Epidemic outbreak prediction using machine learning models

TL;DR

This paper presents a machine learning approach to predict epidemic outbreaks like influenza, hepatitis, and malaria in New York, using historical and non-clinical data to enable early alerts for authorities.

Contribution

It introduces a novel predictive model combining clinical and non-clinical data for epidemic forecasting and develops a portal for real-time outbreak alerts.

Findings

Accurately predicts epidemic cases 5 weeks in advance

Integrates diverse data sources for improved prediction

Provides a practical portal for outbreak alerts

Abstract

In today's world,the risk of emerging and re-emerging epidemics have increased.The recent advancement in healthcare technology has made it possible to predict an epidemic outbreak in a region.Early prediction of an epidemic outbreak greatly helps the authorities to be prepared with the necessary medications and logistics required to keep things in control. In this article, we try to predict the epidemic outbreak (influenza, hepatitis and malaria) for the state of New York, USA using machine and deep learning algorithms, and a portal has been created for the same which can alert the authorities and health care organizations of the region in case of an outbreak. The algorithm takes historical data to predict the possible number of cases for 5 weeks into the future. Non-clinical factors like google search trends,social media data and weather data have also been used to predict the probability of an outbreak.