Abstract: Mathematical modeling of infectious disease transmission played a crucial role in supporting public health during COVID-19 pandemic. This crisis called for the rapid development of models and their continual maintenance and adaptation to answer a growing variety of questions as the epidemiological situation and mitigation measures evolved. Integration of real-time data in these models remains an important challenge to address with the goal of improving the relevance and impact of their previsions.
In my research program, I propose a conceptual framework that combines artificial intelligence and mathematical modeling to enable the real-time generation, calibration and simulation of infectious disease models. Using a composition modeling approach, I will design new methods and algorithms for the automated update and exploration of model structures that will help to integrate new sources of data with time-dependent granularity. Software tools implementing these innovations, to be tested in the workflow of modelers, will improve reproducibility of results and will facilitate the co-construction of mathematical models and simulation scenarios, and their prospective validation, by modelers and knowledge users. My overarching goal is to build key technologies that will enhance the contribution
of mathematical modeling to public health practice, in particular for pandemic preparedness and response.

Keywords: Public health; Mathematical epidemiology; Artificial intelligence; Real-time modeling and simulation.

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