The impact of COVID-19 vaccination campaigns accounting for antibody-dependent enhancement

Abstract

Background COVID-19 vaccines are approved, vaccination campaigns are launched, and worldwide return to normality seems within close reach. Nevertheless, concerns about the safety of COVID-19 vaccines arose, due to their fast emergency approval. In fact, the problem of antibody-dependent enhancement was raised in the context of COVID-19 vaccines. Methods and findings We introduce a complex extension of the model underlying the pandemic preparedness tool CovidSim 1.1 (http://covidsim.eu/) to optimize vaccination strategies with regard to the onset of campaigns, vaccination coverage, vaccination schedules, vaccination rates, and efficiency of vaccines. Vaccines are not assumed to immunize perfectly. Some individuals fail to immunize, some reach only partial immunity, and—importantly—some develop antibody-dependent enhancement, which increases the likelihood of developing symptomatic and severe episodes (associated with higher case fatality) upon infection. Only a fraction of the population will be vaccinated, reflecting vaccination hesitancy or contraindications. The model is intended to facilitate decision making by exploring ranges of parameters rather than to be fitted by empirical data. We parameterized the model to reflect the situation in Germany and predict increasing incidence (and prevalence) in early 2021 followed by a decline by summer. Assuming contact reductions (curfews, social distancing, etc.) to be lifted in summer, disease incidence will peak again. Fast vaccine deployment contributes to reduce disease incidence in the first quarter of 2021, and delay the epidemic outbreak after the summer season. Higher vaccination coverage results in a delayed and reduced epidemic peak. A coverage of 75%–80% is necessary to prevent an epidemic peak without further drastic contact reductions. Conclusions With the vaccine becoming available, compliance with contact reductions is likely to fade. To prevent further economic damage from COVID-19, high levels of immunization need to be reached before next year’s flu season, and vaccination strategies and disease management need to be flexibly adjusted. The predictive model can serve as a refined decision support tool for COVID-19 management.

Seroprevalence of antibodies against SARS-CoV-2 virus in Northern Serbia (Vojvodina): A four consecutive sentinel population-based survey study

“…Apart from these epidemiological measures, a mass immunization is crucial in controlling the COVID-19 epidemic [ 32 , 33 ]. Vaccination campaigns, not only provide individual protection for those that have been immunized, but they also aim for herd immunity, given that a sufficient number of people got the vaccine [ 34 ]. By June 9, 2021, as much as 42% of population of Vojvodina has been vaccinated with at least one dose of vaccine [ 9 ], but the desirable threshold of 60–70% (or higher) of immune people has not been reached yet.…”

Influence of Covid-19 Vaccination Coverage on Case Fatality Rate

“…This study shed a light on the importance of Covid-19 vaccination coverage in decreasing CFR, an unmeasured factor before. Although vaccines might protect from the severe disease [12] which can decrease the CFR per se, the early detection of such ndings in relatively low vaccination coverage together with the low number of infections that encountered in vaccinated patient. These infections if encountered in vaccinated patients are certainly very low and incomparable to infections in nonvaccinated patients.…”

Predicting the impact of COVID-19 vaccination campaigns – a flexible age-dependent, spatially-stratified predictive model, accounting for multiple viral variants and vaccines

“…Implicitly, SEIR models assume exponential-distributed waiting times to progress through the infectious phases. This yields unrealistic epidemic dynamics [33]. To avoid this behavior, each phase of the infection is modeled by a number of equivalent sub-stages through which individuals progress successively.…”

“…As a consequence the waiting times to progress between compartments are Erlang-distributed (cf. [32][33][34][35]).…”