Journal Article

A Chain Multinomial Model for Estimating the Real-Time Fatality Rate of a Disease, with an Application to Severe Acute Respiratory Syndrome

Paul S. F. Yip, Eric H. Y. Lau, K. F. Lam and Richard M. Huggins

in American Journal of Epidemiology

Published on behalf of Johns Hopkins Bloomberg School of Public Health

Volume 161, issue 7, pages 700-706
Published in print April 2005 | ISSN: 0002-9262
Published online April 2005 | e-ISSN: 1476-6256 | DOI: http://dx.doi.org/10.1093/aje/kwi088
A Chain Multinomial Model for Estimating the Real-Time Fatality Rate of a Disease, with an Application to Severe Acute Respiratory Syndrome

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It is well known that statistics using cumulative data are insensitive to changes. World Health Organization (WHO) estimates of fatality rates are of the above type, which may not be able to reflect the latest changes in fatality due to treatment or government policy in a timely fashion. Here, the authors propose an estimate of a real-time fatality rate based on a chain multinomial model with a kernel function. It is more accurate than the WHO estimate in describing fatality, especially earlier in the course of an epidemic. The estimator provides useful information for public health policy makers for understanding the severity of the disease or evaluating the effects of treatments or policies within a shorter time period, which is critical in disease control during an outbreak. Simulation results showed that the performance of the proposed estimator is superior to that of the WHO estimator in terms of its sensitivity to changes and its timeliness in reflecting the severity of the disease.

Keywords: disease outbreaks; epidemiologic methods; fatality rate; models, statistical; multinomial model; severe acute respiratory syndrome; SARS, severe acute respiratory syndrome; WHO, World Health Organization

Journal Article.  3218 words.  Illustrated.

Subjects: Public Health and Epidemiology

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