Identification and estimation of the SEIRD epidemic model for COVID-19
Identification and estimation of the SEIRD epidemic model for COVID-19
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DOI：
10.1016/j.jeconom.2020.07.038
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发表时间：
2021-01-01
影响因子：
6.3
通讯作者：
Korolev, Ivan
中科院分区：
文献类型：
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作者：
Korolev, Ivan
本文研究了COVID-19的SEIRD流行病模型。首先，我证明了该模型从观察到的死亡和确诊病例数中识别出来的效果很差。有许多套参数在短期内观测上是等效的，但会导致明显不同的长期预测。其次，我表明，基本的再生数R-0可以确定的数据，条件流行病学参数，并提出了几个非线性SUR方法来估计R-0。我检查这些方法使用Monte Carlo研究的性能，并证明他们产生相当准确的估计R-0。接下来，我应用这些方法来估计美国、加州和日本的R-0，并记录各地区R-0值的异质性。我的估计方法说明了可能少报的病例数。我证明，如果不考虑漏报和估计R-0从报告的病例数据，由此产生的估计R-0可能会向下倾斜，由此产生的预测可能会夸大长期的死亡人数。最后，我讨论了如何从随机测试的辅助信息可以用来校准模型的初始参数，缩小范围的可能预测未来的死亡人数。(C)2020爱思唯尔B. V.保留所有权利。
This paper studies the SEIRD epidemic model for COVID-19. First, I show that the model is poorly identified from the observed number of deaths and confirmed cases. There are many sets of parameters that are observationally equivalent in the short run but lead to markedly different long run forecasts. Second, I show that the basic reproduction number R-0 can be identified from the data, conditional on epidemiologic parameters, and propose several nonlinear SUR approaches to estimate R-0. I examine the performance of these methods using Monte Carlo studies and demonstrate that they yield fairly accurate estimates of R-0. Next, I apply these methods to estimate R-0 for the US, California, and Japan, and document heterogeneity in the value of R-0 across regions. My estimation approach accounts for possible underreporting of the number of cases. I demonstrate that if one fails to take underreporting into account and estimates R-0 from the reported cases data, the resulting estimate of R-0 may be biased downward and the resulting forecasts may exaggerate the long run number of deaths. Finally, I discuss how auxiliary information from random tests can be used to calibrate the initial parameters of the model and narrow down the range of possible forecasts of the future number of deaths. (C) 2020 Elsevier B.V. All rights reserved.