Modeling Super-spreading Events for Infectious Diseases: Case Study SARS

TL;DR

This paper develops a model for super-spreading events in infectious diseases, specifically SARS, and compares two methods of parameter estimation to predict outbreak severity.

Contribution

It introduces a novel super-spreading model based on SARS data and compares parameter estimation methods for outbreak prediction.

Findings

Model predicts similar outcomes to SIR when using SIR parameters.

Model predicts more severe outbreaks with literature-based parameters.

Demonstrates applicability to both small and large SARS outbreaks.

Abstract

Super-spreading events for infectious diseases occur when some infected individuals infect more than the average number of secondary cases. Several super-spreading individuals have been identified for the 2003 outbreak of severe acute respiratory syndrome (SARS). We develop a model for super-spreading events of infectious diseases, which is based on the outbreak of SARS. Using this model we describe two methods for estimating the parameters of the model, which we demonstrate with the small-scale SARS outbreak at the Amoy Gardens, Hong Kong, and the large-scale outbreak in the entire Hong Kong Special Administrative Region. One method is based on parameters calculated for the classical susceptible - infected - removed (SIR) disease model. The second is based on parameter estimates found in the literature. Using the parameters calculated for the SIR model, our model predicts an outcome similar to that for the SIR model. On the other hand, using parameter estimates from SARS literature our model predicts a much more serious epidemic.