Potential Use of Artificial Neural Networks in Prediction of Dengue Abundance in Agra District

Abstract

Abstract India being a sub-tropical country faces several diseases spread through mosquito vectors, one of which being Dengue. Dengue is classified as mosquito-borne viral infection causing severe illness and mortality in children and adults in Asian sub continent. There are an estimated 390 million infections annually and to reduce the impact of dengue among population in sub-tropical regions, a novel disease prediction model is required. The aim of this study is to estimate the dengue cases with clinical and environmental variables like rainfall, relative humidity and temperature from 2005-2018 using data available on the India meteorological department for the geographical location of Agra district, Uttar Pradesh, India. Datasets were represented as average minimum and maximum temperature, average annual rainfall in cm, relative humidity and number of reported dengue cases. In this paper, we examined the effectiveness of the artificial neural network (ANN) for dengue prediction for four major sites in Agra (Dayalbagh, Sikandra, Agra cantonment and Kamla Nagar) and prediction model was designed for the period of 2019 and compared with the observed values. The results show variation in the prediction model with respect to clinical and environmental variables corresponding to four different sites. The average error of the prediction model ranged between 27.3% and 84% respectively. Until now several approaches have been discovered for controlling mosquito vectors but their effectiveness is still questioned. Therefore developing prediction model for mosquito prevalent areas in India will help in better encounter with the disease, causing less mortality.