Speaker
Description
Understanding the transmission dynamics of infectious diseases within households is crucial for informing and improving public health interventions, but household-level transmission patterns are typically difficult to identify from data. This study presents a likelihood-based inference method which infers epidemiological parameters governing household-level transmission pathways from longitudinal infectious disease testing data, allowing for a detailed understanding of disease spread at the household level. Our method uses a household-based stochastic SEIR model that explicitly accounts for internal transmission within households and external infection pressure from community prevalence . We test the predictive capabilities of our approach by validating against synthetic data with known parameters, and explore its use in practice by applying it to COVID-19 testing data gathered in Vo, Italy during the early stages of the pandemic. Our method highlights critical factors influencing household transmission dynamics, and has the potential to offer valuable insights for optimising targeted interventions such as quarantine strategies and vaccination campaigns. By elucidating the interaction between household and community-level transmission, our work contributes to a more comprehensive understanding of disease spread and lays the groundwork for future epidemic forecasting and control efforts.
