A farewell to <i>R</i>: time-series models for tracking and forecasting epidemics.
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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
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.
State-space model, Kalman Filter, Waves, Gompertz Curve, Covid-19, Stochastic Trend, Humans, Models, Statistical, Bayes Theorem, Forecasting, Epidemics, COVID-19, SARS-CoV-2
External DOI: https://doi.org/10.1098/rsif.2021.0179
This record's URL: https://www.repository.cam.ac.uk/handle/1810/330118
Attribution 4.0 International
Licence URL: https://creativecommons.org/licenses/by/4.0/