PubMed · 16375708
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Thomas W Geisbert. 2005. Emerging viruses: advances and challenges.. https://doi.org/10.2174/156652405774962290
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BACKGROUND: Rapid response to outbreaks of emerging infectious diseases is impeded by uncertain diagnoses and delayed communication. Understanding the effect of inefficient response is a potentially important contribution of epidemic theory. To develop this understanding we studied societal learning during emerging outbreaks wherein patient removal accelerates as information is gathered and disseminated. METHODS AND FINDINGS: We developed an extension of a standard outbreak model, the simple stochastic epidemic, which accounts for societal learning. We obtained expressions for the expected outbreak size and the distribution of epidemic duration. We found that rapid learning noticeably affects the final outbreak size even when learning exhibits diminishing returns (relaxation). As an example, we estimated the learning rate for the 2003 outbreak of severe acute respiratory syndrome (SARS) in Singapore. Evidence for relaxation during the first eight weeks of the outbreak was inconclusive. We estimated that if societal learning had occurred at half the actual rate, the expected final size of the outbreak would have reached nearly 800 cases, more than three times the observed number of infections. By contrast, the expected outbreak size for societal learning twice as effective was 116 cases. CONCLUSION: These results show that the rate of societal learning can greatly affect the final size of disease outbreaks, justifying investment in early warning systems and attentiveness to disease outbreak by both government authorities and the public. We submit that the burden of emerging infections, including the risk of a global pandemic, could be efficiently reduced by improving procedures for rapid detection of outbreaks, alerting public health officials, and aggressively educating the public at the start of an outbreak.
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During the 2003 Severe Acute Respiratory Syndrome (SARS) outbreak, traditional intervention measures such as quarantine and border control were found to be useful in containing the outbreak. We used laboratory verified SARS case data and the detailed quarantine data in Taiwan, where over 150,000 people were quarantined during the 2003 outbreak, to formulate a mathematical model which incorporates Level A quarantine (of potentially exposed contacts of suspected SARS patients) and Level B quarantine (of travelers arriving at borders from SARS affected areas) implemented in Taiwan during the outbreak. We obtain the average case fatality ratio and the daily quarantine rate for the Taiwan outbreak. Model simulations is utilized to show that Level A quarantine prevented approximately 461 additional SARS cases and 62 additional deaths, while the effect of Level B quarantine was comparatively minor, yielding only around 5% reduction of cases and deaths. The combined impact of the two levels of quarantine had reduced the case number and deaths by almost a half. The results demonstrate how modeling can be useful in qualitative evaluation of the impact of traditional intervention measures for newly emerging infectious diseases outbreak when there is inadequate information on the characteristics and clinical features of the new disease-measures which could become particularly important with the looming threat of global flu pandemic possibly caused by a novel mutating flu strain, including that of avian variety.