Identifying policy challenges of COVID-19 in hardly reliable data and judging the success of lockdown measures
Identifying policy challenges of COVID-19 in hardly reliable data and judging the success of lockdown measures
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DOI：
10.1007/s00148-020-00799-x
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发表时间：
2020-08-26
影响因子：
6.1
通讯作者：
Patriarca, Fabrizio
中科院分区：
文献类型：
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作者：
Bonacini, Luca;Gallo, Giovanni;Patriarca, Fabrizio
识别 COVID-19 传染动态的结构性中断对于及时评估政策和评估封锁措施的有效性至关重要。然而，官方数据记录的感染情况是在严重且不可预测的延迟之后发生的。此外，人们对病毒的健康风险做出反应，并预期封锁。所有这些使得快速、准确地检测病毒感染动态的变化模式变得复杂。我们提出了一种机器学习程序来识别 COVID-19 病例时间序列中的结构性中断。我们以意大利这个早期受影响的国家为例，对这种情况没有做好准备，并检测三次国家封锁引发的结构性断裂的日期，以评估其影响并确定一些相关的政策问题。第一次封锁的强烈但显着延迟的影响表明了相关的公告效应。相比之下，上次封锁的影响要小得多。所提出的方法作为早期检测结构性破坏的实时程序是稳健的：前两次封锁的影响可以在实际发生的第二天就被正确识别。
Identifying structural breaks in the dynamics of COVID-19 contagion is crucial to promptly assess policies and evaluate the effectiveness of lockdown measures. However, official data record infections after a critical and unpredictable delay. Moreover, people react to the health risks of the virus and also anticipate lockdowns. All of this makes it complex to quickly and accurately detect changing patterns in the virus's infection dynamic. We propose a machine learning procedure to identify structural breaks in the time series of COVID-19 cases. We consider the case of Italy, an early-affected country that was unprepared for the situation, and detect the dates of structural breaks induced by three national lockdowns so as to evaluate their effects and identify some related policy issues. The strong but significantly delayed effect of the first lockdown suggests a relevant announcement effect. In contrast, the last lockdown had significantly less impact. The proposed methodology is robust as a real-time procedure for early detection of the structural breaks: the impact of the first two lockdowns could have been correctly identified just the day after they actually occurred.