Forecasting infectious disease emergence subject to seasonal forcing

Paige B Miller,Eamon B O’Dea,Pejman Rohani,John M Drake

doi:10.1186/s12976-017-0063-8

Paige B Miller, Eamon B O’Dea + Show 2 more

Open Access

https://doi.org/10.1186/s12976-017-0063-8

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Abstract

BackgroundDespite high vaccination coverage, many childhood infections pose a growing threat to human populations. Accurate disease forecasting would be of tremendous value to public health. Forecasting disease emergence using early warning signals (EWS) is possible in non-seasonal models of infectious diseases. Here, we assessed whether EWS also anticipate disease emergence in seasonal models.MethodsWe simulated the dynamics of an immunizing infectious pathogen approaching the tipping point to disease endemicity. To explore the effect of seasonality on the reliability of early warning statistics, we varied the amplitude of fluctuations around the average transmission. We proposed and analyzed two new early warning signals based on the wavelet spectrum. We measured the reliability of the early warning signals depending on the strength of their trend preceding the tipping point and then calculated the Area Under the Curve (AUC) statistic.ResultsEarly warning signals were reliable when disease transmission was subject to seasonal forcing. Wavelet-based early warning signals were as reliable as other conventional early warning signals. We found that removing seasonal trends, prior to analysis, did not improve early warning statistics uniformly.ConclusionsEarly warning signals anticipate the onset of critical transitions for infectious diseases which are subject to seasonal forcing. Wavelet-based early warning statistics can also be used to forecast infectious disease.

Highlights

Despite high vaccination coverage, many childhood infections pose a growing threat to human populations
We documented signals of critical slowing down when transmission was subject to periodic variation, addressing a current gap in studies of early warning signals for critical transitions (Fig. 2)
The Area Under the Curve (AUC) of most early warning statistics were negatively associated with increasing seasonal amplitude (Figs. 2 and 3)

Summary

Introduction

Many childhood infections pose a growing threat to human populations. Disease forecasting is challenging because transmission is subject to complex interactions among a variety of independent actors, non-linearity, noise, and seasonality driven by age [2] and spatial structure [3], susceptible depletion [4, 5], environmental variability [6], and behavior [5, 7] Despite these challenges, a number of approaches have been developed along distinctly different lines: (i) sequential Monte-Carlo methods (e.g., [8]), (ii) data assimilation techniques inspired by numerical weather prediction [9, 10], and (iii) “Wisdom of the Crowd” approaches [11] among others. We are in the earlier stages of theory and method development for anticipating the emergence of infectious disease [12]

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Discussion

Conclusion