Bayesian spatio-temporal model with INLA for dengue fever risk prediction in Costa Rica

TL;DR

This paper develops a Bayesian spatio-temporal model using INLA to predict dengue fever risk in Costa Rica, aiming to improve early warning systems and public health responses.

Contribution

It introduces a novel Bayesian INLA-based modeling approach tailored to Costa Rica's micro-climates for dengue risk prediction.

Findings

Model effectively captures spatial and temporal dengue risk patterns.

Provides accurate short-term risk forecasts for targeted interventions.

Supports public health decision-making with cost-effective predictions.

Abstract

Due to the rapid geographic spread of the Aedes mosquito and the increase in dengue incidence, dengue fever has been an increasing concern for public health authorities in tropical and subtropical countries worldwide. Significant challenges such as climate change, the burden on health systems, and the rise of insecticide resistance highlight the need to introduce new and cost-effective tools for developing public health interventions. Various and locally adapted statistical methods for developing climate-based early warning systems have increasingly been an area of interest and research worldwide. Costa Rica, a country with micro-climates and endemic circulation of the dengue virus (DENV) since 1993, provides ideal conditions for developing projection models with the potential to help guide public health efforts and interventions to control and monitor future dengue outbreaks.