Visualization and Geo Mapping of Dengue Cases Using Time Series Forecasting

Accurate and timely reporting of dengue cases is essential for the Department of Health (DOH) and Municipal Health Office (MHO) to monitor and respond to outbreaks effectively to help track the spread of the disease and deploy resources where they are most needed. Strengthening surveillance system, ensures real time data collection and analysis, with enhanced epidemiological surveillance units, improving the monitoring of dengue trends and outbreaks. This study developed a system for Visualization and Geo Mapping of Dengue Cases using Time Series Forecasting. Time Series is a certain sequence of data observations that a system collects within specific periods of time (daily, monthly, or yearly). The aim of this study is to design an effective and user-friendly surveillance system that will accurately monitor and control the dengue outbreak in a timely manner. Time series forecasting models were usually used to predict high and low dengue incidence as accurate prediction may provide timely forewarnings, and predictions are made from historical data available. The forecasting feature of the system used time series algorithm to predict future dengue cases, specifically random forest regressor. The visual summary of information has been a great support in showing the concentration of the cases to help in determining high risk areas. With the addition of forecasting, it helped in detecting emerging infections and aid in the decision making and prompted the government unit responsible in taking actions for enhanced and strengthened public health efforts of the government.