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

Area of Science:

• Epidemiology
• Network Science
• Urban Planning

Background:

• Infectious disease transmission is amplified in dense urban settings due to social networks and mobility.
• Traditional epidemiological models struggle with real-time data limitations, especially during global events like the SARS-CoV-2 pandemic.
• Population density alone doesn't explain varied disease spread; high-resolution analysis of drivers is needed.

Purpose of the Study:

• To investigate the impact of containment zones on urban travel patterns.
• To model infectious disease spread at sub-kilometer scales in a dense urban environment (Ahmedabad, India).
• To identify key drivers of disease transmission beyond density and contact networks.

Main Methods:

• Utilized a dynamical network-based infectious disease model.
• Generated road connectivity networks using open-source imagery.
• Incorporated travel patterns from open-source surveys and government reports.
• Analyzed the effectiveness of social distancing, lockdowns, and testing/quarantining strategies.

Main Results:

• Road connectivity and ease of transit are significant factors in disease transmission rates, alongside contact networks and population density.
• Micro-containment zones, coupled with high road network density, show promise in outbreak containment.
• The study evaluated the comparative benefits of various public health interventions.

Conclusions:

• Micro-containment zones combined with enhanced testing are effective strategies for controlling outbreaks in urban areas with high road network density.
• This approach provides a framework for managing disease spread when real-time mobility data is unavailable.
• The findings support targeted interventions for mitigating infectious disease transmission in cities.