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The dense social contact networks and high mobility in congested urban areasfacilitate the rapid transmission of infectious diseases. Typical mechanisticepidemiological models are either based on uniform mixing with ad-hoc contactprocesses or need real-time or archived population mobility data to simulatethe social networks. However, the rapid and global transmission of the novelcoronavirus (SARS-CoV-2) has led to unprecedented lockdowns at global andregional scales, leaving the archived datasets to limited use. While it isoften hypothesized that population density is a significant driver in diseasepropagation, the disparate disease trajectories and infection rates exhibitedby the different cities with comparable densities require a high-resolutiondescription of the disease and its drivers. In this study, we explore theimpact of the creation of containment zones on travel patterns within the city.Further, we use a dynamical network-based infectious disease model tounderstand the key drivers of disease spread at sub-kilometer scalesdemonstrated in the city of Ahmedabad, India, which has been classified as aSARS-CoV-2 hotspot. We find that in addition to the contact network andpopulation density, road connectivity patterns and ease of transit are stronglycorrelated with the rate of transmission of the disease. Given the limitedaccess to real-time traffic data during lockdowns, we generate roadconnectivity networks using open-source imageries and travel patterns fromopen-source surveys and government reports. Within the proposed framework, wethen analyze the relative merits of social distancing, enforced lockdowns, andenhanced testing and quarantining mitigating the disease spread.

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