Alexis Zebrowski and Brendan G. Carr are with the Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, NY. Andrew Rundle, Tonguc Yaman, Wan Yang, James W. Quinn, and Charles C. Branas are with the Department of Epidemiology, Mailman School of Public Health, Columbia University, New York. Sen Pei and Jeffrey Shaman are with the Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University. Sarah Sims is with Patient Insight, Santa Monica, CA. Ronan Doorley is with Media Lab at the Massachusetts Institute of Technology, Cambridge. Neil Schluger is with the Division of Pulmonary, Allergy, and Critical Care Medicine, and Departments of Epidemiology and Environmental Health Sciences, Columbia University Irving Medical Center, Columbia University.
Am J Public Health. 2021 Jun;111(6):1113-1122. doi: 10.2105/AJPH.2021.306220. Epub 2021 Apr 15.
To create a tool to rapidly determine where pandemic demand for critical care overwhelms county-level surge capacity and to compare public health and medical responses. In March 2020, COVID-19 cases requiring critical care were estimated using an adaptive metapopulation SEIR (susceptible‒exposed‒infectious‒recovered) model for all 3142 US counties for future 21-day and 42-day periods from April 2, 2020, to May 13, 2020, in 4 reactive patterns of contact reduction-0%, 20%, 30%, and 40%-and 4 surge response scenarios-very low, low, medium, and high. In areas with increased demand, surge response measures could avert 104 120 additional deaths-55% through high clearance of critical care beds and 45% through measures such as greater ventilator access. The percentages of lives saved from high levels of contact reduction were 1.9 to 4.2 times greater than high levels of hospital surge response. Differences in projected versus actual COVID-19 demands were reasonably small over time. Nonpharmaceutical public health interventions had greater impact in minimizing preventable deaths during the pandemic than did hospital critical care surge response. Ready-to-go spatiotemporal supply and demand data visualization and analytics tools should be advanced for future preparedness and all-hazards disaster response.
创建一种工具，以快速确定哪些县的医疗资源无法应对大流行期间对重症监护的需求，并比较公共卫生和医疗应对措施。2020 年 3 月，我们使用适应性泛种群 SEIR（易感-暴露-感染-恢复）模型，对美国 3142 个县进行了未来 21 天和 42 天的预测，分析了新冠疫情期间对重症监护的需求。接触减少 0%、20%、30%和 40%的 4 种反应模式和低、中、高 4 种激增应对场景。在需求增加的地区，通过清除大量重症监护床位，可避免 104120 例额外死亡（55%），通过增加呼吸机等措施可避免 45%的额外死亡。高接触减少水平下的预期生命挽救率比高医院应对水平下高出 1.9 至 4.2 倍。随着时间的推移，预测和实际的新冠疫情需求之间的差异相当小。非药物公共卫生干预措施在大流行期间减少可预防死亡方面的影响大于医院重症监护应对措施。应提前开发用于未来准备和所有灾害应对的现成的时空供需数据可视化和分析工具。
