| Popis: |
We present a statistical model which can be employed to monitor the time evolution of the COVID-19 contagion curve, and of the associated reproduction rate R0. The model is a Poisson autoregression of the daily new observed cases and can, differently from classical exponential growth models, dynamically adapt its estimates to explain the evolution of contagion in different time periods and locations, allowing for the comparative evaluation of health policy measures. We have applied the model to the first two months of data from the countries most hit by the virus. Our empirical findings show that the proposed model is able to identify where a country lies on the contagion curve, at each point in time: behind a peak, on a peak, and after a peak. Based on this positioning, we draw three main health policy conclusions that can be useful for all countries in the world. First, countries that are still behind the peak (e.g. France and the United Kingdom) should maintain strong containment measures, such as diffuse testing and lockdown. Second, countries that are on the peak and have experienced a steep contagion growth (e.g. Italy, Spain and the United States), could partially relax containment measures but must couple them with continuous monitoring of the growth curve. Third, in countries that have experienced a less steep contagion growth (e.g. Germany) the approach to restrictive measures could be more cautious since there is a risk that social costs outweigh the benefits. Last, countries that have passed the peak (e.g. China) should relax containment measures, but continue statistical monitoring. |
