ArticlePloS one2011
Early estimation of the reproduction number in the presence of imported cases: pandemic influenza H1N1-2009 in New Zealand.
Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 3 of them syntheses that pooled it.
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Who cites it
34 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Pooled it
- Estimates of the reproduction number for seasonal, pandemic, and zoonotic influenza: a systematic review of the literature.BMC infectious diseases · 2014Pooled it
- Effectiveness of antiviral prophylaxis coupled with contact tracing in reducing the transmission of the influenza A (H1N1-2009): a systematic review.Theoretical biology & medical modelling · 2013Pooled it
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- A Primer on Inference and Prediction With Epidemic Renewal Models and Sequential Monte Carlo.Statistics in medicine · 2025Article
- Comprehensive Spatial-Temporal and Risk Factor Insights for Optimizing Livestock Anthrax Vaccination Strategies in Karnataka, India.Vaccines · 2024Article
- Risk averse reproduction numbers improve resurgence detection.PLoS computational biology · 2023Article
- Importation, Local Transmission, and Model Selection in Estimating the Transmissibility of COVID-19: The Outbreak in Shaanxi Province of China as a Case Study.Tropical medicine and infectious disease · 2022Article
- Deciphering early-warning signals of SARS-CoV-2 elimination and resurgence from limited data at multiple scales.Journal of the Royal Society, Interface · 2021Article
- Viral load and contact heterogeneity predict SARS-CoV-2 transmission and super-spreading events.eLife · 2021Article
- Evaluating the effect of Chinese control measures on COVID-19 via temporal reproduction number estimation.PloS one · 2021Article
- An exact method for quantifying the reliability of end-of-epidemic declarations in real time.PLoS computational biology · 2020Article
- Influence of isolation measures for patients with mild symptoms on the spread of COVID-19.Chaos, solitons, and fractals · 2020Article
- Wrong person, place and time: viral load and contact network structure predict SARS-CoV-2 transmission and super-spreading events.medRxiv : the preprint server for health sciences · 2020Article
- Pandæsim: An Epidemic Spreading Stochastic Simulator.Biology · 2020Article
- Reconciling early-outbreak estimates of the basic reproductive number and its uncertainty: framework and applications to the novel coronavirus (SARS-CoV-2) outbreak.Journal of the Royal Society, Interface · 2020Article
- Current epidemiological and clinical features of COVID-19; a global perspective from China.The Journal of infection · 2020Review
- Inferring generation-interval distributions from contact-tracing data.Journal of the Royal Society, Interface · 2020Article
- Transmission dynamics of the COVID-19 outbreak and effectiveness of government interventions: A data-driven analysis.Journal of medical virology · 2020Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We analyse data from the early epidemic of H1N1-2009 in New Zealand, and estimate the reproduction number R. We employ a renewal process which accounts for imported cases, illustrate some technical pitfalls, and propose a novel estimation method to address these pitfalls. Explicitly accounting for the infection-age distribution of imported cases and for the delay in transmission dynamics due to international travel, R was estimated to be (95% confidence interval: 107,1.47). Hence we show that a previous study, which did not account for these factors, overestimated R. Our approach also permitted us to examine the infection-age at which secondary transmission occurs as a function of calendar time, demonstrating the downward bias during the beginning of the epidemic. These technical issues may compromise the usefulness of a well-known estimator of R--the inverse of the moment-generating function of the generation time given the intrinsic growth rate. Explicit modelling of the infection-age distribution among imported cases and the examination of the time dependency of the generation time play key roles in avoiding a biased estimate of R, especially when one only has data covering a short time interval during the early growth phase of the epidemic.
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