Comparing Models for Early Warning Systems of Neglected Tropical Diseases

Luis Fernando Chaves,Mercedes Pascual,Juerg Utzinger

doi:10.1371/journal.pntd.0000033

Luis Fernando Chaves, Mercedes Pascual + Show 1 more

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https://doi.org/10.1371/journal.pntd.0000033

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Abstract

BackgroundEarly warning systems (EWS) are management tools to predict the occurrence of epidemics of infectious diseases. While climate-based EWS have been developed for malaria, no standard protocol to evaluate and compare EWS has been proposed. Additionally, there are several neglected tropical diseases whose transmission is sensitive to environmental conditions, for which no EWS have been proposed, though they represent a large burden for the affected populations.Methodology/Principal FindingsIn the present paper, an overview of the available linear and non-linear tools to predict seasonal time series of diseases is presented. Also, a general methodology to compare and evaluate models for prediction is presented and illustrated using American cutaneous leishmaniasis, a neglected tropical disease, as an example. The comparison of the different models using the predictive R 2 for forecasts of “out-of-fit” data (data that has not been used to fit the models) shows that for the several linear and non-linear models tested, the best results were obtained for seasonal autoregressive (SAR) models that incorporate climatic covariates. An additional bootstrapping experiment shows that the relationship of the disease time series with the climatic covariates is strong and consistent for the SAR modeling approach. While the autoregressive part of the model is not significant, the exogenous forcing due to climate is always statistically significant. Prediction accuracy can vary from 50% to over 80% for disease burden at time scales of one year or shorter.Conclusions/SignificanceThis study illustrates a protocol for the development of EWS that includes three main steps: (i) the fitting of different models using several methodologies, (ii) the comparison of models based on the predictability of “out-of-fit” data, and (iii) the assessment of the robustness of the relationship between the disease and the variables in the model selected as best with an objective criterion.

Highlights

One of the best documented patterns in the dynamics of vectortransmitted diseases is their periodicity at seasonal and interannual temporal scales [1,2,3,4,5,6,7]
For prediction steps larger than one month only non-linear forecasting (NLF), seasonal autoregressive (SAR) and generalized additive models (GAM) models with environmental covariates, MEI and T (4 months lag), did better than predictions based on the average of the time series (Table 1)
Our results indicate that American cutaneous leishmaniasis (ACL) is another example of a population phenomenon whose dynamics can be satisfactorily described by linear statistical models, provided that appropriate covariates and transformations of the data are used

Summary

Introduction

One of the best documented patterns in the dynamics of vectortransmitted diseases is their periodicity at seasonal and interannual temporal scales [1,2,3,4,5,6,7]. These periodicities are the basis for the proposal that early warning systems (EWS) are feasible and useful tools for planning and decision making [2]. Warning systems (EWS) are management tools to predict the occurrence of epidemics of infectious diseases. There are several neglected tropical diseases whose transmission is sensitive to environmental conditions, for which no EWS have been proposed, though they represent a large burden for the affected populations

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Discussion

Conclusion