Monitoring COVID-19 contagion growth

Abstract

We present a statistical model that can be employed to monitor the time evolution of the COVID-19 contagion curve and the associated reproduction rate. The model is a Poisson autoregression of the daily new observed cases and dynamically adapt its estimates to explain the evolution of contagion in terms of a short-term and long-term dependence of case counts, allowing for a comparative evaluation of health policy measures. We have applied the model to 2020 data from the countries most hit by the virus. Our empirical findings show that the proposed model describes the evolution of contagion dynamics and determines whether contagion growth can be affected by health policies. Based on our findings, we can draw two health policy conclusions that can be useful for all countries in the world. First, policy measures aimed at reducing contagion are very useful when contagion is at its peak to reduce the reproduction rate. Second, the contagion curve should be accurately monitored over time to apply policy measures that are cost-effective.

Keywords: COVID-19; Poisson autoregressive models; contagion models; reproduction number.

FIGURE 1

Observed new weekly infection counts in 2020, for Brazil, China, Italy, the United Kingdom, and the United States [Colour figure can be viewed at wileyonlinelibrary.com]

FIGURE 2

Evolution of the $\alpha$, $\beta$ and $\alpha +\beta$ parameters for European countries and the United States. Note that the light and dark gray areas correspond respectively to a government response stringency index ranging between 70% and 80% and a government response stringency index above 80%. The dashed line reports the sum of $\alpha$ and $\beta$ [Colour figure can be viewed at wileyonlinelibrary.com]

FIGURE 3

Evolution of the R _t parameter in Brazil, Italy, the United Kingdom, and the United States. Note that the light and dark gray areas correspond respectively to a government response stringency index ranging between 70% and 80% and a government response stringency index above 80% [Colour figure can be viewed at wileyonlinelibrary.com]