Abstract
Seasonal influenza has considerable impact around the world, both economically and in mortality among risk groups, but there is considerable uncertainty as to the essential mechanisms and their parametrization. In this paper, we identify a number of characteristic features of influenza incidence time series in temperate regions, including ranges of annual attack rates and outbreak durations. By constraining the output of simple models to match these characteristic features, we investigate the role played by population heterogeneity, multiple strains, cross-immunity and the rate of strain evolution in the generation of incidence time series. Results indicate that an age-structured model with non-random mixing and co-circulating strains are both required to match observed time-series data. Our work gives estimates of the seasonal peak basic reproduction number, R0, in the range 1.6–3. Estimates of R0 are strongly correlated with the timescale for waning of immunity to current circulating seasonal influenza strain, which we estimate is between 3 and 8 years. Seasonal variation in transmissibility is largely confined to 15–30% of its mean value. While population heterogeneity and cross-immunity are required mechanisms, the degree of heterogeneity and cross-immunity is not tightly constrained. We discuss our findings in the context of other work fitting to seasonal influenza data.
Highlights
Seasonal influenza causes significant levels of morbidity and mortality around the world each year, yet its dynamics and the annual sequence of pathogen subtypes are hard to predict [1]
We identify a set of key features characterizing seasonal influenza-like illness (ILI) incidence time series and use these to define information measures for the distance between a transmission model and empirical observations
The region of best fits is located in a narrow diagonal band spanning 1.6, Rp, 2.5 and 3, D, 8. Along this band, increasing reproduction number is compensated for by a longer period of effective immunity that decreases the susceptible proportion of the population, giving a constant mean attack rate
Summary
Seasonal influenza causes significant levels of morbidity and mortality around the world each year, yet its dynamics and the annual sequence of pathogen subtypes are hard to predict [1]. Understanding the mechanisms underlying the annual behaviour of influenza and their sensitivity to parameters is important for the forecasting and control of seasonal epidemics and in assessing the effect of the introduction of novel antigenic strains. The 2009 H1N1 pandemic is a recent example [2]. The initial outbreak of this novel H1N1 strain occurred in spring and summer, ‘out of season’ in the Northern Hemisphere. Uncertainty with regard to intensity of transmission during summer substantially complicated forecasting of the likely trajectory of the epidemic over the following months.
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