Modelling the impact of hybrid immunity on future COVID-19 epidemic waves

Abstract

Background: Since the emergence of SARS-CoV-2 (COVID-19), there have been multiple waves of infection and multiple rounds of vaccination rollouts. Both prior infection and vaccination can prevent future infection and reduce severity of outcomes, combining to form hybrid immunity against COVID-19 at the individual and population level. Here, we explore how different combinations of hybrid immunity affect the size and severity of near-future Omicron waves.

Methods: To investigate the role of hybrid immunity, we use an agent-based model of COVID-19 transmission with waning immunity to simulate outbreaks in populations with varied past attack rates and past vaccine coverages, basing the demographics and past histories on the World Health Organization Western Pacific Region.

Results: We find that if the past infection immunity is high but vaccination levels are low, then the secondary outbreak with the same variant can occur within a few months after the first outbreak; meanwhile, high vaccination levels can suppress near-term outbreaks and delay the second wave. Additionally, hybrid immunity has limited impact on future COVID-19 waves with immune-escape variants.

Conclusions: Enhanced understanding of the interplay between infection and vaccine exposure can aid anticipation of future epidemic activity due to current and emergent variants, including the likely impact of responsive vaccine interventions.

Keywords: Epidemiology; Mathematical modelling; Vaccination; Variants.

Fig. 1

Diagram of overall simulation procedure. The core of the simulation uses an agent-based model with an underlying infection transmission model, with multiple primary inputs including immunological parameters and scenario-demography setups. The outputs are then fed into a clinical pathways model that produces clinical outcomes for infected individuals

Fig. 2

Timeline of vaccination schedule and infection seedings with examples of infection time series. There is a vaccination rollout that occurs in three consecutive stages, starting at $t=0$, $t=182$, and $t=364$ and ending at $t=546$. The first wave and the second wave are generated by randomly seeding 100 infections in the population (which could occur due to a super-spreader event, for example) at times $t=225$ and $t=450$. The example time series are for a second wave due to the same variant as the first wave

Fig. 3

Scenarios considered: a exemplar“younger” population demographics, b “older” population demographics (see Appendix A of the Supplementary Material 1 for the construction of these exemplar populations), c 20% vaccination coverage, d 50% vaccination coverage, e 80% vaccination coverage, where the coverage value corresponds to primary vaccination coverage by time $t=364$. Note that c, d, e detail the proportions in the younger population; the proportions in the older population are similar

Fig. 4

Near-future outcomes given past immunity. a Younger population, near-future attack rate; b Older population, near-future attack-rate (the diagonal line represents where past and near-future attack rates are equal); c Younger population, near-future deaths; d Older population, near-future deaths. Note that past attack rate is calculated between $t\in \left(0,,,450\right)$. Past attack rate is dependent on transmission potential, which is different for various simulations, reflecting different populations’ intrinsic transmission. Near-future attack rate and near-future deaths are calculated between $t\in \left[450,,,650\right)$. Note that we have only included simulation results in which the past attack rate is between 20% and 80%

Fig. 5

Near-future attack rate and deaths given past immunity, for a second wave due to a BA.4/BA.5-like immune escape variant. The diagonal line represents where past and near-future attack rates are equal. Note that we have only included simulation results in which the past attack rate is between 20% and 80%