Epidemic outbreak prediction using machine learning models

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 organisations 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. Keywords: Epidemic, Clinical analysis, LSTM, ARIMA Introduction More than six different influenza pandemics and epidemics have struck in just a century. Every year nearly 500,000 people die due to seasonal influenza and other epidemics. Even with the advancement of technology, particularly in the healthcare industry, it is impossible to prevent an outbreak, but it's possible to be prepared for one. With the help of machine learning, we can now monitor and forecast the expected number of cases of a given disease for a particular region by using meteorological data, social media data, and historical data. This would be extremely useful for the health care centres and pharmacies of a particular region to be prepared in advance and stock up their inventory if needed. As seen in India during the second wave of COVID-19, due to the suddenness of the outbreak there was an unprecedented demand in healthcare resources from medicines, beds, etc. Such unexpected epidemic or pandemic outbreaks threaten to overwhelm the healthcare system of any region. But knowing the possibility of an outbreak beforehand helps the healthcare system to be prepared in advance, as it gives them enough time to accumulate the necessary items like medicines, oxygen cylinders, etc.