Fast estimation of time-varying infectious disease transmission rates

by Mikael Jagan, Michelle S. deJonge, Olga Krylova, David J. D. Earn

Compartmental epidemic models have been used extensively to study the historical spread of infectious diseases and to inform strategies for future control. A critical parameter of any such model is the transmission rate. Temporal variation in the transmission rate has a profound influence on disease spread. For this reason, estimation of time-varying transmission rates is an important step in identifying mechanisms that underlie patterns in observed disease incidence and mortality. Here, we present and test fast methods for reconstructing transmission rates from time series of reported incidence. Using simulated data, we quantify the sensitivity of these methods to parameters of the data-generating process and to mis-specification of input parameters by the user. We show that sensitivity to the user’s estimate of the initial number of susceptible individuals—considered to be a major limitation of similar methods—can be eliminated by an efficient, “peak-to-peak” iterative technique, which we propose. The method of transmission rate estimation that we advocate is extremely fast, for even the longest infectious disease time series that exist. It can be used independently or as a fast way to obtain better starting conditions for computationally expensive methods, such as iterated filtering and generalized profiling.

Make more money selling and advertising your products and services for free on Ominy market. Click here to start selling now

Paper source
Plos Journal

READ MORE  Correction: COVID-19 prevention and treatment: A critical analysis of chloroquine and hydroxychloroquine clinical pharmacology

Ominy science editory team

A team of dedicated users that search, fetch and publish research stories for Ominy science.

Enable notifications of new posts    OK No thanks