Effects of Mitigation and Control Policies in Realistic Epidemic Models Accounting for Household Transmission Dynamics

Background: Compartmental infectious disease (ID) models are often used to evaluate nonpharmaceutical interventions (NPIs) and vaccines. Such models rarely separate within-household and community transmission, potentially introducing biases in situations in which multiple transmission routes exist. We formulated an approach that incorporates household structure into ID models, extending the work of House and Keeling. Design: We developed a multicompartment susceptible-exposed-infectious-recovered-susceptible-vaccinated (MC-SEIRSV) modeling framework, allowing nonexponentially distributed duration in exposed and infectious compartments, that tracks within-household and community transmission. We simulated epidemics that varied by community and household transmission rates, waning immunity rate, household size (3 or 5 members), and numbers of exposed and infectious compartments (1–3 each). We calibrated otherwise identical models without household structure to the early phase of each parameter combination’s epidemic curve. We compared each model pair in terms of epidemic forecasts and predicted NPI and vaccine impacts on the timing and magnitude of the epidemic peak and its total size. Meta-analytic regressions characterized the relationship between household structure inclusion and the size and direction of biases. Results: Otherwise similar models with and without household structure produced equivalent early epidemic curves. However, forecasts from models without household structure were biased. Without intervention, they were upward biased on peak size and total epidemic size, with biases also depending on the number of exposed and infectious compartments. Model-estimated NPI effects of a 60% reduction in community contacts on peak time and size were systematically overestimated without household structure. Biases were smaller with a 20% reduction NPI. Because vaccination affected both community and household transmission, their biases were smaller. Conclusions: ID models without household structure can produce biased outcomes in settings in which within-household and community transmission differ. Infectious disease models rarely separate household transmission from community transmission. The pace of household transmission may differ from community transmission, depends on household size, and can accelerate epidemic growth. Many infectious disease models assume exponential duration distributions for infected states. However, the duration of most infections is not exponentially distributed, and distributional choice alters modeled epidemic dynamics and intervention effectiveness. We propose a mathematical framework for household and community transmission that allows for nonexponential duration times and a suite of interventions and quantified the effect of accounting for household transmission by varying household size and duration distributions of infected states on modeled epidemic dynamics. Failure to include household structure induces biases in the modeled overall course of an epidemic and the effects of interventions delivered differentially in community settings. Epidemic dynamics are faster and more intense in populations with larger household sizes and for diseases with nonexponentially distributed infectious durations. Modelers should consider explicitly incorporating household structure to quantify the effects of non-pharmaceutical interventions (e.g., shelter-in-place).