COMOKIT (CoVid19 Modeling Kit)

a modeling kit written in GAMA for analyzing and comparing interventions against the COVID-19 epidemic at the scale of a city

Read the reference paper:
Gaudou, B., Huynh, N. D., Philippon, D., Brugière, A., Chapuis, K., Taillandier, P., Larmande, P., and Drogoul, A. (2020) 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 . Front. Public Health. doi: 10.3389/fpubh.2020.563247
Read the position paper:
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.

Presentation

Public health policies implemented against the CoVid19 pandemic raise a number of questions.

The Institut de recherche pour le développement (IRD) in Vietnam has set up a multidisciplinary team of researchers from its research units UMMISCO, MIVEGEC and DIADE, assisted by colleagues from Thuyloi University, Can Tho University, INRAE and SPH-HKU, to design a realistic spatial computer model called COMOKIT, on the GAMA modeling and simulation platform.

With the support of ANRS (ANRS COV23 COMOKIT) and EDF R&D.

COMOKIT aims at supporting deciders in answering the most pressing of these questions using an integrated model that combines:

  • A sub-model of individual clinical dynamics and epidemiological status
  • A sub-model of direct transmission of the infection from agent to agent
  • A sub-model of environmental transmission through the built environment
  • A sub-model of policy and interventions design and implementation
  • An agenda-based model of people activities at a one-hour time step

Model

The base model represents the spread of COVID-19 and the impact of control policies at the scale of a commune (~ 10.000 inhabitants) using:

An Agent-Based approach

Each inhabitant is represented individually with his/her specific characteristics (age, sex, household), clinical state and daily activities based on a generated agenda

A Flexible Policy representation

An Authority agent can be programmed to apply public health control policies consisting of a combination of mitigation measures and interventions.

Numerous Realistic Scenarios

Each set of parameters (incl. the policies applied) represents a scenario, which can be explored by executing several simulations and compared against other scenarios in more elaborate experiments.

A High Portability

In most cases, only a file containing the built environment may be enough to build a simple model. More realistic scenarios will of course require more detailed datasets.

Team

The people behind the first version of COMOKIT

Alexis Drogoul

UMI 209, UMMISCO, IRD, Sorbonne Université, Bondy, France.

Patrick Taillandier

UR 875, MIAT, INRAE, Toulouse University, Castanet Tolosan, France.

Benoit Gaudou

UMI 209, UMMISCO, IRD, Sorbonne Université, Bondy, France. UMR 5505, IRIT, Université Toulouse 1 Capitole, Toulouse, France.

Marc Choisy

UMR 5290, MIVEGEC, IRD/CNRS/Univ. Montpellier, Montpellier, France.

Kevin Chapuis

UMI 209, UMMISCO, IRD, Sorbonne Université, Bondy, France. UMR 228, ESPACE-DEV, IRD, Montpellier, France.

Quang Nghi Huynh

UMI 209, UMMISCO, IRD, Sorbonne Université, Bondy, France. CICT, Can Tho University, Can Tho, Vietnam.

Ngoc Doanh Nguyen

UMI 209, UMMISCO, IRD, Sorbonne Université, Bondy, France. MSLab / WARM, Thuyloi University, Hanoi, Vietnam.

Damien Philippon

WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.

Arthur Brugière

UMI 209, UMMISCO, IRD, Sorbonne Université, Bondy, France.

Pierre Larmande

UMR 232, DIADE, IRD, Univ. Montpellier, Montpellier, France.

François Sempé

External consultant, EDF R&D, France.