Welcome to the website of the DIFAIRE team --- an interdisciplinary scientific initiative dedicated to studying the mechanisms driving the spread of infectious diseases. By combining immuno-epidemiological modeling with simulations of collective behavior, DIFAIRE adopts an integrated, multiscale approach to analyze, anticipate, and contain epidemic dynamics.
The team operates under a Collaborative Research Program Agreement signed between INRIA and École Centrale Casablanca. Its work focuses on analyzing social interactions, contact dynamics, host--vector relationships, and viral diffusion mechanisms. This original positioning anchors DIFAIRE within a systemic vision of public health, at the crossroads of mathematical modeling, applied epidemiology, and social dynamics.
The associated team DIFAIRE stems from a scientific collaboration bringing together researchers from several institutions: INRIA, École Centrale Casablanca (Center for Research on Complex Systems and Interactions), École Mohammedia des Ingénieurs (LERMA), and LAMAI at the Faculty of Sciences and Technology of Cadi Ayyad University.
Open to all interested stakeholders, researchers, students, and institutions, this initiative is firmly rooted in a spirit of openness, collaboration, and interdisciplinary exchange. Contact us to learn more.
Presentation
Context
The modeling of infectious diseases is often fragmented, focusing on specific aspects such as immune response, contact dynamics, or infection dynamics. However, for a complete and comprehensive understanding of this phenomenon, it is essential to go beyond the limitations of different modeling scales and perspectives. An integrated approach is necessary to bring together the various facets coherently within a well-defined conceptual framework. This approach, which must account for the inherent complexity of infectious disease modeling, must combine these multiple perspectives to obtain a more complete and comprehensive view of disease propagation. In particular, it is necessary to track the evolution of the pathogen in infected individuals. Indeed, the pathogen (including viruses, bacteria, prions, and fungi) is in continuous dynamic interaction with the immune system of the infected person. The probability of disease transmission or death depends on the quantity of pathogen present in the body and the intensity of the immune system response. Disease spread in the population depends, among other things, on all these individual characteristics. To better understand epidemiological processes (between individuals), it is necessary to understand the immunological processes within the individual and the links between them. Although the majority of existing models focus on population dynamics, it is important to connect the multiple scales of disease to better understand its spread.
By combining the previous work of both teams, covering compartmental models, immune response, cellular dynamics, and individual interaction at different scales (see partner publications), we obtain a set of tools that allow us to move toward more ambitious multi-scale models that enable a broader view of infectious disease propagation and a deeper understanding of the underlying mechanisms. Overall, these research efforts come together to create a comprehensive and coherent framework that allows us to better understand the spread of infectious diseases in an integrated manner across different scales.
Objectives
The main objectives of the project are organized into three major areas:
- Develop an immuno-epidemiological model that will trace the concept of the "virtual patient." This model will be designed to study the dynamics of immune response to various external stimuli, encompassing demographic, health, and other characteristics, with a view to gaining a better understanding of immune system physiology and its defense mechanisms. Based on the "virtual patient" concept, we will be able to formulate a structured physiological model to represent the epidemiological dynamics of a disease. For example, the physiological variable could be the immune status of individuals or the time elapsed since infection. The model could also account for direct and indirect pathogen transmission pathways (as with bacteria, for example), as well as the role of the environment in disease dynamics. The coupling of immunological and epidemiological models will consist of linking epidemiological parameters to immunological variables, such as pathogen load and immune response.
- Develop a contact dynamics simulator, integrating interactions at different scales, including microscopic, mesoscopic, and macroscopic levels. This simulator will also account for the specific characteristics of each population and social context (activity locations).
- Establish an intelligent surveillance system by combining the immuno-epidemiological model with the agent mobility model , and with a macroscopic model of infection dynamics (compartmental model) enriched by the results of points 1) and 2). This combination will enable the creation of a synthetic population in a virtual environment, facilitating more responsive intervention in the event of an emerging epidemic or pandemic. It will also allow exploration of different scenarios based on population characteristics, offering a proactive approach to managing epidemic situations.