Harnessing Machine Learning and Multi Agent Systems for Health Crisis Analysis in North Africa

Faculty of Sciences, University of Ibn Tofail, Kenitra, Morocco

Abstract

The COVID-19 pandemic has presented a significant global health challenge, including in Morocco. These actions had direct repercussions on the economy as well as essential institutions in society; however, there were also indirect effects from these changes. This article focuses on these indirect consequences on the environment’s sustainability. It demonstrates that the net effect has been good in terms of reduced carbon gases, oil exploration operations, and pollution. This study introduces a novel approach to predicting and simulating the pandemic’s dynamics in Morocco using machine learning and multi-agent system models. We collected and processed daily data on COVID-19 cases, deaths, and interventions in Morocco from March 2, 2020, to June 30, 2021. We developed and validated several machine learning models, including decision trees, random forests, and support vector machines, to predict daily COVID-19 cases and deaths. Additionally, we designed a multi-agent system model to simulate the interactions among individuals, social groups, and the government in response to the pandemic, using agent-based modelling and game theory. Our results indicate that the machine learning models achieved high accuracy and generalization performance, with an average R-squared value of 0.83 for the cases and 0.90 for the deaths. The multi-agent simulations reveal the complex dynamics and trade-offs among pandemic control measures, economic activity, and social welfare in Morocco, suggesting that a coordinated and adaptive approach is necessary to balance these factors. Our study contributes to the growing literature on using machine learning and multiagent systems for pandemic prediction and management, providing valuable insights and recommendations for policymakers and public health officials in Morocco and beyond.