Within-host viral dynamics (together with Florence Débarre, Alan Perelson, Jérémie Guedj and François Blanquart)
We have studied the effect of different antiviral drugs and their potential effect on the within-host dynamics of SARS-CoV-2 if taking prophylactically. We find that with our model, already a reduced efficacy of 70-80% of a drug can successfully prevent or delay the establishment of a viral infection within a host.
Publication: Success of prophylactic antiviral therapy for SARS-CoV-2: Predicted critical efficacies and impact of different drug-specific mechanisms of action, PLoS Computational Biology, 2021 (view online)

Analysis of stochastic dynamics of early epidemics (together with Emmanuel Schertzer, François Blanquart and Florence Débarre)
We have gathered previous results on the establishment probability of a new epidemic outbreak and the description of a epidemic dynamics by the infection age distribution in the infected population. The latter results in a renewal equation, a solution of the McKendrick-von Foerster equation. We then use a combination of the establishment probability and the renewal equation to study the success of a mass testing effort in terms of the detection rate of infected individuals. We further apply the results to estimate the emergence time of new variants of SARS-CoV-2, e.g. as the variant B.1.1.7 first discovered in England, and approximate the variant cluster size at its detection. Lastly, we compute the minimal testing frequency to detect epidemic outbreaks before they exceed a certain size. 
Publication: The stochastic dynamics of early epidemics: probability of establishment, initial growth rate, and infection cluster size at first detection, Journal of the Royal Society Interface, 2021 (view online) 

A unifying limit equation to describe the epidemiological evolution of SARS-CoV-2 (together with the SMILE-group at Collège de France)
Starting from an individual-based model, we have derived a one-dimensional partial differential equation, the McKendrick-von Foerster equation, that can be used to describe any epidemiological model by its age distribution (the age of an infection within a host). Labeling the states according to the transitions of the epidemiological model (e.g. from susceptible to symptomatic infectious) then gives an accurate description of the population composition.
Publication: From individual-based epidemic models to McKendrick-von Foerster PDEs: A guide to modeling and inferring COVID-19 dynamics, 2020+ (preprint)

Years of life lost due to COVID-19 (together with Marius Rubo)
We have written a commentary to the published paper “COVID-19 — exploring the implications of long-term condition type and extent of multimorbidity on years of life lost: a modelling study” to clarify that the used measure “years of life lost” can not be taken at face value. The statistic is a biased measure in the sense that even a random death, i.e. a death without any specified reason, will result in 9 years of life lost by the standard life expectancy table. Therefore, the message that COVID-19 results in 13 years of life lost is misleading, which we clarify in our statement.
Publication: Years of life lost cannot always be taken at face value: Response to “COVID-19 — exploring the implications of long-term condition type and extent of multimorbidity on years of life lost: a modelling study”, Wellcome Open Research, 2022 (view online)

This research was funded by the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement PolyPath 844369.