A general model for the demographic signatures of the transition from pandemic emergence to endemicity

Li, R.; Metcalf, C. J. E.; Stenseth, N. C.; Bjornstad, O. N.

Anticipating the medium- and long-term trajectory of pathogen emergence has acquired new urgency given the ongoing COVID-19 pandemic. For many human pathogens the burden of disease depends on age and prior exposure. Understanding the intersection between human population demography and transmission dynamics is therefore critical. Here, we develop a realistic age-structured (RAS) mathematical model that integrates demography, social mixing and immunity to establish the suite of possible scenarios of future age-incidence and burden of mortality. With respect to COVID-19, we identify a plausible transition in the age-structure of risks once the disease reaches seasonal endemism, whether assuming long-lasting or brief protective immunity, and across a range of assumptions of relative severity of primary versus subsequent reinfections. We train the model using diverse real-world demographies and age-structured social mixing patterns to bound expectations for changing age-incidence and disease burden. The mathematical framework is flexible and can help tailoring mitigation strategies countries worldwide with varying demographies and social mixing patterns.