
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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.