A Time Series Regression-based Model for Predicting the Spread of Dengue Disease

Abstract

Dengue is a viral disease, spread by the mosquito species Aedes aegypti. According to WHO, every year 100-400 million cases of dengue infection are reported worldwide. Dengue mosquito inhibits in tropical regions and proliferates in wet climate conditions. Since it is impossible to clean those regions from the mosquito completely, therefore an analysis of the relationship between different climatic factors and dengue spread is important to forecast the number of cases ahead so that precautionary measures can be taken beforehand to minimize the disease spread. Specifically, to predict the spread we employed two prominent time series models i.e. SARIMA and SARIMAX on the publicly available DengAI dataset. The performance of the models is evaluated by using Mean Absolute Error (MAE), achieving MAE scores of 27.39 and 25.52 on SARIMA and SARIMAX respectively, which reveals that our proposed methodology outperformed other existing machine learning methods.