Estimating Subnational Age-Specific Contact Patterns using Multilevel Regression with Poststratification

The spread and transmission dynamics of directly transmitted airborne pathogens, such as SARS-CoV-2, are fundamentally determined by in-person contact patterns. Reliable quantitative estimates of contact patterns are critical to modeling and reducing the spread of directly transmitted infectious diseases. While national-level contact data are available in many countries, including the United States, local-level estimates of age-specific contact patterns are key since disease dynamics and public health policy vary by geography. However, collecting contact data for each state would require a very large sample and be prohibitively expensive. To overcome this challenge, we develop a flexible model to estimate age-specific contact patterns at the subnational level using national-level interpersonal contact data. Our model is based on dynamic multilevel regression with poststratification. We apply this approach to a national sample of interpersonal contact data collected by the Berkeley Interpersonal Contact Study (BICS). Results illustrate important state-level variation in levels and trends of contacts across the US.