Sensitivity of RT-PCR tests for SARS-CoV-2 through time.

Reverse transcriptase polymerase chain reaction (RT-PCR) tests are the gold standard for detecting recent infection with SARS-CoV-2. RT-PCR sensitivity varies over the course of an individual's infection, related to changes in viral load. Differences in testing methods, and individual-level variables such as age, may also affect sensitivity. Using data from New Zealand, we estimate the time-varying sensitivity of SARS-CoV-2 RT-PCR under varying temporal, biological and demographic factors. Sensitivity peaks 4-5 days post-infection at 92.7% [91.4%, 94.0%] and remains over 88% between 5 and 14 days post-infection. After the peak, sensitivity declined more rapidly in vaccinated cases compared to unvaccinated, females compared to males, those aged under 40 compared to over 40 s, and Pacific peoples compared to other ethnicities. RT-PCR remains a sensitive technique and has been an effective tool in New Zealand's border and post-border measures to control COVID-19. Our results inform model parameters and decisions concerning routine testing frequency.

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.

Modelling Aotearoa New Zealand's COVID-19 protection framework and the transition away from the elimination strategy.

For the first 18 months of the COVID-19 pandemic, New Zealand used an elimination strategy to suppress community transmission of SARS-CoV-2 to zero or very low levels. In late 2021, high vaccine coverage enabled the country to transition away from the elimination strategy to a mitigation strategy. However, given negligible levels of immunity from prior infection, this required careful planning and an effective public health response to avoid uncontrolled outbreaks and unmanageable health impacts. Here, we develop an age-structured model for the Delta variant of SARS-CoV-2 including the effects of vaccination, case isolation, contact tracing, border controls and population-wide control measures. We use this model to investigate how epidemic trajectories may respond to different control strategies, and to explore trade-offs between restrictions in the community and restrictions at the border. We find that a low case tolerance strategy, with a quick change to stricter public health measures in response to increasing cases, reduced the health burden by a factor of three relative to a high tolerance strategy, but almost tripled the time spent in national lockdowns. Increasing the number of border arrivals was found to have a negligible effect on health burden once high vaccination rates were achieved and community transmission was widespread.

Using mechanistic model-based inference to understand and project epidemic dynamics with time-varying contact and vaccination rates.

Epidemiological models range in complexity from relatively simple statistical models that make minimal assumptions about the variables driving epidemic dynamics to more mechanistic models that include effects such as vaccine-derived and infection-derived immunity, population structure and heterogeneity. The former are often fitted to data in real-time and used for short-term forecasting, while the latter are more suitable for comparing longer-term scenarios under differing assumptions about control measures or other factors. Here, we present a mechanistic model of intermediate complexity that can be fitted to data in real-time but is also suitable for investigating longer-term dynamics. Our approach provides a bridge between primarily empirical approaches to forecasting and assumption-driven scenario models. The model was developed as a policy advice tool for New Zealand's 2021 outbreak of the Delta variant of SARS-CoV-2 and includes the effects of age structure, non-pharmaceutical interventions, and the ongoing vaccine rollout occurring during the time period studied. We use an approximate Bayesian computation approach to infer the time-varying transmission coefficient from real-time data on reported cases. We then compare projections of the model with future, out-of-sample data. We find that this approach produces a good fit with in-sample data and reasonable forward projections given the inherent limitations of predicting epidemic dynamics during periods of rapidly changing policy and behaviour. Results from the model helped inform the New Zealand Government's policy response throughout the outbreak.

Modelling the dynamics of infection, waning of immunity and re-infection with the Omicron variant of SARS-CoV-2 in Aotearoa New Zealand.

Aotearoa New Zealand experienced a wave of the Omicron variant of SARS-CoV-2 in 2022 with around 200 confirmed cases per 1000 people between January and May. Waning of infection-derived immunity means people become increasingly susceptible to re-infection with SARS-CoV-2 over time. We investigated a model that included waning of vaccine-derived and infection-derived immunity under scenarios representing different levels of behavioural change relative to the first Omicron wave. Because the durability of infection-derived immunity is a key uncertainty in epidemiological models, we investigated outcomes under different assumptions about the speed of waning. The model was used to provide scenarios to the New Zealand Government, helping to inform policy response and healthcare system preparedness ahead of the winter respiratory illness season. In all scenarios investigated, a second Omicron wave was projected to occur in the second half of 2022. The timing of the peak depended primarily on the speed of waning and was typically between August and November. The peak number of daily infections in the second Omicron wave was smaller than in the first Omicron wave. Peak hospital occupancy was also generally lower than in the first wave but was sensitive to the age distribution of infections. A scenario with increased contact rates in older groups had higher peak hospital occupancy than the first wave. Scenarios with relatively high transmission, whether a result of relaxation of control measures or voluntary behaviour change, did not necessarily lead to higher peaks. However, they generally resulted in more sustained healthcare demand (>250 hospital beds throughout the winter period). The estimated health burden of Covid-19 in the medium term is sensitive to the strength and durability of infection-derived and hybrid immunity against reinfection and severe illness, which are uncertain.

Real-time estimation of the effective reproduction number of SARS-CoV-2 in Aotearoa New Zealand.

During an epidemic, real-time estimation of the effective reproduction number supports decision makers to introduce timely and effective public health measures. We estimate the time-varying effective reproduction number, Rt , during Aotearoa New Zealand's August 2021 outbreak of the Delta variant of SARS-CoV-2, by fitting the publicly available EpiNow2 model to New Zealand case data. While we do not explicitly model non-pharmaceutical interventions or vaccination coverage, these two factors were the leading drivers of variation in transmission in this period and we describe how changes in these factors coincided with changes in Rt . Alert Level 4, New Zealand's most stringent restriction setting which includes stay-at-home measures, was initially effective at reducing the median Rt to 0.6 (90% CrI 0.4, 0.8) on 29 August 2021. As New Zealand eased certain restrictions and switched from an elimination strategy to a suppression strategy, Rt subsequently increased to a median 1.3 (1.2, 1.4). Increasing vaccination coverage along with regional restrictions were eventually sufficient to reduce Rt below 1. The outbreak peaked at an estimated 198 (172, 229) new infected cases on 10 November, after which cases declined until January 2022. We continue to update Rt estimates in real time as new case data become available to inform New Zealand's ongoing pandemic response.

An assessment of the potential impact of the Omicron variant of SARS-CoV-2 in Aotearoa New Zealand.

New Zealand delayed the introduction of the Omicron variant of SARS-CoV-2 into the community by the continued use of strict border controls through to January 2022. This allowed time for vaccination rates to increase and the roll out of third doses of the vaccine (boosters) to begin. It also meant more data on the characteristics of Omicron became available prior to the first cases of community transmission. Here we present a mathematical model of an Omicron epidemic, incorporating the effects of the booster roll out and waning of vaccine-induced immunity, and based on estimates of vaccine effectiveness and disease severity from international data. The model considers differing levels of immunity against infection, severe illness and death, and ignores waning of infection-induced immunity. This model was used to provide an assessment of the potential impact of an Omicron wave in the New Zealand population, which helped inform government preparedness and response. At the time the modelling was carried out, the date of introduction of Omicron into the New Zealand community was unknown. We therefore simulated outbreaks with different start dates, as well as investigating different levels of booster uptake. We found that an outbreak starting on 1 February or 1 March led to a lower health burden than an outbreak starting on 1 January because of increased booster coverage, particularly in older age groups. We also found that outbreaks starting later in the year led to worse health outcomes than an outbreak starting on 1 March. This is because waning immunity in older groups started to outweigh the increased protection from higher booster coverage in younger groups. For an outbreak starting on 1 February and with high booster uptake, the number of occupied hospital beds in the model peaked between 800 and 3,300 depending on assumed transmission rates. We conclude that combining an accelerated booster programme with public health measures to flatten the curve are key to avoid overwhelming the healthcare system.