Using Machine Learning to drive social learning in a Covid-19 Agent-Based Model

Abstract

Abstract. Disease transmission and governmental interventions influence the spread of Covid-19. Models can be essential tools to optimise these governmental interventions. This requires the exploration of various ways to implement government agent behaviour. In Agent-Based Models (ABMs), government agent behaviour can be rule-based or data-driven, and the agent can be an isolated learner (using only its own data) or a social learner. We explore the creation of a data-driven social approach in which behaviour is based on a Machine Learning (ML) algorithm, and the government considers data from other European countries as input for their decision-making. Governmental actions start with risk perception, based on several parameters, e.g. the number of disease cases, deaths, and hospitalisation rate. The interventions are measured via the stringency index, measuring the simultaneous number of interventions (working from home, wearing a facemask, closing schools, etc.) taken. We test four machine learning algorithms (Bayesian Network (BN), c4.5, Naïve Bayes (NB) and Random Forest (RF)), using a 5-class and a 3-class classification of the stringency level. The algorithms are trained on disease data from many European countries. The best-performing algorithms were c4.5 and RF. The next step is to implement these algorithms into the ABM and evaluate the outcomes compared to the original model.