Multi-type branching inference on contact trees with application to COVID-19
Mapping how diseases spread through real contact networks, not just genetic sequences.
Researchers developed a mathematical method to extract disease transmission patterns directly from contact-tracing data—who infected whom—without needing genetic sequences. The approach accounts for a key reality that older models miss: some infected people have many contacts while others have few, and this affects how fast disease spreads. When tested on COVID-19 data from India, the method accurately recovered transmission rates and contact patterns.
Public health officials use contact tracing to understand outbreak dynamics, but existing tools struggle to extract transmission rates from incomplete records. This framework turns messy contact-tracing data into precise estimates of who is most likely to spread disease and how many contacts matter, enabling faster identification of superspreaders and better targeting of interventions during future outbreaks.