COVID-19 optimal vaccination policies: a modeling study on efficacy, natural and vaccine-induced immunity responses

Abstract

At the date, Europe and part of North America face the second wave of COVID-19, causing more than 1 300 000 deaths worldwide. Humanity lacks successful treatments, and a sustainable solution is an effective vaccine. Pfizer and the Russian Gamaleya Institute report that its vaccines reach more than 90 % efficacy in a recent press release. If third stage trial results favorable, pharmaceutical firms estimate big scale production of its vaccine candidates around the first 2021 quarter and the World Health organization fix as objective, vaccinate 20 % of the whole population at the final of 2021. However, since COVID-19 is new to our knowledge, vaccine efficacy and induced-immunity responses remain poorly understood. There are great expectations, but few think the first vaccines will be fully protective. Instead, they may reduce the severity of illness, reducing hospitalization and death cases.Further, logistic supply, economic and political implications impose a set of grand challenges to develop vaccination policies. For this reason, health decision-makers require tools to evaluate hypothetical scenarios and evaluate admissible responses.Our contribution answers questions in this direction. According to the WHO Strategic Advisory Group of Experts on Immunization Working Group on COVID-19 Vaccines, we formulate an optimal controlled model to describe vaccination policies that minimize the burden of COVID-19 quantified by the number of disability-adjusted years of life lost. Additionally, we analyze the reproductive vaccination number according to vaccination profiles depending on coverage, efficacy, horizon time, and vaccination rate. We explore scenarios regarding efficacy, coverage, vaccine-induced immunity, and natural immunity via numerical simulation. Our results suggest that response regarding vaccine-induced immunity and natural immunity would play a dominant role in the vaccination policy design. Likewise, the vaccine efficacy would influence the time of intensifying the number of doses in the vaccination policy.

The trade-off between mobility and vaccination for COVID-19 control: a metapopulation modeling approach

“…Each scenario is based on a set of numerical values for the vaccine-associated parameters where C i is the target immunization coverage and T i the target time to achieve that coverage. The vaccination rate u i is obtained from the approximation 1−exp (− u i T i ) = C i [1]. To formulate our scenarios we take into consideration the currently available information of the COVID-19 vaccine candidates [20].…”

How to coordinate vaccination and social distancing to mitigate SARS-CoV-2 outbreaks

“…Early work on optimal vaccination involved age-uniform policies, and on-off poli-cies were found to be optimal [32]. Similarly, Acuna-Zegarra et al [2] use optimal control to compute time-varying vaccination policies and find intense vaccination for a limited time period to be optimal. Buckner et al [11] optimize time-varying age-targeted vaccination policies.…”

“…Similarly, Hogan et al [30] consider optimal age-targeted vaccination where the entire age group is assumed to be vaccinated at a constant rate over the course of one month. Acuna-Zegarra et al [31] use optimal control to conclude that intense vaccination for a limited time period is optimal. Buckner et al [32] optimize time-varying age-targeted vaccination.…”

Modeling the effect of vaccination strategies in an Excel spreadsheet: The rate of vaccination, and not only the vaccination coverage, is a determinant for containing COVID-19 in urban areas

“…In principle, all these rates have to be considered when attempting to render an accurate forecast of the evolution of COVID-19 in a particular territory. Several studies [11,12] have used mathematical modeling to investigate the role of vaccine efficacy and vaccine coverage in the attenuation of the pandemic progression. However, one aspect that remains poorly explored is the assessment of the effect of different vaccination rates on the progression of COVID-19.…”