  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.
