Multi-Step Forecasting Method For Influenza Pandemic Using Long Short-Term Memory Model To Improve Public Health Conditions

Abstract

The flu spread is a major global public health problem that gets attention around the world because it can cause serious illness, cost a lot of money, and kill a lot of people every year. To avoid influenza-like disease (ILD) and run healthcare systems well, predicting when an influenza outbreak will happen is essential. Researchers have used Machine Learning (ML) techniques but have yet to find the best ways to see complicated, non-linear trends in sequential data about flu outbreaks. A Long Short-Term Memory (LSTM) and a Genetic Algorithm (GA) are used together in this study to show a new way to predict multi-step influenza breakouts. Every week, the study gathers information on ILD, which covers flu and other illnesses with signs similar to the flu. When measuring performance during busy times, the suggested model does much better than other advanced Machine Learning (ML) methods and Fully Connected Neural Networks (F-CNN).