The impact of vaccination scale and pathogenic mutations on disease transmission

Published 08 April, 2022


Since the first COVID-19 case was reported at the end of 2019, the virus SARS-CoV-2 has caused more than 446 million confirmed cases of COVID-19, including more than 6 million deaths as of 8 March 2022 (source: WHO Coronavirus Dashboard). Although much progress has been achieved in vaccine development, all vaccines can only provide partial protection in preventing symptomatic COVID-19 disease and in reducing SARS-CoV-2 infection. In addition, the waning of the vaccine-derived immunity poses further challenges for the effectiveness of vaccine programmes. The emergence of new variant strains is making matters worse by increasing the probability of vaccine breakthrough infections. It is of great interest to model and evaluate the impact of different COVID-19 vaccines and vaccination programmes (by country, region, global level and allocation) on both the disease burden and the trend of the pandemic. This information is urgently needed by health authorities at national and international levels for coordinated, global COVID-19 control efforts. 

This special issue covers a wide range of mathematical modelling methods used to evaluate the role of vaccines and vaccination strategies in the pandemic at national and global levels, and address the influence of new mutations of the predominant strain. 

Featured papers:

The work by Campos (doi: 10.1016/j.idm.2021.05.003) proposed an age-dependent SIR model to describe the evolution of COVID-19. Location-specific (home, work, school, other) parameters were fitted from in-sample data using a random optimisation scheme. Further numerical investigations compared the effectiveness of group-based vaccination policies: preferably to vaccinate those at most risk (elderly) or those who spread the disease the most (the youngest). 

The study by Humphrey et al. (doi: 10.1016/j.idm.2021.06.008) investigated the control measures in South Korea, Italy, Canada and the United States, specifically through the means of testing and tracing by fitting a disease transmission model. The authors also considered the situation in Canada to see how frequent, large-scale testing to find asymptomatic cases and contact tracing can supplement control of COVID-19 when vaccine supply is limited.

Mancuso, Eikenberry and Gumel (doi: 10.1016/j.idm.2021.08.008) assessed the impact of the vaccine-induced, cross-protective efficacy on the spread of COVID-19 in the United States by a two-strain (one wild-type, one variant) and two-group (vaccinated or otherwise) mechanistic mathematical model. Rigorous analysis focused on the threshold vaccination coverage in the population. Considering the uncertainties on the transmissibility of wild and variant strains, as well as the vaccine efficacies, sensitivity and uncertainty analysis was performed to reveal the most influential model parameters on the reproduction number and threshold vaccine coverage.

Several vaccination scenarios in some provinces of Indonesia were assessed in the study by Nuraini et al. (doi: 10.1016/j.idm.2021.09.002) with a compartmental epidemiological model with age groups. It was shown that the timing and period of the vaccination play an important role in determining the effectiveness of vaccination programmes and specific age groups should be prioritised for vaccination.

Different from the traditional individual surveillance testing via nasal swabs and/or saliva, wastewater surveillance was employed as an early warning signal to locate COVID-positive individuals in a study by Wong et al. (doi: 10.1016/j.idm.2021.09.003). It evaluated the sensitivity of SARS-CoV-2 infection rates to wastewater surveillance, and indicated that wastewater surveillance can effectively reduce the number of false positive cases by identifying subpopulations for further individual testing. 

Brozak et al. (doi: 10.1016/j.idm.2021.10.001) proposed a metapopulation modelling approach to project the dynamics of the COVID-19 pandemic in India and Pakistan. The impact of back-and-forth mobility between the two countries was assessed, and its impact on time-to-elimination was also simulated.

The study by Antonelli, Piccolomini and Zama (doi: 10.1016/j.idm.2021.11.001) monitored COVID-19 spread and immunisation in Italy through a hybrid-switched compartmental model. By fitting model to data, it was shown that the model parameter values were switched. Further possible forecast scenarios on disease transmission were simulated with an increased vaccination rate.

A five-compartment mathematical model was proposed to evaluate the importance of vaccination and vaccine efficiency rate during the ongoing pandemic in Mahmud et al. (doi: 10.1016/j.idm.2021.11.002). In addition to vaccine efficacies of the four widely-used vaccines - Pfizer, Moderna, AstraZeneca and Johnson & Johnson - two other special cases (100% efficacy and no vaccine use) were simulated. It was shown that mass vaccination and quick responses were required to control the spread of the disease.

By coupling a simple model of quarantine and testing strategies for international travellers with a model for transmission of the virus in a partly-vaccinated population, Steyn et al. (doi: 10.1016/j.idm.2021.12.006) evaluated the effect of vaccination, border testing and quarantine requirements on the risk of COVID-19 in New Zealand. The model compared different border control strategies for limiting the risk of community outbreaks, which could inform strategies for relaxing border controls in a phased way.

Liu and Lou (doi: 10.1016/j.idm.2022.02.002) reviewed modelling methods to optimise COVID-19 vaccination programmes during vaccine shortages. In particular, different allocation strategies of vaccines were included in various models with differential vaccine efficacies: priority vaccination strategy (including age-group based priority, risk-based prioritisation, geography-stratified prioritisation), time-varying prioritisation, dose stretching by delaying the second dose, and dose sparing with fractional dosing strategy. Furthermore, various model-based outcome measures were summarised as the objective functions for optimisation.

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