Addressing the Health Versus Economy Dilemma in Data-Driven Policymaking During a Pandemic
Addressing the Health Versus Economy Dilemma in Data-Driven Policymaking During a Pandemic
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解决大流行期间数据驱动决策中的健康与经济困境
DOI：
10.1145/3583133.3590652
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Hotchkiss L
中科院分区：
文献类型：
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作者：
Hotchkiss L
最近的新冠肺炎大流行突显了对工具的需求，以帮助政策制定者在知情的情况下决定实施哪些政策，以减少大流行的影响。以前已经开发了几个工具来模拟非药物干预(NPI)如何影响人群中疾病的增长速度，例如社会距离。建模工作的大部分焦点都集中在健康因素的预测上，将它们与NPI联系起来，很少有研究卫生与经济权衡的工作。然而，在这一领域的真实数据驱动的解决方案的说明中有一个特别的空白。在这篇文章中，我们提出了一个纯数据驱动的框架，我们分别用贝叶斯和递归神经网络(RNN)模型对健康和经济影响进行建模，并使用NSGA-II来确定三周内的政策严格程度。我们证明，这个框架可以产生一系列解决方案，在基于真实数据的健康和经济预测之间进行权衡，政策制定者可以使用这些解决方案来做出明智的决定。
The recent COVID-19 pandemic highlighted a need for tools to help policy-makers make informed decisions on what policies to implement in order to reduce the impact of the pandemic. Several tools have previously been developed to model how non-pharmaceutical interventions (NPIs), such as social distancing, affect the rate of growth of a disease within a population. Much of the focus of the modelling effort have been on projections of health factors, relating them to the NPIs, with only few works addressing the health-economy trade-off. However, there is a particular gap in illustrations of real data-driven solutions in this area. In this paper, we proposed a purely data-driven framework where we modelled health and economic impacts with Bayesian and Recurrent Neural Network (RNN) models respectively, and used NSGA-II to identify policy stringencies over a three-week period. We demonstrate that this framework can produce a range of solutions trading off between health and economy projections based on real data, that may be used by policymakers to reach an informed decision.
DeepCOVID：一个可操作的深度学习驱动框架，用于可解释的实时 COVID-19 预测
DOI：
10.1101/2020.09.28.20203109
发表时间：
2021
期刊：
Proceedings of AAAI
影响因子：
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作者：
Rodriguez, Alexander;Tabassum, Anika;Cui, Jiaming;Xie, Jiajia;Ho, Javen;Agarwal, Pulak;Adhikari, Bijaya;Prakash, B. Aditya
通讯作者：
Prakash, B. Aditya
动态模型中变量选择和多元预测的贝叶斯计算
DOI：
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发表时间：
2020
期刊：
影响因子：
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作者：
Isaac Lavine
通讯作者：
Isaac Lavine