Within-host viral dynamics (together with virologists)
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.
Predicted success of prophylactic antiviral therapy to block or delay SARS-CoV-2 infection depends on the drug’s mechanism of action (preprint)
A unifying limit equation to describe the epidemiological evolution of SARS-CoV-2 (together with the SMILE-group at Collège de France)
We have derived a one-dimensional partial differential 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.
From individual-based epidemic models to McKendrick-von Foerster PDEs: A guide to modeling and inferring COVID-19 dynamics (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.
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” (preprint)