The synthetic control method is an empirical methodology forcausal inference using observational data. By observing thespread of COVID-19 throughout the world, we analyze the dataon the number of deaths and cases in different regions usingthe power of prediction, counterfactual analysis, and syntheticinterventions of the synthetic control and its extensions. Weobserve that the number of deaths and cases in different re-gions would have been much smaller had the lockdowns beenimposed earlier and had the re-openings been done later, es-pecially among indoor bars and restaurants. We also analyzethe speculated impact of herd immunity on the spread giventhe population of each region and show that lockdown policieshave a very strong impact on the spread regardless of the levelof prior infections. Our most up-to-date code, model, and data can be foundon github: https://github.com/niloofarbayat/COVID19-synthetic-control-analysis

中文翻译：

---

合成控制、合成干预和 COVID-19 传播：探索封锁措施和群体免疫的影响

综合控制方法是一种使用观测数据进行因果推断的经验方法。通过观察 COVID-19 在世界范围内的传播，我们利用综合控制及其扩展的预测能力、反事实分析和综合干预来分析不同地区的死亡人数和病例数数据。我们观察到，如果更早实施封锁并晚些重新开放，尤其是在室内酒吧和餐馆中，不同地区的死亡人数和病例数会少得多。我们还分析了考虑到每个地区人口的群体免疫对传播的推测影响，并表明无论先前的感染水平如何，封锁政策对传播都有非常大的影响。我们最新的代码、模型、