Early Detection of Dengue Fever Susceptibility using Climate Parameters and Larval Density
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Dengue infection remains a vital public health challenge in Indonesia, especially in Pontianak City, marked by fluctuating case numbers and unpredictable transmission patterns influenced by climate and environmental factors. Current prevention efforts are often hampered by the lack of precise, integrated early warning information about regional vulnerability. This study aims to develop and evaluate a predictive model for dengue infection vulnerability by incorporating climate factors (rainfall intensity, humidity), population density, and larval-free index (LFI) to establish an accurate early warning system. An observational analytic study with a cross-sectional design was carried out using data from 2022 to 2024 in Pontianak. The study examined monthly rainfall intensity, relative humidity, population density, and LFI as predictors for dengue incidence. Data were analyzed using univariate, bivariate, and multivariate logistic regression analysis to assess regional vulnerability. The results show that rainfall intensity (p= 0.025; AOR= 2.097; 1.100 - 3.398) and LFI (p= 0.042; AOR= 1.102; 1.100 - 3.398) were the predictor of dengue risk. The prediction model can support stakeholders and the community in implementing timely and targeted prevention strategies to lessen the burden of dengue infection
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