Mapping malaria risk among children in Côte d'Ivoire using Bayesian geo-statistical models

Area of Science:

• Epidemiology
• Geographic Information Systems (GIS)
• Spatial Analysis

Background:

• Malaria causes substantial disability-adjusted life years in Côte d'Ivoire, ranking it 14th globally.
• Effective malaria control requires accurate risk mapping for targeted interventions.

Purpose of the Study:

• To predict the geographical distribution of malaria infection risk in children under 16 in Côte d'Ivoire.
• To develop a high-resolution spatial risk map for malaria in children.

Main Methods:

• A systematic review compiled Plasmodium spp. infection prevalence data (1988-2007) for children under 16.
• Bayesian geo-statistical logistic regression models, including non-stationary spatial models, were employed.
• The best-fitting model incorporated environmental covariates like rainfall and temperature.

Main Results:

• 235 data points from 170 unique survey locations were analyzed.
• A Bayesian non-stationary regression model identified rainfall and temperature as significant predictors.
• High-risk malaria areas were predicted in north-central and western Côte d'Ivoire.

Conclusions:

• A high-resolution malaria risk map provides a crucial overview of disease distribution in Côte d'Ivoire.
• The map serves as a valuable tool for the national malaria control program.
• Spatial targeting of interventions and resource allocation can be optimized using this risk map.