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Quantifying the added value of climate information in a spatio-temporal dengue model

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Abstract

Dengue is the world’s most important vector-borne viral disease. The dengue mosquito and virus are sensitive to climate variability and change. Temperature, humidity and precipitation influence mosquito biology, abundance and habitat, and the virus replication speed. In this study, we develop a modelling procedure to quantify the added value of including climate information in a dengue model for the 76 provinces of Thailand, from 1982–2013. We first developed a seasonal-spatial model, to account for dependency structures from 1 month to the next and between provinces. We then tested precipitation and temperature variables at varying time lags, using linear and nonlinear functional forms, to determine an optimum combination of time lags to describe dengue relative risk. Model parameters were estimated using integrated nested Laplace approximation. This approach provides a novel opportunity to perform model selection in a Bayesian framework, while accounting for underlying spatial and temporal dependency structures and linear or nonlinear functional forms. We quantified the additional variation explained by interannual climate variations, above that provided by the seasonal-spatial model. Overall, an additional 8 % of the variance in dengue relative risk can be explained by accounting for interannual variations in precipitation and temperature in the previous month. The inclusion of nonlinear functions of climate in the model framework improved the model for 79 % of the provinces. Therefore, climate forecast information could significantly contribute to a national dengue early warning system in Thailand.

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Acknowledgments

The research leading to these results has received funding from the DENFREE project (Grant Agreement No. 282378) funded by the European Commission’s Seventh Framework Research Programme. RL is grateful to the STEPHI project, Daniel Simpson and Harvard Rue for valuable training and technical support in using the R-INLA package.

Conflict of interest

We declare no competing interests.

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Authors and Affiliations

  1. Climate Dynamics and Impacts Unit, Institut Català de Ciències del Clima (IC3), Doctor Trueta 203, 3a, 08005, Barcelona, Catalonia, Spain

    Rachel Lowe & Xavier Rodó

  2. Unité de Modélisation Mathématique et Informatique des Systèmes Complexes, Université Pierre et Marie Curie, Campus des Cordeliers, 75270, Paris Cedex 06, France

    Bernard Cazelles

  3. Ecole Normale Supérieure, IBENS UMR 8197, Eco-Evolution Mathématique, 46 rue d’Ulm, 75230, Paris Cedex 05, France

    Bernard Cazelles

  4. Institut Pasteur, Unité de la Génétique Fonctionnelle des Maladies Infectieuses, 28 rue du Docteur Roux, 75724, Paris Cedex 15, France

    Richard Paul

  5. Centre National de la Recherche Scientifique, Unité de Recherche Associée 3012, 75724, Paris Cedex 15, France

    Richard Paul

  6. Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain

    Xavier Rodó

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  1. Rachel Lowe
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Correspondence to Rachel Lowe.

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Lowe, R., Cazelles, B., Paul, R. et al. Quantifying the added value of climate information in a spatio-temporal dengue model. Stoch Environ Res Risk Assess 30, 2067–2078 (2016). https://doi.org/10.1007/s00477-015-1053-1

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