Predict, Prevent, Protect: Climate Data and Smart Maps for Dengue Defense

Abstract

Dengue fever is an important public health problem in tropical and subtropical areas. In the Philippines, the disease transmission is highly affected by the weather that favors the reproduction of mosquitoes and the spread of the virus. Traditional dengue surveillance systems are largely reactive and only act once case numbers increase sharply, causing delays in intervention and added pressure on healthcare systems. This study presents a computational early warning system based on climatic factors, past dengue cases, and GIS (Geographic Information System) for temperature data, as well as recorded dengue cases from selected cities in Negros Oriental (Tanjay, Dumaguete, Bayawan, Guihulngan, Bais, and Canlaon) will be used. Using a uniform statistical technique, G-scores for all the variables were computed, which were then accumulated into risk scores and converted into risk levels in percentage. To identify regions that may be susceptible to outbreaks, these values were spatially represented through GIS mapping. Results indicate significant spatial disparities in dengue risk across study areas. The areas with the greatest risk of outbreak were Tanjay City, while Dumaguete and Canlaon were moderate-risk places. The risk levels were low in Bayawan, Guihulngan, and Bais. The mapping through GIS highlighted distinct hotspots for intervention, which means that resources were not going to be wasted on random action. The findings point out that the utilization of climate information can efficiently map out high-risk areas and likewise provide informed suggestions for Local Government Units (LGUs). Despite limitations arising from data availability and the disregard of socio-environmental factors, the framework outlined may help in shifting from reactive to proactive dengue control. The strategy strengthens disease surveillance, enhances preparedness, and offers a scalable model for tackling other climate-sensitive diseases in similar settings.