[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119755-en":3,"doc-seo-119755-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119755,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Harnessing Machine Learning and Multi Agent Systems for Health Crisis Analysis in North Africa","The COVID-19 pandemic created major public health strain in Morocco, reshaping society through both direct outcomes and indirect consequences reaching economic systems, institutions, and environmental sustainability. The study proposes a data-driven framework combining machine learning and multi-agent system modeling to predict and simulate pandemic dynamics in Morocco. Daily data on confirmed cases, deaths, and interventions from March 2, 2020 to June 30, 2021 inform decision trees, random forests, and support vector machines, while agent-based simulation and game theory represent interactions between individuals, social groups, and government. Results show strong predictive accuracy and reveal policy trade-offs across control measures, economic activity, and social welfare.","Harnessing Machine Learning and Multi Agent Systems for Health Crisis Analysis in North Africa  \nETTAZI Haitam 1 , RAFALIA Najat 1 , ABOUCHABAKA Jaafar1 1Faculty of Sciences, University of Ibn Tofail, Kenitra, Morocco  \nAbstract. The COVID-19 pandemic has presented a significant global  \nhealth challenge, including in Morocco. These actions had direct  \nrepercussions on the economy as well as essential institutions in society;  \nhowever, there were also indirect effects from these changes. This article  \nfocuses on these indirect consequences on the environment's sustainability.  \nIt demonstrates that the net effect has been good in terms of reduced  \ncarbon gases, oil exploration operations, and pollution. This study  \nintroduces a novel approach to predicting and simulating the pandemic's  \ndynamics in Morocco using machine learning and multi-agent system  \nmodels. We collected and processed daily data on COVID-19 cases,  \ndeaths, and interventions in Morocco from March 2, 2020, to June 30,  \n2021. We developed and validated several machine learning models,  \nincluding decision trees, random forests, and support vector machines, to  \npredict daily COVID-19 cases and deaths. Additionally, we designed a  \nmulti-agent system model to simulate the interactions among individuals,  \nsocial groups, and the government in response to the pandemic, using  \nagent-based modelling and game theory. Our results indicate that the  \nmachine learning models achieved high accuracy and generalization  \nperformance, with an average R-squared value of 0.83 for the cases and  \n0.90 for the deaths. The multi-agent simulations reveal the complex  \ndynamics and trade-offs among pandemic control measures, economic  \nactivity, and social welfare in Morocco, suggesting that a coordinated and  \nadaptive approach is necessary to balance these factors. Our study  \ncontributes to the growing literature on using machine learning and multi  \nagent systems for pandemic prediction and management, providing  \nvaluable insights and recommendations for policymakers and public health  \nofficials in Morocco and beyond.  \nIndex Terms— COVID-19 analysis; Environment's sustainability; data  \nanalysis; multi-agent System architecture; Angular; Spring Boot  \n1 Introduction  \nThe COVID-19 pandemic has posed unprecedented challenges to public health systems worldwide, with millions of cases and deaths reported globally. Various interventions and policies, such as social distancing measures, lockdowns, and vaccination campaigns, have been implemented to mitigate the pandemic's impact. However, their effectiveness and trade-offs depend on multiple factors, including the transmission rate, population compliance, and health system capacity.  \nA lot of the aforementioned consequences have resulted from the lack or decline of particular activities, such as transportation, which supplies considerably to greenhouse gas emissions on a daily basis. Nevertheless, the efforts in healthcare as an outcome of the  \n© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \nCOVID-19 pandemic have also had an impact on the environment, and not always in a favorable way [16].The rising need for tools, together with problems with waste disposal, can be viewed negatively.  \nThis study aims to compare the effectiveness of two pandemic modeling approaches in predicting and simulating the dynamics of COVID-19. One approach employs a multiagent system (MAS) model, simulating interactions and feedback loops among different agents, such as individuals, social groups, and policymakers, to capture the system's emergent behavior and dynamics. The other approach relies on traditional statistical and machine learning models to predict the pandemic's spread.  \nThe primary objective of this study is to develop and valida","cbCaitpZftHVOTjQ","https://ap.wps.com/l/cbCaitpZftHVOTjQ","pdf",1537271,1,19,"English","en",105,"# Introduction\n## Related works\n## Methodology\n## Machine learning models\n## Multi-agent system simulation\n## Results and evaluation\n## Discussion and recommendations","[{\"question\":\"What data does the study use to model COVID-19 dynamics in Morocco?\",\"answer\":\"The study collects and processes daily data on COVID-19 cases, deaths, and interventions in Morocco from March 2, 2020 to June 30, 2021.\"},{\"question\":\"Which machine learning models are developed for daily prediction?\",\"answer\":\"It develops and validates decision trees, random forests, and support vector machines to predict daily COVID-19 cases and deaths.\"},{\"question\":\"How does the multi-agent system help analyze pandemic control measures?\",\"answer\":\"The multi-agent model simulates interactions among individuals, social groups, and government using agent-based modelling and game theory, revealing trade-offs between control measures, economic activity, and social welfare.\"}]","Harnessing Machine Learning and Multi Agent Systems for Health Crisis Analysis in North Africa | 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data does the study use to model COVID-19 dynamics in Morocco?","Question",{"text":75,"@type":76},"The study collects and processes daily data on COVID-19 cases, deaths, and interventions in Morocco from March 2, 2020 to June 30, 2021.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are developed for daily prediction?",{"text":80,"@type":76},"It develops and validates decision trees, random forests, and support vector machines to predict daily COVID-19 cases and deaths.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the multi-agent system help analyze pandemic control measures?",{"text":84,"@type":76},"The multi-agent model simulates interactions among individuals, social groups, and government using agent-based modelling and game theory, revealing trade-offs between control measures, economic activity, and social 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