Background

The dense social contact networks and high mobility in congested urban areas facilitate the rapid transmission of infectious diseases. Typical mechanistic epidemiological models are either based on uniform mixing with ad-hoc contact processes or need real-time or archived population mobility data to simulate the social networks. However, the rapid and global transmission of the novel coronavirus (SARS-CoV-2) has led to unprecedented lockdowns at global and regional scales, leaving the archived datasets to limited use.

Findings

While it is often hypothesized that population density is a significant driver in disease propagation, the disparate disease trajectories and infection rates exhibited by the different cities with comparable densities require a high-resolution description of the disease and its drivers. In this study, we explore the impact of creation of containment zones on travel patterns within the city. Further, we use a dynamical network-based infectious disease model to understand the key drivers of disease spread at sub-kilometer scales demonstrated in the city of Ahmedabad, India, which has been classified as a SARS-CoV-2 hotspot. We find that in addition to the contact network and population density, road connectivity patterns and ease of transit are strongly correlated with the rate of transmission of the disease. Given the limited access to real-time traffic data during lockdowns, we generate road connectivity networks using open-source imageries and travel patterns from open-source surveys and government reports. Within the proposed framework, we then analyze the relative merits of social distancing, enforced lockdowns, and enhanced testing and quarantining mitigating the disease spread.

Scope

Our results suggest that the declaration of micro-containment zones within the city with high road network density combined with enhanced testing can help in containing the outbreaks until clinical interventions become available.

中文翻译：

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评估现实城市环境中出行模式与 SARS-CoV-2 爆发之间的相互作用

背景

拥挤的城市地区密集的社会联系网络和高流动性促进了传染病的快速传播。典型的机械流行病学模型要么基于与临时接触过程的统一混合，要么需要实时或存档的人口流动数据来模拟社交网络。然而，新型冠状病毒（SARS-CoV-2）在全球范围内的快速传播导致全球和区域范围内史无前例的封锁，使得存档数据集的使用受到限制。

发现

虽然人们通常假设人口密度是疾病传播的重要驱动因素，但具有可比密度的不同城市所表现出的不同疾病轨迹和感染率需要对疾病及其驱动因素进行高分辨率描述。在这项研究中，我们探讨了隔离区的创建对城市内出行模式的影响。此外，我们使用基于动态网络的传染病模型来了解印度艾哈迈达巴德市展示的亚公里级疾病传播的关键驱动因素，该市已被列为 SARS-CoV-2 热点地区。我们发现，除了接触网络和人口密度之外，道路连通模式和交通便利性与疾病的传播率密切相关。鉴于封锁期间实时交通数据的获取有限，我们使用开源图像以及开源调查和政府报告中的出行模式生成道路连接网络。然后，在拟议的框架内，我们分析了社会疏远、强制封锁以及加强检测和隔离以减轻疾病传播的相对优点。

范围

我们的结果表明，在道路网络密度较高的城市内宣布微遏制区，并结合加强检测，有助于遏制疫情爆发，直至采取临床干预措施。