Spatial Analysis of Dengue Infection Using Geographic Information Systems in Dili, Timor Leste

Abstract

Dengue is an acute arthropod virus infection transmitted mainly by the Aedes sp. mosquito. It is the most common arbovirus disease globally and mainly occurs in the tropics and subtropical regions of the world, with an estimated burden of 390 million cases annually. Dili municipality is the capital of Timor Leste; it is an endemic area for the dengue infection outbreak. Therefore, the aim of this study is to develop appropriate models for the identification of areas with the dengue risk factors assessed in Dili municipality by applying the Geographic Information System (GIS) as a tool for a spatial data collection system with integrated attribute data or non-spatial data to study factors influencing this municipality (Capital). There were two categories of data collected: primary data on the knowledge, attitude, and practice (KAP) of housewives regarding dengue prevention and control and mosquito larvae density surveys (container index (CI)); and secondary data on the number of dengue cases from January 2016 to August 2022, household numbers, residential areas, natural water resources, and improper drainage areas to analyze the relationship with dengue infection patients. The data were analyzed using Pearson correlations and descriptive statistics with stepwise multiple regression. According to the GIS model of dengue infection risk assessment, it was discovered that 9% of the total areas were very high-risk areas, 17.75% were high-risk areas, 10.30% were moderate-risk areas, and 62.96% were low-risk areas. At an administrative post (sub-district) level, including Dom Aleixo, Cristo Rei, Vera Cruz, Nain Feto, Metinaro, and Atauro, it was found that Dom Aleixo was only a very high-risk area covering 33.12 km2. At the village level, seven villages were at very high risk, eight villages were at high risk, thirteen villages were at moderate risk, and eight other villages were at low risk. The factors influencing the household number were shown. After applying the GIS to dengue infection risk assessment, it was demonstrated that the GIS was an effective tool for dengue infection surveillance.

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