Quantifying the transmission potential of pandemic influenza

TL;DR

This paper reviews quantitative methods for estimating the basic reproduction number of pandemic influenza, emphasizing the use of historical data and epidemic models to inform intervention strategies and understand disease transmission dynamics.

Contribution

It provides a comprehensive review of structured epidemic models, statistical methods, and stochastic processes for estimating influenza transmission potential from historical epidemic data.

Findings

Structured epidemic models help quantify disease dynamics.

Generation time distribution is crucial for estimating transmission.

Stochastic process applications improve transmission estimates.

Abstract

This article reviews quantitative methods to estimate the basic reproduction number of pandemic influenza, a key threshold quantity to help determine the intensity of interventions required to control the disease. Although it is difficult to assess the transmission potential of a probable future pandemic, historical epidemiologic data is readily available from previous pandemics, and as a reference quantity for future pandemic planning, mathematical and statistical analyses of historical data are crucial. In particular, because many historical records tend to document only the temporal distribution of cases or deaths (i.e. epidemic curve), our review focuses on methods to maximize the utility of time-evolution data and to clarify the detailed mechanisms of the spread of influenza. First, we highlight structured epidemic models and their parameter estimation method which can quantify the detailed disease dynamics including those we cannot observe directly. Duration-structured epidemic systems are subsequently presented, offering firm understanding of the definition of the basic and effective reproduction numbers. When the initial growth phase of an epidemic is investigated, the distribution of the generation time is key statistical information to appropriately estimate the transmission potential using the intrinsic growth rate. Applications of stochastic processes are also highlighted to estimate the transmission potential using the similar data. Critically important characteristics of influenza data are subsequently summarized, followed by our conclusions to suggest potential future methodological improvements.