Centre for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany.
Population Health Sciences Institute, Newcastle University, Newcastle, UK.
Sci Rep. 2021 Nov 24;11(1):22855. doi: 10.1038/s41598-021-02226-x.
Policymakers commonly employ non-pharmaceutical interventions to reduce the scale and severity of pandemics. Of non-pharmaceutical interventions, physical distancing policies-designed to reduce person-to-person pathogenic spread - have risen to recent prominence. In particular, stay-at-home policies of the sort widely implemented around the globe in response to the COVID-19 pandemic have proven to be markedly effective at slowing pandemic growth. However, such blunt policy instruments, while effective, produce numerous unintended consequences, including potentially dramatic reductions in economic productivity. In this study, we develop methods to investigate the potential to simultaneously contain pandemic spread while also minimizing economic disruptions. We do so by incorporating both occupational and contact network information contained within an urban environment, information that is commonly excluded from typical pandemic control policy design. The results of our methods suggest that large gains in both economic productivity and pandemic control might be had by the incorporation and consideration of simple-to-measure characteristics of the occupational contact network. We find evidence that more sophisticated, and more privacy invasive, measures of this network do not drastically increase performance.
政策制定者通常采用非药物干预措施来降低大流行的规模和严重程度。在非药物干预措施中，旨在减少人与人之间病原体传播的身体距离政策最近引起了关注。特别是，全球范围内为应对 COVID-19 大流行而广泛实施的居家政策已被证明在减缓大流行增长方面非常有效。然而，这种生硬的政策工具虽然有效，但会产生许多意想不到的后果，包括经济生产力可能大幅下降。在这项研究中，我们开发了方法来研究同时控制大流行传播同时最小化经济干扰的可能性。我们通过纳入城市环境中包含的职业和接触网络信息来实现这一点，这些信息通常被排除在典型的大流行控制政策设计之外。我们方法的结果表明，通过纳入和考虑职业接触网络的简单可衡量特征，可以在经济生产力和大流行控制方面取得巨大收益。我们有证据表明，更复杂、更侵犯隐私的网络措施并不会大幅提高性能。
