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摘要翻译：
世界各国采用了密集的非药物干预措施（“封锁”）的组合，以减少新型冠状病毒的传播。许多政府已经开始实施封锁退出战略，允许放松限制，同时试图控制病例激增的风险。数学模型在指导干预措施方面发挥了核心作用，但面对持续的传播，设计最佳退出战略的挑战是前所未有的。在此，我们报告艾萨克·牛顿研究所“退出战略模型”研讨会（2020年5月11日至15日）的讨论。向世界各国政府提供证据的各种建模者被要求确定主要问题，如果这些问题得到回答，将能够更准确地预测不同退出战略的影响。基于这些问题，我们提出了一个路线图，以促进开发可靠的模型来指导退出战略。路线图需要科学界和政策制定者的全球协作努力，由三个部分组成：（一）改进对关键流行病学参数的估计；（二）了解人口异质性的来源；（三）侧重于收集数据的要求，特别是在中低收入国家，这将为规划平衡社会经济效益和公共卫生的退出战略提供重要信息。
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英文标题：
《Key Questions for Modelling COVID-19 Exit Strategies》
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作者：
Robin N Thompson, T Deirdre Hollingsworth, Valerie Isham, Daniel
Arribas-Bel, Ben Ashby, Tom Britton, Peter Challoner, Lauren H K Chappell,
Hannah Clapham, Nik J Cunniffe, A Philip Dawid, Christl A Donnelly, Rosalind
Eggo, Sebastian Funk, Nigel Gilbert, Julia R Gog, Paul Glendinning, William S
Hart, Hans Heesterbeek, Thomas House, Matt Keeling, Istvan Z Kiss, Mirjam
Kretzschmar, Alun L Lloyd, Emma S McBryde, James M McCaw, Joel C Miller,
Trevelyan J McKinley, Martina Morris, Philip D ONeill, Carl A B Pearson, Kris
V Parag, Lorenzo Pellis, Juliet R C Pulliam, Joshua V Ross, Michael J
Tildesley, Gianpaolo Scalia Tomba, Bernard W Silverman, Claudio J Struchiner,
Pieter Trapman, Cerian R Webb, Denis Mollison, Olivier Restif
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最新提交年份：
2020
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分类信息：
一级分类：Quantitative Biology 数量生物学
二级分类：Other Quantitative Biology 其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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一级分类：Quantitative Biology 数量生物学
二级分类：Populations and Evolution 种群与进化
分类描述：Population dynamics, spatio-temporal and epidemiological models, dynamic speciation, co-evolution, biodiversity, foodwebs, aging; molecular evolution and phylogeny; directed evolution; origin of life
种群动力学；时空和流行病学模型；动态物种形成；协同进化；生物多样性；食物网；老龄化；分子进化和系统发育；定向进化；生命起源
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英文摘要：
Combinations of intense non-pharmaceutical interventions ('lockdowns') were introduced in countries worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement lockdown exit strategies that allow restrictions to be relaxed while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute 'Models for an exit strategy' workshop (11-15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, will allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. The roadmap requires a global collaborative effort from the scientific community and policy-makers, and is made up of three parts: i) improve estimation of key epidemiological parameters; ii) understand sources of heterogeneity in populations; iii) focus on requirements for data collection, particularly in Low-to-Middle-Income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health.
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PDF链接：
https://arxiv.org/pdf/2006.13012
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