We wrote a technical note presenting a simulation study on the effect of the Covid Safe Ticket (CST) in limiting the number of transmissions that might occur during events. While the model used to simulate transmission events relies on simplistic assumptions, the set of simulation runs, and the sensitivity analyses performed highlight characteristics that can affect the effectiveness of the CST.

Study highlights

• We explored characteristics related to the event (i.e., number of attendees, number of index cases), SARS-CoV-2 infectiousness (i.e., basic reproduction number), vaccine-induced immunity (i.e., vaccine effectiveness against infectiousness and vaccine effectiveness against susceptibility to infection), test sensitivity and vaccination coverage of attendees.

• For each simulation scenario, we compared three strategies: everyone can join the event (No CST), all people present need to be vaccinated, recently tested or recently infected (CST) and all the individuals (vaccinated and non-vaccinated) need to take a COVID-19 test to attend the event (CST-X).

• Results indicate that while some characteristics such as the event size, the basic reproduction number, and the proportion of index cases affect the total number of infections, other quantities such as the vaccination coverage at the event affect the relative effectiveness of the strategies.

• In the baseline scenario, which is characterized by high vaccination coverage and low effectiveness, the use of CST decreased the number of infections that might take place during an event by 13-15%. However, when the vaccination coverage at an event is low, (i.e., 20%), the effectiveness of a CST strategy substantially increased since a higher proportion of index cases is unvaccinated and therefore tested (relative difference with No CST is 54%).

• In this work we simulated infections that take place during an isolated event at a specific time in the pandemic by adopting disease estimated for the Delta variant. To account for the number of infections generated at the population level, it would be necessary to embed this model in a more general framework that accounts for all human-to-human interactions taking place before and after the events and in the vicinity of the events.

• We assumed a homogeneous population, therefore no individual characteristics (e.g., age) are considered. We also did not distinguish between symptomatic and asymptomatic infections. We did not account for characteristics of the environment, e.g. ventilation, and we assumed that susceptibility to infection is driven exclusively by vaccine-induced immunity. These assumptions might affect the effectiveness of CST measures when different viral load profiles, vaccine effectiveness or coverage values are heterogeneously distributed among the attendees.

The full report from October 2022 is available here.