Benneyan James C, Gehrke Christopher, Ilies Iulian, Nehls Nicole
medRxiv. 2020 Sep 13:2020.08.29.20184366. doi: 10.1101/2020.08.29.20184366.
Significant uncertainty exists in many countries about the safety of, and best strategies for, reopening college and university campuses until the Covid-19 pandemic is better controlled. Little also is known about the effects on-campus students may have on local higher-risk communities. We aimed to estimate potential community and campus Covid-19 exposures, infections, and mortality due to various university reopening and precaution plans under current ranges of assumptions and uncertainties.
We developed and calibrated campus-only, community-only, and campus-x-community epidemic differential equation and agent-based models. Input parameters for campus and surrounding communities were estimated via published and grey literature, scenario development, expert opinion, accuracy optimization algorithms, and Monte Carlo simulation; models were cross-validated against each other using February-June 2020 data from heterogeneous U.S. counties and states. Campus opening plans (spanning various fully open, hybrid, and fully virtual approaches) were identified from websites and publications. All scenarios were simulated assuming 16-week semesters and estimated ranges for Covid-19 prevalence among community residents and arriving students, precaution compliance, contact frequency, virus attack rates, and tracing and isolation effectiveness. Additional student and community exposures, infections, and mortality were estimated under each scenario, with 10% trimmed medians, standard deviations, and probability intervals computed to omit extreme outlier scenarios. Factorial analyses were conducted to identify intervention inputs with largest and smallest effects.
As a base case with no precautions (or no compliance), predicted 16-week student infections and mortality under normal operations ranged significantly from 471 to 9,495 (median: 2,286, SD: 2,627) and 0 to 123 (median: 9, SD: 14) per 10,000 students, respectively. The maximum active exposures across a semester was 15.76% of all students warranting tracing. Total additional community exposures, infections, and mortality ranged from 1 to 187, 13 to 820, and 1 to 21 per 10,000 residents, respectively. 1% and 5% of on-campus students were infected after a mean (SD) of 11 (3) and 76 (17) days, respectively; >10% students infected by the end of a semester in 34.8% of scenarios, with the greatest increase (first inflection point) occurring on aver-age on day 84 (SD: 10.2 days). Common reopening precautions reduced infections by 24% to 26% and mortality by 36% to 50% in both populations. Uncertainties in many factors, however, produced tremendous variability in all results, ranging from medians by -67% to +342%.
Consequences on community and student Covid-19 exposures, infections, and mortality of reopening physical campuses are very highly unpredictable, depending on a combination of random chance, controllable (e.g. physical layouts), and uncontrollable (e.g. human behavior) factors. Implications include needs for criteria to adapt campus operations mid-semester, methods to detect when necessary, and contingency plans for doing so.
在许多国家,在新冠疫情得到更好控制之前,大学校园重新开放的安全性和最佳策略存在重大不确定性。对于在校学生可能对当地高风险社区产生的影响,人们了解也甚少。我们旨在估计在当前一系列假设和不确定性下,由于各种大学重新开放和预防计划,可能导致的社区和校园新冠病毒暴露、感染及死亡情况。
我们开发并校准了仅针对校园、仅针对社区以及校园 - 社区的流行病微分方程和基于主体的模型。通过已发表文献、灰色文献、情景开发、专家意见、准确性优化算法以及蒙特卡罗模拟来估计校园和周边社区的输入参数;使用2020年2月至6月来自美国不同县和州的数据对模型进行相互交叉验证。从网站和出版物中确定校园开放计划(涵盖各种完全开放、混合式和完全虚拟的方式)。所有情景模拟均假设为期16周的学期,并估计社区居民和到校学生中新冠病毒流行率、预防措施的遵守情况、接触频率、病毒攻击率以及追踪和隔离效果的范围。在每种情景下估计额外的学生和社区暴露、感染及死亡情况,计算10%截尾中位数、标准差和概率区间以排除极端异常值情景。进行析因分析以确定影响最大和最小的干预输入。
作为无预防措施(或无遵守情况)的基础案例,在正常运营情况下,预测每10000名学生16周的感染数和死亡数分别在471至9495(中位数:2286,标准差:2627)和0至123(中位数:9,标准差:14)之间显著波动。一学期内最大的活跃暴露量占所有需追踪学生的15.76%。每10000名居民的社区额外暴露、感染及死亡总数分别在1至187、13至820和1至21之间。分别有1%和5%的在校学生在平均(标准差)11(3)天和76(17)天后被感染;在34.8%的情景中,到学期末超过10%的学生被感染,最大增幅(第一个拐点)平均出现在第84天(标准差:10.2天)。常见的重新开放预防措施使两个群体的感染率降低了24%至26%,死亡率降低了36%至50%。然而,许多因素的不确定性导致所有结果产生巨大差异,中位数变化范围为 -67%至 +342%。
重新开放实体校园对社区和学生新冠病毒暴露、感染及死亡的影响非常难以预测,这取决于随机因素、可控因素(如物理布局)和不可控因素(如人类行为)的综合作用。这意味着需要制定标准以便在学期中调整校园运营、必要时进行检测的方法以及相应的应急预案。