Utilizing reinforcement learning and deep neural networks to optimize non-pharmaceutical COVID-19 interventions in Florida

Abstract

Under the umbrella of artificial intelligence is machine learning that allows a system to improve through experience without any explicit programs telling it to. It is able to find patterns in massive amounts of data from works, images, numbers, to statistics. One approach to machine learning is neural networks in which the computer learns to finish a task by analyzing training samples. Another approach used in this study is reinforcement learning which manipulates it environment to discover errors and rewards. 

    This study aimed developed a deep neural network and used reinforcement learning to develop a system that was able to predict whether the cases will increase or decrease, then using that information, was able to predict which actions would most effectively cause a decline in cases while keeping things like economy and education in mind for a better long term effect. These models were made based on Florida using eight different counties’ data including things like mobility, temperature, dates of government actions, etc. Based on this information, data exploration and feature engineering was conducted to add dimensions that would further the accuracy of the neural network. The reinforcement learning model’s actions consisted of first, a shutdown for about two months before reopening schools and allowing things to return to normal. Then interestingly the model decided to keep school operating in a hybrid model with some students going back to school while others continue to study remotely.