ArticlePLoS computational biology2016
Detecting Presymptomatic Infection Is Necessary to Forecast Major Epidemics in the Earliest Stages of Infectious Disease Outbreaks.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.
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Who cites it
44 citing papers in PubMed, 1 synthesis or guideline pooled it, 84 citations in OpenAlex.
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- Interventions targeting non-symptomatic cases can be important to prevent local outbreaks: SARS-CoV-2 as a case study.Journal of the Royal Society, Interface · 2021Article
- Statistical Estimation of the Reproductive Number From Case Notification Data.American journal of epidemiology · 2021Review
- The effect of the definition of 'pandemic' on quantitative assessments of infectious disease outbreak risk.Scientific reports · 2021Article
- Lies, Gosh Darn Lies, and not enough good statistics: why epidemic model parameter estimation fails.Scientific reports · 2021Article
- The risk of SARS-CoV-2 outbreaks in low prevalence settings following the removal of travel restrictions.Communications medicine · 2021Article
- Will an outbreak exceed available resources for control? Estimating the risk from invading pathogens using practical definitions of a severe epidemic.Journal of the Royal Society, Interface · 2020Article
- Containment, Contact Tracing and Asymptomatic Transmission of Novel Coronavirus Disease (COVID-19): A Modelling Study.Journal of clinical medicine · 2020Article
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Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
We assess how presymptomatic infection affects predictability of infectious disease epidemics. We focus on whether or not a major outbreak (i.e. an epidemic that will go on to infect a large number of individuals) can be predicted reliably soon after initial cases of disease have appeared within a population. For emerging epidemics, significant time and effort is spent recording symptomatic cases. Scientific attention has often focused on improving statistical methodologies to estimate disease transmission parameters from these data. Here we show that, even if symptomatic cases are recorded perfectly, and disease spread parameters are estimated exactly, it is impossible to estimate the probability of a major outbreak without ambiguity. Our results therefore provide an upper bound on the accuracy of forecasts of major outbreaks that are constructed using data on symptomatic cases alone. Accurate prediction of whether or not an epidemic will occur requires records of symptomatic individuals to be supplemented with data concerning the true infection status of apparently uninfected individuals. To forecast likely future behavior in the earliest stages of an emerging outbreak, it is therefore vital to develop and deploy accurate diagnostic tests that can determine whether asymptomatic individuals are actually uninfected, or instead are infected but just do not yet show detectable symptoms.
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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.