Assessing the Interplay between travel patterns and SARS-CoV-2 outbreak in realistic urban setting

TL;DR

This study investigates how urban travel patterns, road connectivity, and containment measures influence SARS-CoV-2 transmission in a dense city, using a high-resolution network model and open-source data to inform mitigation strategies.

Contribution

It introduces a dynamical network model that incorporates open-source traffic and mobility data to analyze disease spread at sub-kilometer scales in urban settings.

Findings

Road connectivity and transit ease significantly affect transmission rates.

Containment zones alter travel patterns and disease dynamics.

Social distancing and lockdowns effectively reduce disease spread.

Abstract

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. 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 the 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.