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Joint estimation of the basic reproduction number and generation time parameters for infectious disease outbreaks.

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TLDR
It is recommended that, wherever possible, estimation of the basic and effective reproduction numbers should be based on a well-defined epidemic model; moreover, if external information is available then it should be incorporated in a Bayesian analysis.
Abstract
The basic reproduction number is a key parameter determining whether an infectious disease will persist. Its counterpart over time, the effective reproduction number, is of value in assessing in real time whether interventions have brought an outbreak under control. In this paper, we use theoretical arguments and simulation to understand the relationship between estimation of the reproduction number based on a full continuous time epidemic model and 2 other recently developed estimators. All these methods make use of "epidemic curve" data and require assumptions about the generation time distribution. The 2 simplest estimators do not require information about the-often difficult to obtain-population size. The simplest estimator is shown to require further assumptions that are rarely valid in practical settings and to produce severely biased estimates compared to the others. Furthermore, we show that in general the parameters of the generation time distribution and the reproduction number are non-identified in the early stages of an incomplete outbreak. On the basis of these results, we recommend that, wherever possible, estimation of the basic and effective reproduction numbers should be based on a well-defined epidemic model; moreover, if external information is available then it should be incorporated in a Bayesian analysis.

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Citations
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Journal ArticleDOI

Improved inference of time-varying reproduction numbers during infectious disease outbreaks

TL;DR: It is shown that accurate inference of current transmissibility, and the uncertainty associated with this estimate, requires: up-to-date observations of the serial interval to be included, and cases arising from local transmission to be distinguished from those imported from elsewhere.
Journal ArticleDOI

Estimation in emerging epidemics: biases and remedies.

TL;DR: In this paper, the authors propose methods to remove or at least reduce bias using statistical modelling, with emphasis on: contact tracing backwards in time, replacing generation times by serial intervals, multiple potential infectors and censoring effects amplified by exponential growth.
Journal ArticleDOI

Capturing the time-varying drivers of an epidemic using stochastic dynamical systems

TL;DR: This paper considers stochastic extensions in order to capture unknown influences (changing behaviors, public interventions, seasonal effects, etc.) and introduces diffusion-driven susceptible exposed infected retired-type models with age structure.
Posted Content

Capturing the time-varying drivers of an epidemic using stochastic dynamical systems

TL;DR: In this article, the authors consider stochastic extensions in order to capture unknown influences (changing behaviors, public interventions, seasonal effects etc) and assign diffusion processes to the time-varying parameters, and their inferential procedure is based on a suitably adjusted adaptive particle MCMC algorithm.
Journal ArticleDOI

The correlation between infectivity and incubation period of measles, estimated from households with two cases.

TL;DR: Results indicate that the mean incubation period and the generation time of measles are positively correlated, and that both lie in the range of 11-12 days, suggesting that infectiousness of measles cases increases significantly around the time of symptom onset.
References
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Book

Infectious Diseases of Humans: Dynamics and Control

TL;DR: This book discusses the biology of host-microparasite associations, dynamics of acquired immunity heterogeneity within the human community indirectly transmitted helminths, and the ecology and genetics of hosts and parasites.
Journal ArticleDOI

Transmission Dynamics and Control of Severe Acute Respiratory Syndrome

TL;DR: It is estimated that a single infectious case of SARS will infect about three secondary cases in a population that has not yet instituted control measures, and public-health efforts to reduce transmission are expected to have a substantial impact on reducing the size of the epidemic.
Journal ArticleDOI

Different Epidemic Curves for Severe Acute Respiratory Syndrome Reveal Similar Impacts of Control Measures

TL;DR: A likelihood-based estimation procedure that infers the temporal pattern of effective reproduction numbers from an observed epidemic curve for SARS revealed that epidemics in the various affected regions were characterized by markedly similar disease transmission potentials and similar levels of effectiveness of control measures.
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