A farewell to <i>R</i>: time-series models for tracking and forecasting epidemics.
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Publication Date
2021-09-29ISSN
1742-5689
Language
eng
Type
Article
This Version
VoR
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Harvey, A., & Kattuman, P. (2021). A farewell to <i>R</i>: time-series models for tracking and forecasting epidemics.. https://doi.org/10.1098/rsif.2021.0179
Abstract
The time-dependent reproduction number, <i>R</i><sub><i>t</i></sub>, is a key metric used by epidemiologists to assess the current state of an outbreak of an infectious disease. This quantity is usually estimated using time-series observations on new infections combined with assumptions about the distribution of the serial interval of transmissions. Bayesian methods are often used with the new cases data smoothed using a simple, but to some extent arbitrary, moving average. This paper describes a new class of time-series models, estimated by classical statistical methods, for tracking and forecasting the growth rate of new cases and deaths. Very few assumptions are needed and those that are made can be tested. Estimates of <i>R</i><sub><i>t</i></sub>, together with their standard deviations, are obtained as a by-product.
Keywords
State-space model, Kalman Filter, Waves, Gompertz Curve, Covid-19, Stochastic Trend, Humans, Models, Statistical, Bayes Theorem, Forecasting, Epidemics, COVID-19, SARS-CoV-2
Identifiers
PMC8479341, 34583564
External DOI: https://doi.org/10.1098/rsif.2021.0179
This record's URL: https://www.repository.cam.ac.uk/handle/1810/330118
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