Multiannual forecasting of seasonal influenza dynamics reveals climatic and evolutionary drivers

Abstract

Human influenza occurs annually in most temperate climatic zones of the world, with epidemics peaking in the cold winter months. Considerable debate surrounds the relative role of epidemic dynamics, viral evolution, and climatic drivers in driving year-toyear variability of outbreaks. The ultimate test of understanding is prediction; however, existing influenza models rarely forecast beyond a single year at best. Here, we use a simple epidemiological model to reveal multiannual predictability based on high-quality influenza surveillance data for Israel; the model fit is corroborated by simple metapopulation comparisons within Israel. Successful forecasts are driven by temperature, humidity, antigenic drift, and immunity loss. Essentially, influenza dynamics are a balance between large perturbations following significant antigenic jumps, interspersed with nonlinear epidemic dynamics tuned by climatic forcing.

Dynamical prediction of flu seasonality driven by ambient temperature: influenza vs. common cold

“…At the same time, it corresponds to the duration of common cold consequences [9] but is sufficiently smaller than the characteristic value for mutations of influenza viruses [27]. The last fact is in line with the result, which indicates that the considered temperature-driven model does not properly reproduce the curve of influenza.…”

Chaotic dynamics in the seasonally forced SIR epidemic model

“…school schedules), play an important role in the transmission and dynamical behaviour of most infectious diseases (Diedrichs et al 2014;Earn et al 2000;Rebelo et al 2012; B Alfonso Ruiz-Herrera ruizalfonso@uniovi.es Pablo G. Barrientos pgbarrientos@id.uff.br J. Ángel Rodríguez jarodriguez@uniovi.es Keeling and Grenfell 1997). Specifically, seasonal influenza generally recurs with a large epidemic in winter and a negligible presence in summer (Stone et al 2007;Axelsen et al 2014). Many childhood diseases such as measles, cough, and rubella are also highly sensitive to the opening and closing of the school year, as well as holidays periods (London and Yorke 1973).…”

“…The analysis of some empirical data suggests that intense seasonality leads to very erratic patterns. For example, many studies focusing on the annual influenza or the measle emphasized the presence of chaotic oscillations in response to seasonal forces (Axelsen et al 2014;Keeling and Grenfell 1997;Earn et al 2000;Stone et al 2007;Diedrichs et al 2014). Understanding how seasonality comes to shape the spread of an infectious disease is not doubt of great importance.…”

Forecasting influenza outbreak dynamics in Melbourne from Internet search query surveillance data

“…A variety of recursive Bayesian estimation methods (‘filters’) have been used for such forecasting purposes,1, 2 often in combination with Internet search query surveillance data and mechanistic models of infection 3, 4, 5, 6, 7, 8…”