A note on the estimation of the initial number of susceptible individuals in the general epidemic model

Traditional inference for epidemic models depends on knowledge of the initial number of susceptible individuals. However, this may be difficult to obtain in practice. In this short note we show that it is possible to use data from a major epidemic to estimate the number of individuals initially susceptible to a disease and an approximate asymptotic variance is derived. The results are confirmed in simulations of major epidemics. An application to a data set on smallpox is given. © 2004 Elsevier B.V. All rights reserved

A chain multinomial model for estimating the real-time fatality rate of a disease, with an application to severe acute respiratory syndrome

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. Copyright © 2005 Johns Hopkins Bloomberg School of Public Health All rights reserved