Comparative Analysis of Epidemic Alert System using Machine Learning for Dengue and Chikungunya

Abstract

The Rapid spread of a disease is known as an epidemic. The catastrophe brought by an epidemic not only effects the people of an area, but also brings about a lot of distress in every sector of social strata. An epidemic alerting system has a potential to carve the path how medical surveillance could become more efficient. The epidemic causing diseases are usually vector borne. The diseases are spread by pathogens present in these vectors. An epidemic alerting system could predict how the weather conditions and several other factors effect the growth and propagation of these vectors. The weather conditions could be predicted using the high-end instruments and satellites currently available. Using this prediction, we could forecast the next targets of the epidemic. To implement this epidemic alert system, four algorithms are used namely Random Forest Regression, Decision Tree Regression, Support Vector Regression and Multiple Linear Regression. For dengue, the state wise cases data of the year 2013 to 2017 has been used in the system while for chikungunya the data used is of the year 2013 to 2016. This dataset has been downloaded from a government website, i.e., https://www.data.gov.in/. For the case of dengue, the model has been trained on the data of the year 2013 to 2016 and predictions of the year 2017 have been done. On the other hand, the model has been trained on the data of the year 2013 to 2015 and predictions for the year 2017 have been made regarding Chikungunya. At last, a contrastive analysis has been made on the four algorithms used for both the diseases.