[Accepted Manuscript] Quantifying the added value of climate information in a spatio-temporal dengue model


Lowe, R.; Cazelles, B.; Paul, R.; Rodó, X.; (2015) [Accepted Manuscript] Quantifying the added value of climate information in a spatio-temporal dengue model. Stochastic environmental research and risk assessment. ISSN 1436-3240 DOI: https://doi.org/10.1007/s00477-015-1053-1

WarningThere is a more recent version of this item available.
[img]
Preview
Text - Published Version
License:

Download (7MB) | Preview

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.

Item Type: Article
Faculty and Department: Faculty of Epidemiology and Population Health > Dept of Infectious Disease Epidemiology
URI: http://researchonline.lshtm.ac.uk/id/eprint/3783977

Available Versions of this Item

Statistics


Download activity - last 12 months
Downloads since deposit
53Downloads
33Hits
Accesses by country - last 12 months
Accesses by referrer - last 12 months
Impact and interest
Additional statistics for this record are available via IRStats2

Actions (login required)

Edit Item Edit Item