Xiao Yao, Yang Mofeng, Zhu Zheng, Yang Hai, Zhang Lei, Ghader Sepehr
School of Intelligent System Engineering, Sun Yat-Sen University, Shenzhen, Guangdong, China.
Maryland Transportation Institute, Department of Civil and Environmental Engineering, University of Maryland at College Park, Maryland, USA.
Transp Policy (Oxf). 2021 Aug;109:12-23. doi: 10.1016/j.tranpol.2021.05.004. Epub 2021 May 16.
Mathematical modeling of epidemic spreading has been widely adopted to estimate the threats of epidemic diseases (i.e., the COVID-19 pandemic) as well as to evaluate epidemic control interventions. The indoor place is considered to be a significant epidemic spreading risk origin, but existing widely-used epidemic spreading models are usually limited for indoor places since the dynamic physical distance changes between people are ignored, and the empirical features of the essential and non-essential travel are not differentiated. In this paper, we introduce a pedestrian-based epidemic spreading model that is capable of modeling indoor transmission risks of diseases during people's social activities. Taking advantage of the before-and-after mobility data from the University of Maryland COVID-19 Impact Analysis Platform, it's found that people tend to spend more time in grocery stores once their travel frequencies are restricted to a low level. In other words, an increase in dwell time could balance the decrease in travel frequencies and satisfy people's demands. Based on the pedestrian-based model and the empirical evidence, combined non-pharmaceutical interventions from different operational levels are evaluated. Numerical simulations show that restrictions on people's travel frequency and open hours of indoor places may not be universally effective in reducing average infection risks for each pedestrian who visit the place. Entry limitations can be a widely effective alternative, whereas the decision-maker needs to balance the decrease in risky contacts and the increase in queue length outside the place that may impede people from fulfilling their travel needs. The results show that a good coordination among the decision-makers can contribute to the improvement of the performance of combined non-pharmaceutical interventions, and it also benefits the short-term and long-term interventions in the future.
流行病传播的数学模型已被广泛用于估计流行病（如新冠疫情）的威胁以及评估疫情防控干预措施。室内场所被认为是一个重要的流行病传播风险源，但现有的广泛使用的流行病传播模型通常在室内场所存在局限性，因为忽略了人与人之间动态的物理距离变化，并且没有区分必要出行和非必要出行的经验特征。在本文中，我们引入了一种基于行人的流行病传播模型，该模型能够对人们社交活动期间疾病的室内传播风险进行建模。利用马里兰大学新冠疫情影响分析平台的前后移动数据，发现一旦人们的出行频率被限制在较低水平，他们往往会在杂货店花费更多时间。换句话说，停留时间的增加可以平衡出行频率的降低并满足人们的需求。基于该基于行人的模型和经验证据，评估了来自不同运营层面的联合非药物干预措施。数值模拟表明，限制人们的出行频率和室内场所的开放时间可能并非对每个进入该场所的行人都普遍有效地降低平均感染风险。入口限制可能是一种广泛有效的替代方案，而决策者需要平衡风险接触的减少与场所外排队长度的增加，排队长度增加可能会妨碍人们满足出行需求。结果表明，决策者之间的良好协调有助于提高联合非药物干预措施的效果，并且对未来的短期和长期干预措施也有益处。
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