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. 2022 Jan 31;20(1):37.
doi: 10.1186/s12916-022-02243-1.

Investigating vaccine-induced immunity and its effect in mitigating SARS-CoV-2 epidemics in China

Hengcong Liu #  1 , Juanjuan Zhang #  1   2   3 , Jun Cai  1 , Xiaowei Deng  1 , Cheng Peng  1 , Xinghui Chen  1 , Juan Yang  1   2   3 , Qianhui Wu  1 , Xinhua Chen  1 , Zhiyuan Chen  1 , Wen Zheng  1 , Cécile Viboud  4 , Wenhong Zhang #  2 , Marco Ajelli #  5 , Hongjie Yu #  6   7   8
Affiliations

Investigating vaccine-induced immunity and its effect in mitigating SARS-CoV-2 epidemics in China

Hengcong Liu et al. BMC Med. .

Abstract

Background: To allow a return to a pre-COVID-19 lifestyle, virtually every country has initiated a vaccination program to mitigate severe disease burden and control transmission. However, it remains to be seen whether herd immunity will be within reach of these programs.

Methods: We developed a compartmental model of SARS-CoV-2 transmission for China, a population with low prior immunity from natural infection. Two vaccination programs were tested and model-based estimates of the immunity level in the population were provided.

Results: We found that it is unlikely to reach herd immunity for the Delta variant given the relatively low efficacy of the vaccines used in China throughout 2021 and the lack of prior natural immunity. We estimated that, assuming a vaccine efficacy of 90% against the infection, vaccine-induced herd immunity would require a coverage of 93% or higher of the Chinese population. However, even when vaccine-induced herd immunity is not reached, we estimated that vaccination programs can reduce SARS-CoV-2 infections by 50-62% in case of an all-or-nothing vaccine model and an epidemic starts to unfold on December 1, 2021.

Conclusions: Efforts should be taken to increase population's confidence and willingness to be vaccinated and to develop highly efficacious vaccines for a wide age range.

Keywords: COVID-19; Delta variant; Herd immunity; SLIR model; Vaccination program.

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Conflict of interest statement

H.Y. has received research funding from Sanofi Pasteur, GlaxoSmithKline, Yichang HEC Changjiang Pharmaceutical Company, and Shanghai Roche Pharmaceutical Company. M.A. has received research funding from Seqirus. None of those research funding is related to COVID-19. All other authors report no competing interests.

Figures

Fig. 1
Fig. 1
Time series of vaccine coverage, daily incidence, effective reproductive number, and proportion of immune individuals. a Age-specific vaccine coverage over time for strategy 1. The dotted lines correspond the start of epidemic. The inserted table shows the age-specific coverage for the two key time points (the start of epidemic (i.e., December 1, 2021) and the time that the coverage keeps constant (i.e., March 11)). The line corresponds to the mean value, while the shaded area represents 95% CI. b As a, but for strategy 2. c Daily incidence per 10,000 for strategy 1 (mean and 95% CI). d As c, but for strategy 2. e Effective reproduction number Re over time (mean and 95% CI) for strategy 1. The shaded area in gray indicates the epidemic threshold Re =1. The numbers around the shaded area indicate when Re cross this threshold (i.e., January 31) for strategy 1. f As e, but for strategy 2. g Proportion of immune individuals due to either natural infection or vaccination over time for strategy 1. h As g, but for strategy 2
Fig. 2
Fig. 2
Disease burdens of COVID-19 in the baseline scenario. a Cumulative number of infections per 10,000 individuals after 1 simulated year for reference scenario and two vaccination strategies using “all-or-nothing” vaccine model (mean and 95% CI). b Reduction in infections (mean and 95% CI) with respect to the reference scenario in different age groups and the total population. The 95% CI of the reduction may cross 0 as the burden between reference scenario and vaccination scenario is approximately the same in some simulations. We thus trimmed the lower limit of 95% CI at 0 through the manuscript. c, d as for a, b, but for death. e–h as for a–d, but for “leaky” vaccine model
Fig. 3
Fig. 3
Impact of delaying the start of the epidemic and adopting NPIs. a Effective reproduction number Re (mean and 95% CI) as a function of vaccine coverage at the time when infection is seeded. Colors refer to the scenario of delaying the start of the epidemic to different date. The shaded area in gray indicates Re ≤1. b Cumulative number of infections per 10,000 individuals after 1 simulated year for reference scenario and two vaccination strategies (mean and 95% CI). c Reduction in infections (mean and 95% CI) with respect to the reference scenario. d As a, but for net reproduction number Rt (mean and 95% CI) adopting different intensity of NPIs. e As b, but for the scenario of adopting different intensity of NPIs. f As c, but for the scenario of adopting different intensity of NPIs
Fig. 4
Fig. 4
Impact of delaying the start of the epidemic start and adopting NPIs on net reproduction number. a Net reproduction number Rt as a function of formula image and epidemic start date for strategy 1. The bold line in black indicates Rt =1. b As a, but for strategy 2
Fig. 5
Fig. 5
The impact of vaccine efficacy and vaccine coverage on the effective reproduction number. The bold line in black indicates the herd immunity threshold Re =1
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Update of

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