Agent-based modelling of reactive vaccination of workplaces and schools against COVID-19

Abstract

With vaccination against COVID-19 stalled in some countries, increasing vaccine accessibility and distribution could help keep transmission under control. Here, we study the impact of reactive vaccination targeting schools and workplaces where cases are detected, with an agent-based model accounting for COVID-19 natural history, vaccine characteristics, demographics, behavioural changes and social distancing. In most scenarios, reactive vaccination leads to a higher reduction in cases compared with non-reactive strategies using the same number of doses. The reactive strategy could however be less effective than a moderate/high pace mass vaccination program if initial vaccination coverage is high or disease incidence is low, because few people would be vaccinated around each case. In case of flare-ups, reactive vaccination could better mitigate spread if it is implemented quickly, is supported by enhanced test-trace-isolate and triggers an increased vaccine uptake. These results provide key information to plan an adaptive vaccination rollout.

Fig. 1. Modelling reactive vaccination.

a Distribution of workplace size and of school type for the municipality of Metz (Grand Est region, France), used in the simulation study. b Schematic representation of the population structure, the reactive vaccination and contact tracing. The synthetic population is represented as a dynamic multi-layer network, where layers encode contacts in household, workplace, school, community and transport. In the figure, school and workplace layers are collapsed and community and transport are not displayed for the sake of visualisation. Nodes repeatedly appear on both the household and the workplace/school layer. The identification of an infectious individual (in purple in the figure) triggers the detection and isolation of his/her contacts (nodes with orange border) and the vaccination of individuals attending the same workplace/school and belonging to the same household who accept to be vaccinated (green). c Compartmental model of COVID-19 transmission and vaccination. Description of the compartments is reported on the Methods section. d Timeline of events following infection for a case that is detected in a scenario with reactive vaccination. For panels c, d transition rate parameters and their values are described in the Methods and in the Supplementary Information.

Fig. 2. Comparison between vaccination strategies.

a–h Comparison between reactive and non-reactive vaccination strategies for the baseline scenario and different values of initial vaccination coverage. a, d, g Relative reduction (RR) in the attack rate (AR) over the first 2 months for all strategies as a function of the vaccination pace. RR is computed as (AR_ref − AR)/AR_ref with AR_ref being the AR of the reference scenario, where no vaccination campaign is conducted during the course of the simulation and vaccination coverage remains at its initial level. AR is computed from clinical cases. Three initial vaccination coverages are investigated: 15% of adults (low) (a); 40% of adults (intermediate) (d) and 65% of adults (high) (g). b, e, h Weekly incidence of clinical cases for 100,000 inhabitants for the first 8 weeks with different vaccination strategies. The non-reactive scenarios plotted are obtained with the same average daily vaccination pace as for reactive vaccination. Low, intermediate and high vaccination coverages are investigated in b, e, h, respectively. c Number of daily first-dose vaccinations, and number of workplaces/schools (WP/S in the plot) where vaccines are deployed for the same reactive scenario as in a, b—low vacc. cov., with 15% initial vaccine coverage. f AR RR for different initial vaccination coverages. The four strategies are compared at equal numbers of vaccine doses. The baseline epidemic scenario of panels a–h is defined by the following key parameters: R = 1.6; VE_S,1 = 48%, VE_SP,1 = 53%, VE_S,2 = 70%, VE_SP,2 = 73%; initial immunity 32%; initial incidence 160 clinical cases weekly per 100,000 inhabitants; 90% of 60+ vaccinated at the beginning. i AR RR for different vaccine effectiveness levels, assuming intermediate vaccination coverage (40% of adults) and all other parameters as in panels a–h. The baseline vaccine effectiveness values used in the other panels is compared with a worst and a best-case scenario, defined respectively by VE_S,1 = 30%, VE_SP,1 = 35%, VE_S,2 = 53%, VE_SP,2 = 60%, and by VE_S,1 = 65%, VE_SP,1 = 75%, VE_S,2 = 80%, VE_SP,2 = 95%. For each vaccine effectiveness scenario the four strategies are compared at equal numbers of vaccine doses. In panels a, d, f, g, i, data are means over 2000 independent stochastic realisations and error bars are derived from the standard error of the mean—these are smaller than the size of the dot in the majority of cases. In panels b, c, e, h, continuous lines are means over 2000 independent stochastic realisations and the shaded areas are the standard error of the mean (±2SEM)—not visible in panels b, e, h. The distribution of outcomes over all 2000 independent stochastic realisations is provided in Supplementary Fig. 3 comparing all vaccination strategies and considering the parameterisation of Fig. 2e as an example. The following abbreviations were used in the Fig.: vacc. for vaccines, inhab. for inhabitants, cov. for coverage, inc. for incidence, univ. for universities.

Fig. 3. Combined reactive and mass vaccination for managing sustained COVID-19 spread.

a Relative reduction (RR) in the attack rate (AR) over the first 2 months for the combined strategy (mass and reactive) and the mass strategy with the same number of first-dose vaccinations as in the combined strategy during the period. RR is computed with respect to the reference scenario with initial vaccination only, as in Fig. 2. Combined strategy is obtained by running in parallel the mass strategy—from 50 to 250 daily vaccination rate per 100,000 inhabitants—and the reactive strategy. Number of doses displayed in the x-axis of the figure is the total number of doses used by the combined strategy, daily. Corresponding incidence curves are reported in Supplementary Fig. 8. b Number of first-dose vaccinations deployed each day for the combined strategy with different daily vaccines’ capacity limits. c, d, e AR RR for the combined strategy as a function of the average daily number of first-dose vaccinations in the 2-months period. Symbols of different colours indicate: c different values of daily vaccines’ capacity limit; d different time from case detection to vaccine deployment; e different threshold size for the cluster to trigger vaccination. In panel c and e the curve corresponding to mass vaccination only is also plotted for comparison. f Comparison between 100% and baseline vaccination uptake in case of reactive vaccination. Exception for the parameters indicated in the legend we assume in all panels baseline parameter values with intermediate vaccination coverage at the beginning—i.e. R = 1.6; VE_S,1 = 48%, VE_SP,1 = 53%, VE_S,2 = 70%, VE_SP,2 = 73%; initial immunity 32%; initial incidence 160 clinical cases weekly per 100,000 inhabitants; vaccinated at the beginning 90% and 40% for 60+ and <60, respectively. In panels a, c, d–f data are means over 2000 independent stochastic realisations and error bars are derived from the standard error of the mean. In panel b continuous lines are means over 2000 independent stochastic realisations and the shaded areas are the standard error of the mean (±2SEM)—only the standard error of the unlimited case is shown for clarity. The following abbreviations were used in the Fig.: vacc. for vaccines, inhab. for inhabitants, reac. for reactive.

Fig. 4. Combined reactive and mass vaccination for managing a COVID-19 flare-up.

a, b Attack rate (AR) per 100,000 inhabitants in the first 2 months for the enhanced (a) and baseline (b) TTI scenarios described in the main text. Four vaccination strategies are compared: mass only, combined where the reactive vaccination starts at the detection of 1, 5, 10 cases (Comb. cl. s. = 1, 5, 10 in the Fig.). For mass vaccination the number of first-dose vaccinations during the period is the same as in the comb. cl. s = 1 of the same scenario. Except when otherwise indicated parameters are the ones of the baseline epidemic scenario with intermediate vaccination coverage at the beginning—i.e. R = 1.6; VE_S,1 = 48%, VE_SP,1 = 53%, VE_S,2 = 70%, VE_SP,2 = 73%; initial immunity 32%; vaccinated at the beginning 90% and 40% for 60+ and <60, respectively. In both panels, data are means over 8000 independent stochastic realisations and error bars are the standard error of the mean (±2SEM). Corresponding incidence curves are reported in Supplementary Fig. 9. The following abbreviations were used in the Fig.: vacc. for vaccines, inhab. for inhabitants.