Multimodeling approach to evaluating the efficacy of layering pharmaceutical and nonpharmaceutical interventions for influenza pandemics.
Multimodeling approach to evaluating the efficacy of layering pharmaceutical and nonpharmaceutical interventions for influenza pandemics.
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评估对流感大流感的分层药物和非药物干预措施的功效的多模型方法。
DOI：
10.1073/pnas.2300590120
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发表时间：
2023-07-11
影响因子：
11.1
通讯作者：
Biggerstaff, Matthew
中科院分区：
文献类型：
--
作者：
V. Prasad, Pragati;Steele, Molly K.;Reed, Carrie;Meyers, Lauren Ancel;Du, Zhanwei;Pasco, Remy;Alfaro-Murillo, Jorge A.;Lewis, Bryan;Venkatramanan, Srinivasan;Schlitt, James;Chen, Jiangzhuo;Orr, Mark;Wilson, Mandy L.;Eubank, Stephen;Wang, Lijing;Chinazzi, Matteo;Piontti, Ana Pastore Y.;Davis, Jessica T.;Halloran, M. Elizabeth;Longini, Ira;Vespignani, Alessandro;Pei, Sen;Galanti, Marta;Kandula, Sasikiran;Shaman, Jeffrey;Haw, David J.;Arinaminpathy, Nimalan;Biggerstaff, Matthew
该项目突出了合作研究网络的效用，通过比较和整合不同的模型，为大流行性流感决策提供集体见解。虽然该项目始于2019年，但在SARS-CoV-2大流行出现之前，从大流行中吸取的教训强调了建立系统的必要性，以便在呼吸道病毒大流行期间比较，汇总和解释多种传染病数学模型的模拟。当流感大流行出现时，暂时关闭学校和抗病毒治疗可以减缓病毒传播，减少整体疾病负担，并为疫苗开发，分发和管理提供时间，同时保持大部分普通人群无感染。这些措施的影响将取决于病毒的传播能力和严重程度以及实施这些措施的时间和程度。为了对分层的大流行干预策略进行强有力的评估，美国疾病控制和预防中心（CDC）资助了一个学术团体网络，以建立一个开发和比较多种大流行性流感模型的框架。来自哥伦比亚大学、帝国理工学院伦敦/普林斯顿大学、东北大学、得克萨斯大学奥斯汀分校/耶鲁大学和弗吉尼亚大学的研究小组独立模拟了CDC和网络成员合作开发的三组规定的大流行性流感情景。将各组提供的结果汇总为基于平均值的集合。总体模型和大部分组成模型都同意按影响对最有效和最无效的干预战略进行排序，但不同意这些影响的程度。在所评估的情景中，由于开发、批准和部署所需的时间，预计仅疫苗接种不会大幅减少疾病、住院和死亡的数量。研究发现，只有包括尽早实施学校关闭在内的策略才能大幅减轻早期传播，并为疫苗的开发和接种留出时间，特别是在高度传播的大流行情况下。
This project highlights the utility of collaborative research networks to gain collective insights for pandemic influenza decision-making through the comparison and ensembling of diverse models. While this project started in 2019, prior to the emergence of the SARS-CoV-2 pandemic, lessons learned from the pandemic underscore the need to build out systems for comparing, aggregating, and interpreting simulations from multiple infectious disease mathematical models during respiratory virus pandemics. When an influenza pandemic emerges, temporary school closures and antiviral treatment may slow virus spread, reduce the overall disease burden, and provide time for vaccine development, distribution, and administration while keeping a larger portion of the general population infection free. The impact of such measures will depend on the transmissibility and severity of the virus and the timing and extent of their implementation. To provide robust assessments of layered pandemic intervention strategies, the Centers for Disease Control and Prevention (CDC) funded a network of academic groups to build a framework for the development and comparison of multiple pandemic influenza models. Research teams from Columbia University, Imperial College London/Princeton University, Northeastern University, the University of Texas at Austin/Yale University, and the University of Virginia independently modeled three prescribed sets of pandemic influenza scenarios developed collaboratively by the CDC and network members. Results provided by the groups were aggregated into a mean-based ensemble. The ensemble and most component models agreed on the ranking of the most and least effective intervention strategies by impact but not on the magnitude of those impacts. In the scenarios evaluated, vaccination alone, due to the time needed for development, approval, and deployment, would not be expected to substantially reduce the numbers of illnesses, hospitalizations, and deaths that would occur. Only strategies that included early implementation of school closure were found to substantially mitigate early spread and allow time for vaccines to be developed and administered, especially under a highly transmissible pandemic scenario.
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1918年大流行性流感的传播性。
影响因子：
64.8
作者：
Mills CE;Robins JM;Lipsitch M
通讯作者：
Lipsitch M
DOI：
10.1073/pnas.0706849105
发表时间：
2008-03-25
影响因子：
11.1
作者：
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影响因子：
11.8
作者：
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通讯作者：
Meltzer, Martin I.
影响因子：
4.2
作者：
Vynnycky, E.;Edmunds, W. J.
通讯作者：
Edmunds, W. J.
影响因子：
11.8
作者：
Borse RH;Shrestha SS;Fiore AE;Atkins CY;Singleton JA;Furlow C;Meltzer MI
通讯作者：
Meltzer MI