Shweta Bansal

The paper models how fragmented social networks affect the spread of social behaviors versus infectious diseases, finding that complex contagions are less hindered by subgroup structure than simple contagions.

The paper uses machine learning to analyze skin lesions and rake marks in bottlenose dolphins to assess disease vulnerability and environmental influences on health.

This study analyzes gender and race/ethnicity imbalances in infectious disease dynamics research using machine learning to infer author identities from publications 2000-2019, revealing persistent disparities in authorship and citation practices.

This study develops a data-driven epidemiological model for bottlenose dolphins to understand disease spread and improve outbreak preparedness by analyzing spatial and contact structures.

This study examines how social breathing behavior in bottlenose dolphins influences respiratory virus transmission risk by analyzing contact patterns and demographic differences.

This paper develops a machine learning model to detect skin lesions in bottlenose dolphins and estimates lesion prevalence, finding high model accuracy and a positive relationship between lesion presence and gregariousness.