Economic optimization of a global strategy to address the pandemic threat

Economic optimization of a global strategy to address the pandemic threat
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DOI:
10.1073/pnas.1412661112
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发表时间:
2014-12-30
影响因子:
11.1
通讯作者:
Daszak, Peter
Daszak, Peter
中科院分区:
综合性期刊1区
文献类型:
--
作者:
Pike, Jamison;Bogich, Tiffany;Daszak, Peter

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新出现的大流行病威胁着全球健康和经济,而且发生频率正在增加。全球协调一致的抗击流行病战略,与当前应对气候变化的战略类似,在很大程度上是适应性的,因为它们试图在病原体出现后减少其影响。然而,与气候变化一样,已经制定了缓解战略,其中包括减少大流行病的潜在驱动因素,特别是动物到人类的疾病传播。在这里,我们使用真实的选择经济模型,目前全球协调的适应战略,以预防大流行病。我们表明,它们将在27年内得到最佳实施,以减少50%的新发传染病事件的年增长率,估计一次性成本约为3437亿美元。然后,我们分析了世界银行关于多边“一个健康”大流行缓解计划的数据。我们发现,由于大多数流行病都是动物起源的,因此缓解是比一切照旧的适应计划更具成本效益的政策,如果今天实施,在未来100年内节省3440.7亿美元至360.3亿美元。我们的结论是,需要紧急制定全球协调的大流行病预防政策,以取得最佳效果,通过减少其根本驱动因素的影响来减轻大流行病的战略可能比一切照旧更有效。
Emerging pandemics threaten global health and economies and are increasing in frequency. Globally coordinated strategies to combat pandemics, similar to current strategies that address climate change, are largely adaptive, in that they attempt to reduce the impact of a pathogen after it has emerged. However, like climate change, mitigation strategies have been developed that include programs to reduce the underlying drivers of pandemics, particularly animal-to-human disease transmission. Here, we use real options economic modeling of current globally coordinated adaptation strategies for pandemic prevention. We show that they would be optimally implemented within 27 y to reduce the annual rise of emerging infectious disease events by 50% at an estimated one-time cost of approximately $343.7 billion. We then analyze World Bank data on multilateral "One Health" pandemic mitigation programs. We find that, because most pandemics have animal origins, mitigation is a more cost-effective policy than business-asusual adaptation programs, saving between $344.0.7 billion and $360.3 billion over the next 100 y if implemented today. We conclude that globally coordinated pandemic prevention policies need to be enacted urgently to be optimally effective and that strategies to mitigate pandemics by reducing the impact of their underlying drivers are likely to be more effective than business as usual.