COVID-19 with uncertain phases: estimation issues with an illustration for Argentina

We use an approach to assess COVID-19 performance that starts from what we consider is the most likely set of hypotheses about the uncertain evolution of the pandemic, that envisage a sequence of different cycles with unknown duration and magnitude over 18-24 months. This pattern implies a research strategy where short-term time series forecasting of the evolution of observed cases and deaths play a central role in both detecting transitions from phase to phase of infections and the estimation of necessarily changing structural parameters and indicators of a SIRD model. We illustrate our approach with Buenos Aires City performance, which represents a significant share of the Argentine case with an early introduction of a lockdown followed by a second wave latter on. This approach can be extended to include measures of the intensity and compliance of lockdowns, as well as the heterogeneity across areas. We find that mobility (as a proxy for the effectiveness of the lockdown) has an impact on observed cases in Buenos Aires City with a lag of 8 days and deaths relate with new cases registered 16 to 19 days before. Mobility has a clear impact on the growth rate of cases and by extension deaths. Our approach and results have implications for policy dialogue issues.