Monitoring and forecasting the SARS-Covid-19 pandemic in France (preprint)

ABSTRACT

Over the past year, 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 SARS-Covid-19 pandemic evolution. With the advent of data-sharing, computational approaches have gained a key role in evaluating future scenarios and offering predictions in a constantly evolving social environment. To design optimal social, hospitalization and economical strategies that guarantee control over the pandemic progression, we developed a data-driven modelling framework with the aim to provide reliable near future predictions under constantly evolving social and pandemic events. The framework is flexible enough to be used at a single hospital, regional or national level. We used a variety of data such as social, serological, testing and clinical data to compute the infection dynamics and the hospital workload for France. We developed inference methods to calibrate model parameters from observed hospitalization statistics over adjustable time periods. We applied our model to study the age stratified pandemic evolution inside and outside hospitals until February 2021, and the competition between vaccinations and the novel delta variant. We obtained several predictions about hidden pandemic properties such as fractions of infected, infection hospitality and infection fatality ratios. We show that reproduction numbers and herd immunity levels are not universal but strongly depend on the underlying social dynamics. We find that with normal social interactions the present vaccination status and rate is not sufficient to prevent a new pandemic wave driven by the delta variant.