COMOKIT: A Modeling Kit to Understand, Analyze, and Compare the Impacts of Mitigation Policies Against the COVID-19 Epidemic at the Scale of a City

Benoit Gaudou (1, 2, 3, 4) , Nghi Quang Huynh (1, 3, 5) , Damien Philippon (6, 7) , Arthur Brugière (4) , Kevin Chapuis (1, 8) , Patrick Taillandier (9) , Pierre Larmande (10, 4) , Alexis Drogoul (1)

1 UMMISCO - Unité de modélisation mathématique et informatique des systèmes complexes [Bondy]
2 IRIT-SMAC - Systèmes Multi-Agents Coopératifs
3 IRD - Institut de Recherche pour le Développement
4 USTH - University of Science and Technology of Hanoi
5 CICT - College of Information and Communication Technology
6 HKU - The University of Hong Kong
7 HKU Med - School of Public Health, Li Ka Shing Faculty of Medicine
8 IMAGES-Espace DEV - Institut de Modélisation et d'Analyses en géo-environnement et santé - Espace Développement
9 MIAT INRAE - Unité de Mathématiques et Informatique Appliquées de Toulouse
10 UMR DIADE - Diversité, adaptation, développement des plantes

Since its emergence in China, the COVID-19 pandemic has spread rapidly around the world. Faced with this unknown disease, public health authorities were forced to experiment, in a short period of time, with various combinations of interventions at different scales. However, as the pandemic progresses, there is an urgent need for tools and methodologies to quickly analyze the effectiveness of responses against COVID-19 in different communities and contexts. In this perspective, computer modeling appears to be an invaluable lever as it allows for the in silico exploration of a range of intervention strategies prior to the potential field implementation phase. More specifically, we argue that, in order to take into account important dimensions of policy actions, such as the heterogeneity of the individual response or the spatial aspect of containment strategies, the branch of computer modeling known as agent-based modeling is of immense interest. We present in this paper an agent-based modeling framework called COVID-19 Modeling Kit (COMOKIT), designed to be generic, scalable and thus portable in a variety of social and geographical contexts. COMOKIT combines models of person-to-person and environmental transmission, a model of individual epidemiological status evolution, an agenda-based 1-h time step model of human mobility, and an intervention model. It is designed to be modular and flexible enough to allow modelers and users to represent different strategies and study their impacts in multiple social, epidemiological or economic scenarios. Several large-scale experiments are analyzed in this paper and allow us to show the potentialities of COMOKIT in terms of analysis and comparison of the impacts of public health policies in a realistic case study.

Domaines: Modélisation et simulation; Système multi-agents [cs.MA]; Intelligence artificielle [cs.AI]

Benoit Gaudou, Nghi Quang Huynh, Damien Philippon, Arthur Brugière, Kevin Chapuis, et al.. COMOKIT: A Modeling Kit to Understand, Analyze, and Compare the Impacts of Mitigation Policies Against the COVID-19 Epidemic at the Scale of a City. Frontiers in Public Health, 2020, 8, pp.563247. ⟨10.3389/fpubh.2020.563247⟩. ⟨hal-02968260⟩