Background: It is unclear which variables contribute to the variance in Covid-19 related deaths and Covid-19 cases. Method: We modelled the relationship of various predictors (health systems variables, population and population health indicators) together with variables indicating public health measures (school closures, border closures, country lockdown) in 40 European and other countries, using Generalized Linear Models and minimized information criteria to select the best fitting and most parsimonious models. Results: We fitted two models with log-linearly linked variables on gamma-distributed outome variables (CoV2 cases and Covid-19 related deaths, standardized on population). Population standardized cases were best predicted by number of tests, life-expectancy in a country, and border closure (negative predictor, i.e. preventive). Population standardized deaths were best predicted by time, the virus had been in the country, life expectancy, smoking (negative predictor, i.e. preventive), and school closures (positive predictor, i.e. accelerating). Model fit statistics and model adequacy were good. Discussion and Interpretation: Interestingly, none of the variables that code for the preparedness of the medical system, for health status or other population parameters were predictive. Of the public health variables only border closure had the potential of preventing cases and none were predictors for preventing deaths. School closures, likely as a proxy for social distancing, was associated with increased deaths. Conclusion: The pandemic seems to run its autonomous course and only border closure has the potential to prevent cases. None of the contributes to preventing deaths.
Collection : COVID-19 SARS-CoV-2 preprints from medRxiv and bioRxiv