Climate Variability and Threshold-Dependent Dengue Outbreak Dynamics in Tropical Southeast Asia: A Multi-Country Time-Series Study
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Abstract
INTRODUCTION: Dengue remains endemic across tropical Southeast Asia, where climate variability may alter transmission dynamics. However, the extent to which climate indicators provide predictive value beyond short-term transmission momentum remains uncertain. This study aims to quantify lagged associations between climate variables and dengue incidence and to evaluate the incremental predictive contribution of climate indicators under multiple outbreak definitions.
METHOD: A multi-country ecological weekly time-series analysis was conducted in Malaysia, the Philippines, Singapore, and Viet nam. Negative binomial distributed lag models (0–12 weeks) with population offset and country fixed effects were used to estimate cumulative incidence rate ratios. Model fit was assessed using Akaike Information Criterion. Early warning performance compared autoregressive baseline, climate-only, and combined models. Discrimination was evaluated using area under the receiver operating characteristic curve and area under the precision–recall curve, while calibration was assessed using Brier scores and reliability plots. Sensitivity analyses included alternative lag windows and outbreak definitions based on percentile thresholds and rolling historical baselines.
RESULTS: Rainfall demonstrated modest inverse cumulative associations, while temperature showed a positive but imprecise cumulative effect over 12 weeks. Lag-12 specification provided better model fit than lag-8, and non-linear temperature terms did not improve fit. Under percentile-based outbreak definitions, autoregressive models consistently outperformed climate-only models, with incremental discrimination observed primarily in Viet nam. When outbreaks were defined as deviations above rolling historical baselines, climate-enhanced models demonstrated substantially greater discrimination in Malaysia and the Philippines and moderate improvement in Viet nam. Calibration analysis indicated improved high-risk alignment in selected settings.
CONCLUSIONS: The predictive value of climate indicators in dengue outbreak detection is context-dependent and sensitive to outbreak definition. Climate variables enhance identification of abnormal incidence surges rather than routine transmission momentum, supporting their integration into country-specific early warning systems.
Keywords: dengue; climate variability; distributed lag model; early warning; Southeast Asia
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