[论文解读] Universal Masking is Urgent in the COVID-19 Pandemic: SEIR and Agent Based Models, Empirical Validation, Policy Recommendations

De Kai, Guy Philippe Goldstein+3 more|arXiv (Cornell University)|Apr 22, 2020

Infection Control and Ventilation参考文献 21被引用 120

一句话总结

本论文开发了SEIR和基于代理的模型，以评估全民佩戴口罩对 COVID-19 传播的影响，使用经验数据验证预测，并提出政策建议。

ABSTRACT

We present two models for the COVID-19 pandemic predicting the impact of universal face mask wearing upon the spread of the SARS-CoV-2 virus--one employing a stochastic dynamic network based compartmental SEIR (susceptible-exposed-infectious-recovered) approach, and the other employing individual ABM (agent-based modelling) Monte Carlo simulation--indicating (1) significant impact under (near) universal masking when at least 80% of a population is wearing masks, versus minimal impact when only 50% or less of the population is wearing masks, and (2) significant impact when universal masking is adopted early, by Day 50 of a regional outbreak, versus minimal impact when universal masking is adopted late. These effects hold even at the lower filtering rates of homemade masks. To validate these theoretical models, we compare their predictions against a new empirical data set we have collected that includes whether regions have universal masking cultures or policies, their daily case growth rates, and their percentage reduction from peak daily case growth rates. Results show a near perfect correlation between early universal masking and successful suppression of daily case growth rates and/or reduction from peak daily case growth rates, as predicted by our theoretical simulations. Our theoretical and empirical results argue for urgent implementation of universal masking. As governments plan how to exit societal lockdowns, it is emerging as a key NPI; a "mouth-and-nose lockdown" is far more sustainable than a "full body lockdown", on economic, social, and mental health axes. An interactive visualization of the ABM simulation is at http://dek.ai/masks4all. We recommend immediate mask wearing recommendations, official guidelines for correct use, and awareness campaigns to shift masking mindsets away from pure self-protection, towards aspirational goals of responsibly protecting one's community.

研究动机与目标

• 在COVID-19大流行期间，促进全民戴口罩作为一种关键的非药物干预措施。
• 开发并比较两种理论建模方法（在随机动态网络上的SEIR模型和ABM蒙特卡洛）以量化佩戴口罩的影响。
• 在具有不同佩戴口罩文化和政策的地区的新经验数据集上验证模型预测。
• 提供政策建议，使口罩佩戴能够早期且广泛实施以控制传播。
• 强调将佩戴口罩与其他非药物干预措施结合使用，以在解除封锁时制定退出策略的实际意义。

提出的方法

• 在随机动态网络上实现的SEIR模型，用于捕捉近距离与全局接触及 S→E→I→R→F 的转变，参数为 β, σ, γ, μI。
• 在二维环绕空间中的单个体代理的ABM蒙特卡洛模拟，用于建模基于接近度的传播和口罩效应。
• 通过用因子T降低传播速率、用A吸收来建模口罩效应，并通过随时间改变口罩采用率M来模拟。
• 对比场景包括0%、50%和80–90%的口罩采用率及时机（发病日0、日50、日75）。
• 基线参数设定为 β=0.155, σ=1/5.2, γ=1/12.39；初始感染为1%；为校准，人口规模约为67,000（SEIR）和200个代理（ABM）。

实验结果

研究问题

• RQ1 在仅有社交疏离和封锁的情况下，普遍佩戴口罩（高采用率）如何影响COVID-19感染的轨迹？
• RQ2 实施全民口罩的时机对疫情结果有何影响？
• RQ3 理论上的SEIR和ABM模型是否与在不同佩戴口罩文化或政策的地区的经验数据一致？
• RQ4 可以得出哪些政策指导，以将口罩作为更广泛缓解策略的一部分来优化？

主要发现

• 在SEIR网络模型中，80%采用率的全民佩戴显著拉平曲线并减少相对于仅封锁的死亡人数。
• 在SEIR仿真中，50%采用率的佩戴不足以阻止传播。
• 早期全民佩戴（在第50天前）在SEIR和ABM模型中显著减少感染，而晚期佩戴影响甚微。
• 来自38个地区的经验数据表明，早期全民佩戴与日新增病例增长被抑制以及峰值后的下降之间几近完美相关，支持模型预测。
• ABM结果表明，若在第50天或更早实施口罩可以显著压制传播，而将口罩延迟到第75天会降低效果。
• 政策要点显示口罩作为关键的非药物干预措施，与检测、追踪和隔离等措施协同工作，以在不依赖全面封锁的情况下管理COVID-19。