Modelling policy combinations of vaccination and transmission suppression of SARS-CoV-2 in Rio de Janeiro, Brazil

Abstract

COVID-19 vaccination in Brazil required a phased program, with priorities for age groups, health workers, and vulnerable people. Social distancing and isolation interventions have been essential to mitigate the advance of the pandemic in several countries. We developed a mathematical model capable of capturing the dynamics of the SARS-CoV-2 dissemination aligned with social distancing, isolation measures, and vaccination. Surveillance data from the city of Rio de Janeiro provided a case study to analyze possible scenarios, including non-pharmaceutical interventions and vaccination in the epidemic scenario. Our results demonstrate that the combination of vaccination and policies of transmission suppression potentially lowered the number of hospitalized cases by 380+ and 66+ thousand cases, respectively, compared to an absence of such policies. On top of transmission suppression-only policies, vaccination impacted more than 230+ thousand averted hospitalized cases and 43+ thousand averted deaths. Therefore, health surveillance activities should be maintained along with vaccination planning in scheduled groups until a large vaccinated coverage is reached. Furthermore, this analytical framework enables evaluation of such scenarios.

Keywords: COVID-19; SARS-CoV-2; Vaccination.

Fig. 1

Schematic diagram of the model compartments.

Fig. 2

Model results for new daily hospitalizations and cases of SARI in Rio de Janeiro. Notified cases of SARI in Rio de Janeiro are represented by black lines, other colors represent the different simulated vaccination scenarios: vaccination with the applied restrictions (pink), no vaccination but applying the same restrictions as the pink case (green), and vaccination with lockdown scenario (blue). Red lines represents the median values in each scenario.

Fig. 3

Different scenarios comparing prevented deaths and hospitalizations, and cumulative deaths and hospitalizations due to SARI. To calculate the prevented deaths and hospitalizations, we used our model to calculate a scenario where no restrictions and no vaccination were applied, the cumulative deaths and hospitalization curves of this scenario was our reference to calculate the absolute the number of prevention.

Fig. 4

Different scenarios model comparison. A different color identifies each intervention. The points represent the stochastic calculation done with the model considering the given probabilities with 100 iterations per day. The red lines are means of each intervention. The used parameters are given in Table 1, with the exception of R₀, which is 3.5.

Fig. 5

Normalized death and hospitalization profiles for different intervention scenarios. Normalized values are calculated by the quotient of each daily new hospitalization or death by the highest hospitalization or death of the group with most hospitalizations or death through the pandemic.