Abstract
The time-dependent reproduction number, Rt, 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 Rt, together with their standard deviations, are obtained as a by-product.
Highlights
The degree of infectiousness of a disease is given by the basic reproduction number, R0, defined as the number of infections that are expected to result from a single infectious individual in a completely susceptible population
New time-series models are able to track the progress of an epidemic by providing nowcasts and forecasts of the daily number of new cases and deaths
Estimates and forecasts of the instantaneous reproduction number Rt can be computed as a by-product, using a formula that links it to the estimated growth rate of new cases, based on assumptions made about the serial interval distribution
Summary
The degree of infectiousness of a disease is given by the basic reproduction number, R0, defined as the number of infections that are expected to result from a single infectious individual in a completely susceptible population. Harvey & Kattuman [4]—hereafter HK—developed a class of generalized logistic (GL) time-series models for predicting future values of a variable which, when cumulated, is subject to an unknown saturation level. These models are relevant for many disciplines, but attention in HK was focused on applications for coronavirus.. Bayesian methods are often used to combine the information on the serial interval with the observations on new cases, often smoothed by a simple, but to some extent arbitrary, moving average.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
