Abstract
BackgroundThe 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.FindingsWhile 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.ScopeOur 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.
Highlights
Modern history is witness to several infectious disease pandemics which have shaped our knowledge of their epidemiology, transmission, and management (Shearer et al 2020)
Mathematical models of infectious diseases were dependent on the classification of individuals on their epidemiological status based on their potential ability to host and transmit a pathogen: Susceptible, Infectious, and Recovered [SIR] (Keeling et al 2001; Keeling and Danon 2009; Anderson and May 1991)
Our analysis shows that Ahmedabad’s central region has a high Betweenness Centrality rank (BC rank), demonstrating the priority intersection that can be vulnerable to disruption due to heavy traffic
Summary
Modern history is witness to several infectious disease pandemics which have shaped our knowledge of their epidemiology, transmission, and management (Shearer et al 2020). The HIV pandemic is characterised by a chronic infective state The transmission of such a sexually transmitted infection is dependent on the host immune status, the infected individual’s viral load, and multiple social aspects like sexual practices of interactions between multiple structured risk groups within the population. In such a situation, the variability in infectious state predicts the progression of the epidemic and stochasticity merits more complex modelling (Wearing et al 2005). The rapid and global transmission of the novel coronavirus (SARSCoV-2) has led to unprecedented lockdowns at global and regional scales, leaving the archived datasets to limited use
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