The importance of non-pharmaceutical interventions during the COVID-19 vaccine rollout

Abstract

The promise of efficacious vaccines against SARS-CoV-2 is fulfilled and vaccination campaigns have started worldwide. However, the fight against the pandemic is far from over. Here, we propose an age-structured compartmental model to study the interplay of disease transmission, vaccines rollout, and behavioural dynamics. We investigate, via in-silico simulations, individual and societal behavioural changes, possibly induced by the start of the vaccination campaigns, and manifested as a relaxation in the adoption of non-pharmaceutical interventions. We explore different vaccination rollout speeds, prioritization strategies, vaccine efficacy, as well as multiple behavioural responses. We apply our model to six countries worldwide (Egypt, Peru, Serbia, Ukraine, Canada, and Italy), selected to sample diverse socio-demographic and socio-economic contexts. To isolate the effects of age-structures and contacts patterns from the particular pandemic history of each location, we first study the model considering the same hypothetical initial epidemic scenario in all countries. We then calibrate the model using real epidemiological and mobility data for the different countries. Our findings suggest that early relaxation of safe behaviours can jeopardize the benefits brought by the vaccine in the short term: a fast vaccine distribution and policies aimed at keeping high compliance of individual safe behaviours are key to mitigate disease resurgence.

Fig 1. Demographic, mixing patterns, and relative deaths difference for different rollout speeds and prioritization strategies.

A) Demographic characteristics and mixing patterns of the six countries considered are shown: percentage of the population aged over 65, contacts intensity for different age groups, and a measure of inter-generational mixing. B) Relative deaths difference is computed as the fraction of deaths that are avoided with a vaccine with respect to a baseline simulation without vaccine. We display results of the simulations for three vaccine rollout speed and prioritization strategies. Other parameters used are γ = 0.5, R₀ = 1.15, r = 1.3, VE_S = 70% and VE_Symp such that VE = 90%, 0.5% of initially infected, 10% of initially immune individuals, and simulations length is set to 1 year.

Fig 2. Interplay between vaccine efficacy and rollout speed.

Contourplot of the relative deaths difference in the scenario with a mild behavioural response (α = 1) for different combinations of VE and r_V. We let VE_S vary between 30% and 70% and we choose different VE_Symp such that the overall efficacy VE vary between 50% and 90%. A 30% reduction of deaths is highlighted with a red dashdotted line. The black dashed line highlight the 30% death drop achieved with a vaccination campaign without the behavioural component (α = 0). Vaccination Strategy 1 is considered, parameters used are γ = 0.5, R₀ = 1.15, r = 1.3, 0.5% of initially infected, 10% of initially immune individuals, and simulations length is set to 1 year.

Fig 3. Giving up NPIs during rollout may nullify the benefits brought by the vaccine.

A) We display for the different countries the boxplot of the calibrated infection parameter β, the projected number of symptomatic infectious cases per 100′000 and the fraction of recovered at the 2021/01/01, start of the vaccination campaign in our simulation. We also report the ratio between the leading eigenvalue of the contacts matrix considering restrictions and of the baseline contacts matrix with no restrictions. B) We display the median relative deaths difference for the calibrated model in the different countries. We consider the three vaccination strategies and two possible rollout speed: r_V = 1% (faster rollout), and r_V = 0.25% (slower rollout). We run the model over the period 2021/01/01–2021/06/01. Other parameters are γ = 0.5, α = 10², r = 1.3, VE_S = 70% (VE = 90%).

Fig 4. Compartmental model.

We consider an extension of the classic SLIR model adding compartments for presymptomatic (P) and asymptomatic (A). We also design compartments for vaccinated (V), dead (D describes individuals that will die with a delay of Δ⁻¹ entering the compartment D^o), susceptible (S^NC) and vaccinated (V^NC) individuals that do not comply with COVID-safe behaviours. The vaccine offers a protection VE_S against infection and VE_Symp against symptoms that can lead to severe outcomes such as death. The transmission rate for susceptible is β and for susceptible non-compliant rβ (r > 1). The parameter α regulates the transition from compliant to non-compliant behaviours, while γ regulates the opposite flow. Arrows describe the transitions between compartments. For simplicity of visualization, we do not display the compartments mediating the different transitions. For example, the transition S → L is mediated by the infectious compartments (P, A, I, P^V, I^V, A^V). The compartmentalization is then extended to account for empirical age-structure and contact matrices.

Fig 5. Calibration results.

For each country we represent the observed and simulated weekly deaths (median, 50% and 95% confidence intervals).