Monitoring and forecasting the SARS-Covid-19 pandemic in France

Before the availability of vaccines, many countries have resorted multiple times to drastic social restrictions to prevent saturation of their health care system, and to regain control over an otherwise exponentially increasing COVID-19 pandemic. With the advent of data-sharing, computational approaches are key to efficiently control a pandemic with non-pharmaceutical interventions (NPIs). Here we develop a data-driven computational framework based on a time discrete and age-stratified compartmental model to control a pandemic evolution inside and outside hospitals in a constantly changing environment with NPIs. Besides the calendrical time, we introduce a second time-scale for the infection history, which allows for non-exponential transition probabilities. We develop inference methods and feedback procedures to successively recalibrate model parameters as new data becomes available. As a showcase, we calibrate the framework to study the pandemic evolution inside and outside hospitals in France until February 2021. We combine national hospitalization statistics from governmental websites with clinical data from a single hospital to calibrate hospitalization parameters. We infer changes in social contact matrices as a function of NPIs from positive testing and new hospitalization data. We use simulations to infer hidden pandemic properties such as the fraction of infected population, the hospitalisation probability, or the infection fatality ratio. We show how reproduction numbers and herd immunity levels depend on the underlying social dynamics.

Mean time to infection by small diffusing droplets containing SARS-CoV-2 during close social contacts

Airborne viruses such as SARS-CoV-2 are partly spreading through aerosols containing viral particles. Inhalation of infectious airborne particles can lead to infection, a route that can even be more predominant compared with droplet or contact transmission. To study the transmission between a susceptible and an infected person, we estimate the distribution of arrival times of small diffusing aerosol particles to the inhaled region located below the nose until the number of particles reaches a critical threshold. Our results suggest that although contamination by continuous respiration can take around 90 minutes at a distance of one meter, it is reduced to a few minutes when coughing or sneezing. Interestingly, there is not much differences between outdoors and indoors when the air is still. When a window is open inside an office, the infection time is reduced. Finally, wearing a mask leads to a delay in the time to infection. To conclude, diffusion analysis provides several key time scale of viral airborne transmission.

Monitoring and predicting SARS-CoV-2 epidemic in France after deconfinement using a multiscale age-stratified rate model

The entire world and France were strongly impacted by the SARS-COV-2 epidemic. Finding appropriate measures that effectively contain the spread of the epidemic without putting a too severe pressure on social and economic life is a major challenge for modern predictive approaches. To assess the impact of confinement (March 17th till May 11th) and deconfinement, we develop a novel rate model to monitor and predict the spread of the epidemic and its impact on the health care system. The model accounts for age-dependent interactions between population groups and predicts consequences for various infection categories such as number of infected, hospitalized, load of intensive care units (ICU), number of death, recovered from hospitalization and more. We use online health care data for the five most infected regions of France to calibrate the model. At day of deconfinement (May 11th), we find that 13% (around 4.8M) of the population is infected in the five most affected regions of France (extrapolating to 5.8M for France). The model predicts that if the reproduction number R0 is reduced by at least a factor of 2.5-3 for all age groups after deconfinement, which could be achieved by wearing masks and social distancing, a significant second peak can be prevented. However, if the reduction in R0 for the age group 0-25 would be less and below 2 (e.g. due to school openings), a second peak with ICU saturation is unavoidable. In that context, we argue that testing should be focused on children, but without tracing it will nevertheless have only a very limited impact on reducing the spread.