Estimating the reproductive numbers of influenza pandemics from multiple historical data sources

James E. Oeppen, Max Planck Institute for Demographic Research
Carlo G. Camarda, Max Planck Institute for Demographic Research

One of the contributions that historical demography can make to modern science is to expand the contexts available for testing methodologies designed to address contemporary problems. This is particularly relevant for influenza modelling as there have only been five genetic shifts leading to pandemics in the past 125 years. An influenza pandemic is the outcome of an interaction between two populations – the human immune system and the new variant of the influenza A virus – each with very different demographic characteristics. Accurate real-time estimation of the parameters of this interaction, together with their confidence intervals, would be of enormous help to health planners. Our model aims to improve the estimation of the daily Reproductive Number (R0¬) of past pandemics in their growth phase (NRR in demographic terms), while explicitly accounting for the mis-recording created by week-ends and public holidays. When R0¬ is greater than unity a pandemic continues to grow, so the evolution of this crucial parameter is an indicator of the need for intervention and a measure of its success or failure. To evaluate the model we use data from two influenza pandemics: 1889-90 in Munich and 1918 in New York State. Treating these historical data as an ex ante estimation problem shows how well this key parameter might be estimated in a future pandemic.