Predictive Analysis of Chikungunya

TL;DR

This paper analyzes DARPA's dataset on chikungunya to forecast incidence rates using weather features and geographic data, employing linear regression for prediction and accuracy assessment.

Contribution

It extends existing datasets with weather and location features and applies linear regression for chikungunya incidence prediction.

Findings

Weather features improve prediction accuracy

Linear regression provides a reliable forecast model

Extended dataset enhances understanding of disease spread

Abstract

Chikungunya is an emerging threat for health security all over the world which is spreading very fast. Researches for proper forecasting of the incidence rate of chikungunya has been going on in many places in which DARPA has done a very extensive summarized result from 2014 to 2017 with the data of suspected cases, confirmed cases, deaths, population and incidence rate in different countries. In this project, we have analysed the dataset from DARPA and extended it to predict the incidence rate using different features of weather like temperature, humidity, dewiness, wind and pressure along with the latitude and longitude of every country. We had to use different APIs to find out these extra features from 2014-2016. After creating a pure dataset, we have used Linear Regression to predict the incidence rate and calculated the accuracy and error rate.