COMOKIT (1.0.1)

In the face of the COVID-19 pandemic, public health authorities around the world have experimented, in a short period of time, with various combinations of interventions at different scales. However, as the pandemic continues to progress, there is a growing need for tools and methodologies to quickly analyze the impact of these interventions and answer concrete questions regarding their effectiveness, range and temporality.

COMOKIT, the COVID-19 modeling kit, is such a tool. It is a computer model that allows intervention strategies to be explored in silico before their possible implementation phase. It can take into account important dimensions of policy actions, such as the heterogeneity of individual responses or the spatial aspect of containment strategies.

In COMOKIT, built using the agent-based modeling and simulation platform GAMA, the profiles, activities and interactions of people, person-to-person and environmental transmissions, individual clinical statuses, public health policies and interventions are explicitly represented and they all serve as a basis for describing the dynamics of the epidemic in a detailed and realistic representation of space.

Relying on sub-models that have been extensively tested, spatial and social data that can be collected easily and quickly, COMOKIT has been designed from the ground up to be generic, scalable and portable in a variety of social, epidemiological, economic, and geographical scenarios. As a consequence, it is highly configurable and extendable to new case studies.

Release Notes

An all-in-one distribution of COMOKIT V1.0.1 (comprising a Java Virtual Machine, GAMA 1.8.1 and COMOKIT itself), together with a comprehensive set of guidelines and documentation, allows first-time users to quickly experiment with the model and build their own extensions or applications in a matter of minutes. Advanced GAMA users can download and import the model from its GitHub repository as long as they run GAMA 1.8.1. Additional datasets are available in a separate repository.

Associated Publications

Drogoul, A., Taillandier, P., Gaudou, B., Choisy, M., Chapuis, K., Huynh, N. Q. , Nguyen, N. D., Philippon, D., Brugière, A., and Larmande, P. (2020) Designing social simulation to (seriously) support decision-making: COMOKIT, an agent-based modelling toolkit to analyze and compare the impacts of public health interventions against COVID-19 . Review of Artificial Societies and Social Simulation, 27th April 2020. https://rofasss.org/2020/04/27/comokit/