Public health and population health refer to the study of daily life factors, prevention efforts, and their effects on the health of populations. Building on the success of our first workshop at NeurIPS 2020, this workshop will focus on data and algorithms related to the non-medical conditions that shape our health including structural, lifestyle, policy, social, behavior and environmental factors. Data that is traditionally used in machine learning and health problems are really about our interactions with the health care system, and this workshop aims to balance this with machine learning work using data on non-medical conditions. We expect contributions on, but not limited to the following areas:

• Data: feature generation from internet/mobile datasets relevant to health, privacy and security challenges related to public health and urban planning data and tasks, ascertainment of data to measure and define factors related to social disparities

• Methods: methods for combining non-clinical and clinical data for public and population health applications, algorithms for public health and urban planning goals, model transport across environments, spatial analyses

• Policy and implementation: ML approaches for mitigating disparities, identifying methodological assumptions that fail in public health and urban planning settings, human and ML interaction in the public health and urban planning context

• Health Topics: ML integration in infectious disease models, improving non-communicable disease surveillance and prediction using ML, health equity