Modelling the impact of the Omicron BA.5 subvariant in New Zealand.

New Zealand experienced a wave of the Omicron variant of SARS-CoV-2 in early 2022, which occurred against a backdrop of high two-dose vaccination rates, ongoing roll-out of boosters and paediatric doses, and negligible levels of prior infection. New Omicron subvariants have subsequently emerged with a significant growth advantage over the previously dominant BA.2. We investigated a mathematical model that included waning of vaccine-derived and infection-derived immunity, as well as the impact of the BA.5 subvariant which began spreading in New Zealand in May 2022. The model was used to provide scenarios to the New Zealand Government with differing levels of BA.5 growth advantage, helping to inform policy response and healthcare system preparedness during the winter period. In all scenarios investigated, the projected peak in new infections during the BA.5 wave was smaller than in the first Omicron wave in March 2022. However, results indicated that the peak hospital occupancy was likely to be higher than in March 2022, primarily due to a shift in the age distribution of infections to older groups. We compare model results with subsequent epidemiological data and show that the model provided a good projection of cases, hospitalizations and deaths during the BA.5 wave.

Simulating the impact of vaccination rates on the initial stages of a COVID-19 outbreak in Aotearoa New Zealand with a stochastic model.

AIM: The August 2021 COVID-19 outbreak in Auckland caused the New Zealand Government to transition from an elimination strategy to suppression, which relies heavily on high vaccination rates in the population. As restrictions ease and as COVID-19 spreads throughout New Zealand, there is a need to understand how different levels of vaccination will impact the initial stages of COVID-19 outbreaks that are seeded around the country. METHOD: A stochastic branching process model is used to simulate the initial spread of a COVID-19 outbreak for different vaccination rates. RESULTS: High vaccination rates are effective at minimising the number of infections and hospitalisations. Increasing vaccination rates from 20% (approximate value at the start of the August 2021 outbreak) to 80% (approximate proposed target) of the total population can reduce the median number of infections that occur within the first four weeks of an outbreak from 1011 to 14 (25th and 75th quantiles of 545-1602 and 2-32 for V=20% and V=80%, respectively). As the vaccination rate increases, the number of breakthrough infections (infections in fully vaccinated individuals) and hospitalisations of vaccinated individuals increases. Unvaccinated individuals, however, are 3.3x more likely to be infected with COVID-19 and 25x more likely to be hospitalised. CONCLUSION: This work demonstrates the importance of vaccination in protecting individuals from COVID-19, preventing high caseloads, and minimising the number of hospitalisations and hence limiting the pressure on the healthcare system.

Likelihood of infecting or getting infected with COVID-19 as a function of vaccination status, as investigated with a stochastic model for Aotearoa New Zealand for Delta and Omicron variants.

AIM: The New Zealand Government has transitioned from the Alert Level framework, which relied on Government action and population level controls, to the COVID-19 Protection Framework, which relies on vaccination rates and allows for greater freedoms (for the vaccinated). Under the COVID-19 Protection Framework with and current widespread community transmission of Omicron, there is significant interest in understanding the relative risk of spreading COVID-19 posed by unvaccinated, vaccinated, and boosted individuals. METHODS: A stochastic branching process model is used to simulate the spread of COVID-19 for outbreaks seeded by unvaccinated, vaccinated, or boosted individuals. The likelihood of infecting or getting infected with COVID-19 is calculated based on vaccination status. The model is applied to both the Delta and Omicron variants. RESULTS: For the Delta variant, a vaccinated traveller infected with COVID-19 is 9x less likely to seed an outbreak than an unvaccinated traveller infected with COVID-19; however, for the Omicron variant, there is little difference between outbreaks seeded by unvaccinated and vaccinated individuals (boosted individuals are slightly less likely to seed large outbreaks). For the Delta variant, unvaccinated individuals are responsible for 87% of all infections whereas only 3% of infections are from vaccinated-to-vaccinated when normalised by population. Therefore, a vaccinated individual is 6.8x more likely to be infected by an unvaccinated individual than by a vaccinated individual. For the Omicron variant, unvaccinated individuals are responsible for 45% of all infections compared to 39% for vaccinated (two doses) and 15% for boosted (three doses) individuals when normalised by population. Despite the vaccine being less effective at preventing breakthrough transmission for Omicron, only 3% of all infections are from boosted-to-boosted individuals when normalised by population, indicating that three doses of the vaccine provides good protection from infection and breakthrough transmission. CONCLUSIONS: This work demonstrates that most new infections are caused by unvaccinated individuals, especially for the Delta variant. These simulations illustrate the importance of vaccination in stopping individuals from becoming infected with COVID-19 and in preventing onward transmission. For Omicron, individuals vaccinated with two doses are only slightly less likely to spread COVID-19 than those who are unvaccinated. This work suggests that for the current Omicron outbreak the COVID-19 Protection Framework be updated to distinguish between those who have received two primary doses of the Pfizer-BioNTech vaccine (vaccinated individuals) and those who have received three doses (boosted individuals).