Audrey Lustig

Invasive mammal eradications are increasingly attempted across large, complex landscapes. Sequentially controlled management zones can be at risk of reinvasion from adjacent uncontrolled areas, and managers must weigh the relative benefits of ensuring complete elimination from a zone or minimizing reinvasion risk. This is complicated in urban areas, where habitat heterogeneity and a lack of baseline ecological knowledge increase uncertainty. We applied a spatial agent‐based model to predict the reinvasion of a well‐studied species, the brushtail possum (Trichosurus vulpecula), across an urban area onto a peninsula that is the site of an elimination campaign in Aotearoa New Zealand. We represented fine‐scale urban habitat heterogeneity in a land cover layer and tested management scenarios that varied four factors: the density of possums remaining following an elimination attempt, the maintenance trap density on the peninsula, and effort expended toward preventing reinvasion by means of a high‐density trap buffer at the peninsula isthmus or control of the source population adjacent to the peninsula. We found that achieving complete elimination on the peninsula was crucial to avoid rapid repopulation. The urban isthmus was predicted to act as a landscape barrier and restrict immigration onto the peninsula, but reliance on this barrier alone would fail to prevent repopulation. In combination, complete elimination, buffer zone, and source population control could reduce the probability of possum repopulation to near zero. Our findings support urban landscape barriers as one tool for sequential invasive mammal elimination but reaffirm that novel methods to expose residual individuals to control will be necessary to secure elimination in management zones. Work to characterize the urban ecology of many invasive mammals is still needed.

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.

Background: Reverse transcription polymerase chain reaction (RT-PCR) tests are the gold standard for detecting recent infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Reverse transcription 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.Methods: Using data from New Zealand, we estimate the time-varying sensitivity of SARS-CoV-2 RT-PCR under varying temporal, biological, and demographic factors.Results: Sensitivity peaks 4–5 days postinfection at 92.7% (91.4%–94.0%) and remains over 88% between 5 and 14 days postinfection. After the peak, sensitivity declined more rapidly in vaccinated cases compared with unvaccinated, females compared with males, those aged under 40 compared with over 40s, and Pacific peoples compared with other ethnicities.Conclusions: Reverse transcription PCR remains a sensitive technique and has been an effective tool in New Zealand’s border and postborder measures to control coronavirus disease 2019. Our results inform model parameters and decisions concerning routine testing frequency.

Estimating the longevity of an individual's immune response to the Sars-Cov-2 virus is vital for future planning, particularly of vaccine requirements. Neutralising antibodies (Nabs) are increasingly being recognised as a correlate of protection and whilst there are many studies which follow the response of a cohort of people, each study alone is not enough to predict the long term response. Studies use different assays to measure Nabs making them hard to combine. We present a modelling method which can combine multiple datasets and can be updated as more detailed data becomes available. Combining data from six published datasets we predict that after a short period of rapid decay the half-life of the NAb response is approximately one year giving optimism that the response will be long-lived.

Models of contact tracing often over-simplify the effects of quarantine and isolation on disease transmission. We develop a model that allows us to investigate the importance of these factors in reducing the effective reproduction number. We show that the reduction in onward transmission during quarantine and isolation has a bigger effect than tracing coverage on the reproduction number. We also show that intuitively reasonable contact tracing performance indicators, such as the proportion of contacts quarantined before symptom onset, are often not well correlated with the reproduction number. We conclude that provision of support systems to enable people to quarantine and isolate effectively is crucial to the success of contact tracing.

New Zealand had 1499 cases of COVID-19 before eliminating transmission of the virus. Extensive contract tracing during the outbreak has resulted in a dataset of epidemiologically linked cases. This data contains useful information about the transmission dynamics of the virus, its dependence on factors such as age, and its response to different control measures. We use Monte-Carlo network construction techniques to provide an estimate of the number of secondary cases for every individual infected during the outbreak. We then apply standard statistical techniques to quantify differences between groups of individuals. Children under 10 years old are significantly under-represented in the case data. Children infected fewer people on average and had a lower probability of transmitting the disease in comparison to adults and the elderly. Imported cases infected fewer people on average and also had a lower probability of transmitting than domestically acquired cases. Superspreading is a significant contributor to the epidemic dynamics, with 20% of cases among adults responsible for 65–85% of transmission. Subclinical cases infected fewer individuals than clinical cases. After controlling for outliers serial intervals were approximated with a normal distribution (μ = 4.4 days, σ = 4.7 days). Border controls and strong social distancing measures, particularly when targeted at superspreading, play a significant role in reducing the spread of COVID-19.

Aims We aim to quantify differences in clinical outcomes from COVID-19 infection by ethnicity with a focus on risk of hospitalisation. Methods We used data on age, ethnicity, deprivation index, pre-existing health conditions, and clinical outcomes on 1,829 COVID-19 cases reported in New Zealand. We used a logistic regression model to calculate odds ratios for the risk of hospitalisation by ethnicity. We also consider length of hospital stay and risk of fatality. Results Māori have 2.5 times greater odds of hospitalisation than non-Māori, non-Pacific people, after controlling for age and pre-existing conditions. Similarly, Pacific people have 3 times greater odds. Conclusions Structural inequities and systemic racism in the healthcare system mean that Māori and Pacific communities face a much greater health burden from COVID-19. Older people and those with pre-existing health conditions are also at greater risk. This should inform future policy decisions including prioritising groups for vaccination.

New Zealand responded to the COVID-19 pandemic with a combination of border restrictions and an Alert Level system that included strict stay-at-home orders. These interventions were successful in containing the outbreak and ultimately eliminating community transmission of COVID-19. The timing of interventions is crucial to their success. Delaying interventions for too long may both reduce their effectiveness and mean that they need to be maintained for a longer period of time. Here, we use a stochastic branching process model of COVID-19 transmission and control to simulate the epidemic trajectory in New Zealand and the effect of its interventions during its COVID-19 outbreak in March-April 2020. We use the model to calculate key outcomes, including the peak load on the contact tracing system, the total number of reported COVID-19 cases and deaths, and the probability of elimination within a specified time frame. We investigate the sensitivity of these outcomes to variations in the timing of the interventions. We find that a delay to the introduction of Alert Level 4 controls results in considerably worse outcomes. Changes in the timing of border measures have a smaller effect. We conclude that the rapid response in introducing stay-at-home orders was crucial in reducing the number of cases and deaths and increasing the probability of elimination. Executive Summary Evaluating the effectiveness of New Zealand’s COVID-19 response, relative to hypothetical response scenarios, is important for guiding future response strategies. We assess the importance of early implementation of interventions for controlling COVID-19. We model counterfactual (alternative ‘what-if’) scenarios in which the timings of three policy interventions are varied: border restrictions requiring 14-day quarantine of all international arrivals, border closure except to returning residents and citizens, and Alert Level 4 restrictions. Key measures describing the dynamics of a COVID-19 outbreak (notably peak load on the contact tracing system, the total number of reported COVID-19 cases and deaths, and the probability of elimination within a specified time frame), are used to compare outcomes from each scenario with New Zealand’s actual outcomes. Key measures were more sensitive to the timing of Alert Level 4, than to timing of border restrictions and border closure. Of the three counterfactual scenarios, an earlier start to Alert Level 4 would have resulted in the greatest reduction in numbers of cases and deaths. Delaying the start of Alert Level 4 by 20 days could have led to over 11,500 cases and 200 deaths, and would have substantially reduced the probability of eliminating community transmission of COVID-19, requring a longer period at Alert Level 4 to achieve control.

Background: Digital tools are being developed to support contact tracing as part of the global effort to control the spread of COVID-19. These include smartphone apps, Bluetooth-based proximity detection, location tracking, and automatic exposure notification features. Evidence on the effectiveness of alternative approaches to digital contact tracing is so far limited. Methods: We use an age-structured branching process model of the transmission of COVID-19 in different settings to estimate the potential of manual contact tracing and digital tracing systems to help control the epidemic. We investigate the effect of the uptake rate and proportion of contacts recorded by the digital system on key model outputs: the effective reproduction number, the mean outbreak size after 30 days, and the probability of elimination. Results: Effective manual contact tracing can reduce the effective reproduction number from 2.4 to around 1.5. The addition of a digital tracing system with a high uptake rate over 75% could further reduce the effective reproduction number to around 1.1. Fully automated digital tracing without manual contact tracing is predicted to be much less effective. Conclusions: For digital tracing systems to make a significant contribution to the control of COVID-19, they need be designed in close conjunction with public health agencies to support and complement manual contact tracing by trained professionals.

On 25 ^th March 2020, New Zealand implemented stringent lockdown measures (Alert Level 4, in a four-level alert system) with the goal of eliminating community transmission of COVID-19. Once new cases are no longer detected over consecutive days, the probability of elimination is an important measure for informing decisions on when certain COVID-19 restrictions should be relaxed. Our model of COVID-19 spread in New Zealand estimates that after 2-3 weeks of no new reported cases, there is a 95% probability that COVID-19 has been eliminated. We assessed the sensitivity of this estimate to varying model parameters, in particular to different likelihoods of detection of clinical cases and different levels of control effectiveness. Under an optimistic scenario with high detection of clinical cases, a 95% probability of elimination is achieved after 10 consecutive days with no new reported cases, while under a more pessimistic scenario with low case detection it is achieved after 22 days.