DefinePK

Abstract

Dengue fever remains a significant public health concern in many parts of the world and a considerable challenge to health authorities regarding timely intervention and resource allocation. Forecasting of dengue cases can be crucial in effective disease management and control. The study proposed an innovative method to predict the spread of dengue cases by combining geospatial mapping with the Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) algorithm. The study utilizes geospatial data, demographic factors, and historical dengue case records to build a comprehensive forecasting model. The SARIMAX algorithm integrates the temporal patterns of dengue incidence with relevant exogenous variables. The SARIMAX algorithm with geospatial mapping outperforms the traditional methods in forecasting dengue cases. Integrating spatial information allows for better identification of disease hotspots and potential risk areas, facilitating targeted intervention strategies. Incorporating exogenous variables enhances the model's accuracy and provides a more comprehensive understanding of the factors influencing dengue transmission dynamics. The developed application presents a promising approach to forecasting dengue cases, contributing to improved disease surveillance and public health management. The developed web application can assist health authorities in making informed decisions, allocating resources effectively, and implementing timely preventive measures to combat the spread of dengue fever