SARS oubreaks in Ontario, Hong Kong and Singapore: the role of diagnosis and isolation as a control mechanism

TL;DR

This study models SARS outbreaks in Ontario, Hong Kong, and Singapore using a SEIJR model to analyze the effects of diagnosis and isolation on controlling the epidemic, highlighting regional differences and the importance of rapid diagnosis.

Contribution

It introduces a SEIJR model fitted to regional data to evaluate how diagnosis and isolation influence SARS epidemic dynamics and containment.

Findings

Toronto's outbreak slowed after increased diagnosis and isolation.

The average reproductive number of SARS is approximately 1.2.

Containment is possible but sensitive to model parameters.

Abstract

In this article we use global and regional data from the SARS epidemic in conjunction with a model of susceptible, exposed, infective, diagnosed, and recovered classes of people (``SEIJR'') to extract average properties and rate constants for those populations. The model is fitted to data from the Ontario (Toronto) in Canada, Hong Kong in China and Singapore outbreaks and predictions are made based on various assumptions and observations, including the current effect of isolating individuals diagnosed with SARS. The epidemic dynamics for Hong Kong and Singapore appear to be different from the dynamics in Toronto, Ontario. Toronto shows a very rapid increase in the number of cases between March 31st and April 6th, followed by a {\it significant} slowing in the number of new cases. We explain this as the result of an increase in the diagnostic rate and in the effectiveness of patient isolation after March 26th. Our best estimates are consistent with SARS eventually being contained in Toronto, although the time of containment is sensitive to the parameters in our model. It is shown that despite the empirically modeled heterogeneity in transmission, SARS' average reproductive number is 1.2, a value quite similar to that computed for some strains of influenza \cite{CC2}. Although it would not be surprising to see levels of SARS infection higher than ten per cent in some regions of the world (if unchecked), lack of data and the observed heterogeneity and sensitivity of parameters prevent us from predicting the long-term impact of SARS.