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评估不同公共卫生治理结构下针对 COVID-19 的遏制策略的动态结果:巴基斯坦和孟加拉国的比较。

Assessing the Dynamic Outcomes of Containment Strategies against COVID-19 under Different Public Health Governance Structures: A Comparison between Pakistan and Bangladesh.

机构信息

Research Institute of Economics and Management, Southwestern University of Finance and Economics, Chengdu 610074, China.

Division of Science & Technology, BNU-HKBU United International College, Zhuhai 519087, China.

出版信息

Int J Environ Res Public Health. 2022 Jul 28;19(15):9239. doi: 10.3390/ijerph19159239.

DOI:10.3390/ijerph19159239
PMID:35954595
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9368361/
Abstract

COVID-19 scenarios were run using an epidemiological mathematical model (system dynamics model) and counterfactual analysis to simulate the impacts of different control and containment measures on cumulative infections and deaths in Bangladesh and Pakistan. The simulations were based on national-level data concerning vaccination level, hospital capacity, and other factors, from the World Health Organization, the World Bank, and the Our World in Data web portal. These data were added to cumulative infections and death data from government agencies covering the period from 18 March 2020 to 28 February 2022. Baseline curves for Pakistan and Bangladesh were obtained using piecewise fitting with a consideration of different events against the reported data and allowing for less than 5% random errors in cumulative infections and deaths. The results indicate that Bangladesh could have achieved more reductions in each key outcome measure by shifting its initial lockdown at least five days backward, while Pakistan would have needed to extend its lockdown to achieve comparable improvements. Bangladesh's second lockdown appears to have been better timed than Pakistan's. There were potential benefits from starting the third lockdown two weeks earlier for Bangladesh and from combining this with the fourth lockdown or canceling the fourth lockdown altogether. Adding a two-week lockdown at the beginning of the upward slope of the second wave could have led to a more than 40 percent reduction in cumulative infections and a 35 percent reduction in cumulative deaths for both countries. However, Bangladesh's reductions were more sensitive to the duration of the lockdown. Pakistan's response was more constrained by medical resources, while Bangladesh's outcomes were more sensitive to both vaccination timing and capacities. More benefits were lost when combining multiple scenarios for Bangladesh compared to the same combinations in Pakistan. Clearly, cumulative infections and deaths could have been highly impacted by adjusting the control and containment measures in both national settings. However, COVID-19 outcomes were more sensitive to adjustment interventions for the Bangladesh context. Disaggregated analyses, using a wider range of factors, may reveal several sub-national dynamics. Nonetheless, the current research demonstrates the relevance of lockdown timing adjustments and discrete adjustments to several other control and containment measures.

摘要

使用流行病学数学模型(系统动力学模型)和反事实分析对 COVID-19 情景进行了模拟,以模拟不同控制和遏制措施对孟加拉国和巴基斯坦累计感染和死亡人数的影响。模拟基于世界卫生组织、世界银行和 Our World in Data 门户网站提供的国家层面数据,包括疫苗接种水平、医院容量和其他因素。这些数据与 2020 年 3 月 18 日至 2022 年 2 月 28 日期间政府机构报告的累计感染和死亡数据进行了合并。使用分段拟合获得了巴基斯坦和孟加拉国的基线曲线,考虑了与报告数据相比的不同事件,并允许累计感染和死亡数据存在不到 5%的随机误差。结果表明,孟加拉国通过将最初的封锁至少向后推迟五天,本可以在每个关键结果指标上实现更多的减少,而巴基斯坦则需要延长封锁时间以实现可比的改善。孟加拉国的第二次封锁似乎比巴基斯坦的封锁时机更好。孟加拉国如果提前两周开始第三次封锁,或者将第三次封锁与第四次封锁结合起来,或者干脆取消第四次封锁,可能会有潜在的好处。在第二波上升阶段的开始时增加两周的封锁,两国的累计感染人数可能减少 40%以上,累计死亡人数减少 35%。然而,孟加拉国的减少对封锁的持续时间更为敏感。巴基斯坦的反应受到医疗资源的限制更大,而孟加拉国的结果则对疫苗接种时间和能力更为敏感。与相同的组合相比,孟加拉国的多个场景组合损失了更多的好处。显然,通过调整两国的控制和遏制措施,累计感染和死亡人数可能会受到很大影响。然而,COVID-19 的结果对孟加拉国调整干预措施更为敏感。使用更广泛的因素进行分解分析,可能会揭示出一些次国家动态。尽管如此,当前的研究表明,锁定时间调整和对其他一些控制和遏制措施的离散调整具有相关性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c2f/9368361/8a00edbd96e4/ijerph-19-09239-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c2f/9368361/8263f331c62d/ijerph-19-09239-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c2f/9368361/e647359c5248/ijerph-19-09239-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c2f/9368361/1f19357d391f/ijerph-19-09239-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c2f/9368361/f6a136057a62/ijerph-19-09239-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c2f/9368361/14d233e2ae26/ijerph-19-09239-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c2f/9368361/1e3dd17c7a73/ijerph-19-09239-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c2f/9368361/b57c9ae9ef20/ijerph-19-09239-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c2f/9368361/2e7fa76e6d4d/ijerph-19-09239-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c2f/9368361/8a00edbd96e4/ijerph-19-09239-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c2f/9368361/8263f331c62d/ijerph-19-09239-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c2f/9368361/e647359c5248/ijerph-19-09239-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c2f/9368361/1f19357d391f/ijerph-19-09239-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c2f/9368361/f6a136057a62/ijerph-19-09239-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c2f/9368361/14d233e2ae26/ijerph-19-09239-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c2f/9368361/1e3dd17c7a73/ijerph-19-09239-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c2f/9368361/b57c9ae9ef20/ijerph-19-09239-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c2f/9368361/2e7fa76e6d4d/ijerph-19-09239-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c2f/9368361/8a00edbd96e4/ijerph-19-09239-g009.jpg

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