Influenza Forecasting

Abstract

In the 2018-19 influenza season, between 37.4 and 42.9 million people in the United States experienced flu like symptoms. From that number, 431 to 647 thousand were hospitalized and 36400 to 61200 (most of them children and seniors) succumbed to the disease. Due to the annual mutation of the very many strands of the flu virus, new vaccines must be developed and administered every flu season. Therefore, the prediction of the rate of growth in reported infection cases of each strand of the flu is paramount to ensuring the correct supply of vaccines per strand. A great tool for making future predictions using existing data is Machine learning - specifically Neural Networks. eVision (Epidemic Vision) is a software using Long Short-Term Memory (LSTM) neural networks under research and development by Santa Clara University's EPIC (Ethical, Pragmatic, and Intelligent Computing) and Bioinnovation & Design labs to predict the trend of influenza cases throughout the flu season using data from the CDC, WHO, and Google Trends in order to help pharmaceuticals decide on the ramping up or down of their development of tester kits, vaccines, and medicines weeks in advance.