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BackgroundIn response to the COVID-19 pandemic, some countries in the Asia-Pacific Region used very intensive control measures, and one of these, New Zealand (NZ), adopted a clear "elimination strategy". We therefore aimed to compare key health and economic outcomes of NZ relative to OECD countries as of mid-June 2021. MethodsThis analysis compared health outcomes (cumulative death rates from COVID-19 and "excess death" rates) and economic measures (quarterly GDP and unemployment levels) across OECD countries. ResultsNZ had the lowest cumulative COVID-19 death rate in the OECD at 242 times lower than the 38-OECD-country average: 5{middle dot}2 vs 1256 per million population. When considering "excess deaths", NZ had the largest negative value in the OECD, equivalent to around 2000 fewer deaths than expected. When considering the average GDP change over the five quarters of 2020 to 2021-Q1, NZ was the sixth best performer (at 0{middle dot}5% vs -0{middle dot}3% for the OECD average). The increase in unemployment in NZ was also less than the OECD average (1{middle dot}1 percentage points to a peak of 5{middle dot}2%, vs 3{middle dot}3 points to 8{middle dot}6%, respectively). ConclusionsNew Zealands elimination strategy response to COVID-19 produced the best mortality protection outcomes in the OECD. In economic terms it also performed better than the OECD average in terms of adverse impacts on GDP and employment. Nevertheless, a fuller accounting of the benefits and costs needs to be done once the population is vaccinated and longer-term health and economic outcomes are considered.
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A large-scale SARS-CoV-2 serosurvey of New Zealand blood donors (n=9806) was conducted at the end of 2020. Seroprevalence, after adjusting for test sensitivity and specificity, was very low (0.1%). This finding is consistent with limited community transmission and provides robust evidence to support New Zealand s successful elimination strategy for COVID-19.
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ObjectivesSophisticated epidemic models have been created to help governments and large healthcare organisations plan the necessary resources to manage the COVID-19 pandemic. Whilst helpful, current modelling systems are not widely accessible or easily adapted to different populations and circumstances. Our objective was to develop a widely applicable, easily accessible, adaptable model for projecting new COVID-19 infections and deaths that requires minimal expertise or resources to use. The model should be adaptable to different populations and able to accommodate social and pharmaceutical interventions as well as changes in the disease. DesignA Susceptible, Infected and Removed (SIR) infectious disease model was created using widely available Microsoft Excel(C) software. The model is deterministic, generating projections based on the available data and assumptions made. It uses a process of Monitored Forecasting through Visual Matching of predicated vs observed curves to improve accuracy and facilitate adaptability. A review of the COVID-19 literature was performed in order to produce an initial set of adjustable parameters on which to base the output of the model. SettingThis model can be adapted to different regions or countries for which the requisite input data (population size and number of deaths due to the disease) are available. This model has been successfully used with data from England, Sudan and Saudi Arabia. Data from NHS England were used for producing the illustrative results presented here. The model is a generic infectious disease forecast model which may be adapted to other epidemics. InterventionGovernments, public health organisations, pharmaceutical companies and other public institutions may introduce interventions that affect disease transmission or severity. Other unknown factors such as new variants of the infective agent may do the same. The effects of changes in disease transmission are identified by the model when predicted and observed curves deviate. By aligning the curves an evaluation of the effect of the changes can be made. Outcome MeasuresThe model graphically demonstrates projections for daily deaths, cumulative deaths, case mix (asymptomatic, symptomatic and severe infections requiring admission), hospital admissions and bed occupancy (ICU, general medical and total). ResultsThe model successfully produced projections for the outcome measures using NHS England data. Users can adapt and continuously update the model correcting its projections as further local data becomes available. The Microsoft Excel platform allows the model to be used without expensive health information systems or computing infrastructure. ConclusionWe present an SIR epidemic model that projects COVID-19 disease progression, is widely accessible, adaptable to different populations and environments as the disease progresses and is likely to be of benefit for identifying changing population healthcare needs.
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ObjectivesTo identify COVID-19 outbreaks and border control failures associated with quarantine systems in Australia and New Zealand and to estimate the failure risks. Design, setting, participantsObservational epidemiological study of travellers transiting quarantine in Australia and New Zealand up to 15 June 2021. Main outcome measuresThe incidence of COVID-19 related failures arising from quarantine, and the failure risk for those transiting quarantine, estimated both per 100,000 travellers and per 1000 SARS-CoV-2 positive cases. ResultsAustralia and New Zealand had 32 COVID-19 related failures arising from quarantine systems up to 15 June 2021 (22 and 10, respectively). One resultant outbreak involved an estimated 800 deaths and quarantine failures instigated nine lockdowns. The failure risk for those transiting quarantine was estimated at 5.0 failures per 100,000 travellers and 6.1 failures (95%CI: 4.0 to 8.3) per 1000 SARS-CoV-2 positive cases. The latter risk was two-fold higher in New Zealand compared with Australia. The full vaccination of frontline border workers could likely have prevented a number of quarantine system failures. ConclusionsQuarantine system failures can be costly in terms of lives and economic impacts such as lockdowns. Ongoing improvements or alternatives to hotel-based quarantine are required.
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BackgroundExcess winter mortality (EWM) has been attributed to both seasonal cold exposure, and to infectious disease. In 2020, New Zealands border management and lockdown measures successfully eliminated community transmission of SARS-CoV-2, and also largely eliminated influenza and many other respiratory viruses. This study investigates the contribution of infections and temperature to EWM and typical extended winter (May to October) deaths in this natural experiment created by New Zealands COVID-19 pandemic response. MethodsWe used age-standardised weekly deaths to measure EWM 2011 to 2019, then used historical patterns to estimate high, medium and low scenario 2020 EWMs. We then modelled typical year and 2020 heating degree day: mortality relationships to estimate relative contributions of cold temperature and infection to typical EWEDs. ResultsEWM 2011 to 2019 averaged 14.7% (low 11.4%, high 20.9%). In contrast, 2020 EWM was estimated at 1.6%, 2.7%, or 3.8% under high, medium, and low spring-summer mortality scenarios. Between 2011 and 2019, temperature was estimated to explain 47% of extended winter deaths, and infection 27%, with the remaining 26% attributable to the interaction between infection and temperature. DiscussionThe society-wide response to COVID-19 in 2020 resulted in a major reduction of winter mortality in this high-income nation with a temperate climate. In addition to influenza, other respiratory pathogens likely also make a significant contribution to EWM. Low cost protection measures such as mask wearing (eg, in residential care facilities), discouragement of sick presenteeism, and increased influenza vaccine coverage, all have potential to reduce future winter mortality. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSExcess winter mortality (EWM) is a widely observed phenomenon, commonly attributed to physiological responses to short and long-term outdoor and indoor cold exposure (and associated increased air pollution); other seasonal physiology changes; and higher incidence of some infectious diseases. Previous estimates of EWM in New Zealand range from 10.3% to 25.6%, with influenza estimated to make up roughly a third of that excess. Internationally, deaths attributable to cold temperatures are also found outside the traditional winter period, with influenza making a large contribution to cold temperature deaths. Added value of this studyThis study finds that following a successful COVID-19 elimination strategy, which simultaneously prevented the annual winter influenza season, and likely other winter respiratory infections, New Zealand is likely in 2020 to experience less than a third of the usual winter mortality excess. Further, this study for the first time estimates the relative contributions of cold temperature and infection, and the interaction between the two, to New Zealand winter deaths. We estimate that of the 9.5% fewer deaths than in typical years recorded between 1 May and 31 October 2020, 92.5% were prevented by infection control measures; 1.4% by the 1.14{degrees}C warmer than average winter; and 6.1% by the interaction between infection and low temperature. Implications of all the available evidenceInfluenza and other infectious respiratory pathogens appear to make a much larger contribution to winter mortality than previously recognised. Low cost protection measures such as mask wearing (eg, in residential care facilities), discouragement of sick presenteeism, and increased influenza vaccine coverage, all have potential to reduce future winter deaths, and lower overall annual mortality rates.
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Stringent nonpharmaceutical interventions (NPIs) such as lockdowns and border closures are not currently recommended for pandemic influenza control. New Zealand used these NPIs to eliminate coronavirus disease 2019 during its first wave. Using multiple surveillance systems, we observed a parallel and unprecedented reduction of influenza and other respiratory viral infections in 2020. This finding supports the use of these NPIs for controlling pandemic influenza and other severe respiratory viral threats.
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BackgroundReal-time genomic sequencing has played a major role in tracking the global spread and local transmission of SARS-CoV-2, contributing greatly to disease mitigation strategies. After effectively eliminating the virus, New Zealand experienced a second outbreak of SARS-CoV-2 in August 2020. During this August outbreak, New Zealand utilised genomic sequencing in a primary role to support its track and trace efforts for the first time, leading to a second successful elimination of the virus. MethodsWe generated the genomes of 80% of the laboratory-confirmed samples of SARS-CoV-2 from New Zealands August 2020 outbreak and compared these genomes to the available global genomic data. FindingsGenomic sequencing was able to rapidly identify that the new COVID-19 cases in New Zealand belonged to a single cluster and hence resulted from a single introduction. However, successful identification of the origin of this outbreak was impeded by substantial biases and gaps in global sequencing data. InterpretationAccess to a broader and more heterogenous sample of global genomic data would strengthen efforts to locate the source of any new outbreaks. FundingThis work was funded by the Ministry of Health of New Zealand, New Zealand Ministry of Business, Innovation and Employment COVID-19 Innovation Acceleration Fund (CIAF-0470), ESR Strategic Innovation Fund and the New Zealand Health Research Council (20/1018 and 20/1041).
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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 may both reduce their effectiveness and mean that they need to be maintained for a longer period. 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 measures, 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 measures to variations in the timing of interventions and show that changing the timing of Alert Level 4 (the strictest level of restrictions) has a far greater impact than the timing of border measures. Delaying Alert Level 4 restrictions results in considerably worse outcomes and implementing border measures alone, without Alert Level 4 restrictions, is insufficient to control the outbreak. 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.
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New Zealand, a geographically remote Pacific island with easily sealable borders, implemented a nation-wide lockdown of all non-essential services to curb the spread of COVID-19. New Zealand has now effectively eliminated the virus, with low numbers of new cases limited to new arrivals in managed quarantine facilities at the border. Here, we generated 649 SARS-CoV-2 genome sequences from infected patients in New Zealand with samples collected between 26 February and 22 May 2020, representing 56% of all confirmed cases in this time period. Despite its remoteness, the viruses imported into New Zealand represented nearly all of the genomic diversity sequenced from the global virus population. The proportion of D614G variants in the virus spike protein increased over time due to an increase in their importation frequency, rather than selection within New Zealand. These data also helped to quantify the effectiveness of public health interventions. For example, the effective reproductive number, Re, of New Zealands largest cluster decreased from 7 to 0.2 within the first week of lockdown. Similarly, only 19% of virus introductions into New Zealand resulted in a transmission lineage of more than one additional case. Most of the cases that resulted in a transmission lineage originated from North America, rather than from Asia where the virus first emerged or from the nearest geographical neighbour, Australia. Genomic data also helped link more infections to a major transmission cluster than through epidemiological data alone, providing probable sources of infections for cases in which the source was unclear. Overall, these results demonstrate the utility of genomic pathogen surveillance to inform public health and disease mitigation.
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AimsWe aimed to estimate the risk of COVID-19 outbreaks associated with air travel from a country with a very low prevalence of COVID-19 infection (Australia) to a COVID-19-free country (New Zealand; [NZ]), along with the likely impact of various control measures for passengers and cabin crew. MethodsA stochastic version of the SEIR model CovidSIM v1.1, designed specifically for COVID-19 was utilized. It was populated with data for both countries and parameters for SARS-CoV-2 transmission and control measures. We assumed one Australia to NZ flight per day. ResultsWhen no interventions were in place, an outbreak of COVID-19 in NZ was estimated to occur after an average time of 1.7 years (95% uncertainty interval [UI]: 0.04-6.09). However, the combined use of exit and entry screening (symptom questionnaire and thermal camera), masks on aircraft and two PCR tests (on days 3 and 12 in NZ), combined with self-reporting of symptoms and contact tracing and mask use until the second PCR test, reduced this risk to one outbreak every 29.8 years (0.8 to 110). If no PCR testing was performed, but mask use was used by passengers up to day 15 in NZ, the risk was one outbreak every 14.1 years. However, 14 days quarantine (NZ practice in May 2020), was the most effective strategy at one outbreak every 34.1 years (0.86 to 126); albeit combined with exit screening and mask use on flights. ConclusionsPolicy-makers can require multi-layered interventions to markedly reduce the risk of importing the pandemic virus into a COVID-19-free nation via air travel. There is potential to replace 14-day quarantine with PCR testing or interventions involving mask use by passengers in NZ. However, all approaches require continuous careful management and evaluation.
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AimsWe aimed to determine the length of time from the last detected case of SARS-CoV-2 infection before elimination can be assumed at a country level in an island nation. MethodsA stochastic version of the SEIR model Covid SIM v1.1 designed specifically for COVID-19 was utilised. It was populated with data for the case study island nation of New Zealand (NZ) along with relevant parameters sourced from the NZ and international literature. This included a testing level for symptomatic cases of 7,800 tests per million people per week. ResultsIt was estimated to take between 27 and 33 days of no new detected cases for there to be a 95% probability of epidemic extinction. This was for effective reproduction numbers (Re) in the range of 0.50 to 1.0, which encompass such controls as case isolation (the shorter durations relate to low Re values). For a 99% probability of epidemic extinction, the equivalent time period was 37 to 44 days. In scenarios with lower levels of symptomatic cases seeking medical attention and lower levels of testing, the time period was up to 53 to 91 days (95% level). ConclusionsIn the context of a high level of testing, a period of around one month of no new notified cases of COVID-19 would give 95% certainty that elimination of SARS-CoV-2 transmission had been achieved.
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AimsWe aimed to determine the effectiveness of surveillance using testing for SARS-CoV-2 to identify an outbreak arising from a single case of border control failure at a country level. MethodsA stochastic version of the SEIR model CovidSIM v1.1 designed specifically for COVID-19 was utilised. It was seeded with New Zealand (NZ) population data and relevant parameters sourced from the NZ and international literature. ResultsFor what we regard as the most plausible scenario with an effective reproduction number of 2.0, the results suggest that 95% of outbreaks from a single imported case would be detected in the period up to day 33 after introduction. At the time point of detection, there would be a median number of 6 infected cases in the community (95%UI: 1-68). To achieve this level of detection, an on-going programme of 7,800 tests per million people per week for the NZ population would be required. The vast majority of this testing (96%) would be of symptomatic cases in primary care settings and the rest in hospitals. Despite the large number of tests required, there are plausible strategies to enhance testing yield and cost-effectiveness eg, (i) adjusting the eligibility criteria via symptom profiles; (ii) and pooling of test samples. ConclusionsThis model-based analysis suggests that a surveillance system with a very high level of routine testing is probably required to detect an emerging or re-emerging SARS-CoV-2 outbreak within one month of a border control failure in a nation.
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A SEIR simulation model for the COVID-19 pandemic was developed (http://covidsim.eu) and applied to a hypothetical European country of 10 million population. Our results show which interventions potentially push the epidemic peak into the subsequent year (when vaccinations may be available) or which fail. Different levels of control (via contact reduction) resulted in 22% to 63% of the population sick, 0.2% to 0.6% hospitalised, and 0.07% to 0.28% dead (n=6,450 to 28,228).