评估菲律宾Covid-19政策的有效性

Lim Chris
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引用次数: 0

摘要

本研究通过计算政府政策实施后的区域和国家有效再生产数(Rt),评估了菲律宾政府新冠肺炎政策的有效性。Rt是通过使用具有贝叶斯框架的模型获得的,贝叶斯框架是一种基于现有证据创建概率模型的方法。这个被称为“EpiEstim”的模型通过分析预定时间段内的每日病例发生率来提供估计。通过分析2020年2月6日至2021年3月3日期间政府严格程度指数(OxCGRT)与全国Rt估计值之间的双变量关系,确定严格程度对全国传播率的影响。这项研究是相关的,因为菲律宾政府对COVID-19的反应是已知的最长期和最严格的大流行方法之一,对政府政策有效性的定量衡量可以帮助确定这些方法是否可以减轻未来疫情的影响。从区域来看,政府政策的实施导致随后的Rt值在16个测试日期中的10个中大约返回到1.0的临界阈值。这些Rt值在0.82和3.05之间。在全国范围内,严格程度与传播率呈负相关,最明显的是在2020年3月6日至2020年4月6日期间。政府的许多政策可以被视为延迟反应,因为它们是在Rt首次偏离关键阈值1.0之后几天实施的。
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Assessing the Efficacy of Covid-19 Policies in the Philippines
This research assessed the efficacy of the Philippine government’s COVID-19 policies by calculating the regional and national effective reproduction number (Rt) after the implementation of government policies. Rt was obtained through using a model with a Bayesian framework, which is an approach that creates probabilistic models based on existing evidence. The model, known as “EpiEstim”, provides estimates through analyzing the daily incidence of cases within predefined time periods. The effects of stringency on nationwide transmissibility were determined by analyzing the bi-variate relationship between the Government Stringency Index (OxCGRT) with the estimated nationwide Rt values, between February 6, 2020 and March 3, 2021. This study is relevant because the Philippine government’s response to COVID-19 is known to be one of the most prolonged and stringent approaches to the pandemic, and a quantitative measure of the efficacy of the government’s policies can help determine whether these approaches can mitigate the impact of outbreaks in the future. Regionally, the implementation of government policies has led subsequent Rt values to approximately return to the critical threshold of 1.0 in 10 out of the 16 dates tested. These Rt values ranged between 0.82 and 3.05. Nationally, the level of stringency approximates a negative correlation with transmissibility, most notably between March 6, 2020 and April 6, 2020. Many of the government’s policies can be considered as delayed responses as they were implemented several days after Rt first deviated from the critical threshold of 1.0.
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