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.

中文翻译：

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监测 COVID-19 传染增长

我们提出了一个统计模型，可用于监测 COVID-19 传染曲线的时间演变和相关的繁殖率。该模型是每日新观察病例的泊松自回归，并动态调整其估计值，以根据病例数的短期和长期依赖性来解释传染病的演变，从而可以对卫生政策措施进行比较评估。我们已将该模型应用于受病毒影响最严重的国家的 2020 年数据。我们的实证研究结果表明，所提出的模型描述了传染动力学的演变，并确定了传染增长是否会受到卫生政策的影响。根据我们的研究结果，我们可以得出两个对世界所有国家都有用的卫生政策结论。第一的，当传染达到高峰时，旨在减少传染的政策措施对降低繁殖率非常有用。其次，应随着时间的推移准确监测传染曲线，以应用具有成本效益的政策措施。