An algorithm to estimate the real time secondary infections in sub-urban bus travel: COVID-19 epidemic experience at Chennai Metropolitan city India

Globalization, global climatic changes, and human behavior pose threats to highly pathogenic avian influenza (HPAI) virus spillover from animals to human. Current SARS-CoV2 transmission continues in several countries despite drastic reduction in COVID-19 cases following world-wide containment measures including RNA vaccines. China reimposed lockdown in November 2022 following the surge in commercial hubs. Urban population density and intracity travel in over-crowded public transport play crucial roles in early transition to an exponential phase of the epidemic in metro-cities. Based on the SARS-CoV2 transmission during the lockdown period in Chennai metro-city, we developed an algorithm that mimics a real-time scenario of passengers boarding and deboarding at each bus-stop on a trip of 36.1 km in 21G bus service in Chennai city to understand the pattern of secondary infections on a daily basis. The algorithm was simulated to estimate R0, and the COVID-19 secondary infections was estimated for each bus trip. Results showed that the R0 depended on the boarding and deboarding of the infected individuals at various bus stops. R0 varied from 0 to 1.04, each trip generated 5–9 secondary infections and four bus stops as potential locations for a higher transmission level. More than 80% of the working population in metro-cities depends on unorganized sectors, and separate mitigation strategies must be in place for successful epidemic containment. The developed algorithm has significant public health relevance and can be utilized to draw necessary containment plans in near future in the event of new COVID-19 wave or any other similar epidemic.