ABSTRACT
New Zealand (NZ)’s elimination of community transmission of influenza and respiratory syncytial virus (RSV) infections in May 2020, due to stringent COVID-19 countermeasures, provided a rare opportunity to assess the impact of border restrictions and relaxations on common respiratory viral infections over the subsequent two-years. Using multiple surveillance systems, we observed that border closure to most non-residents, and mandatory government-managed isolation and quarantine on arrival for those allowed to enter, appeared to be effective in keeping influenza and RSV infections out of the NZ community. Partial border relaxations through quarantine free travel with Australia and other countries were associated, within weeks, with importation of RSV and influenza into NZ in 2021 and 2022. Border restrictions did not have effect on community transmission of other respiratory viruses such as rhinovirus and parainfluenza virus type 1. These data can inform future pandemic influenza preparedness as well as provide insights into effective strategies to plan and model the impact of seasonal influenza, RSV, and other respiratory viral infections.
Subject(s)
COVID-19 , Respiratory Syncytial Virus Infections , Respiratory Tract InfectionsABSTRACT
Genomic technologies have become routine in the surveillance and monitoring of the Coronavirus Disease 2019 (COVID-19) pandemic, as evidenced by the millions of SARS-CoV-2 sequences uploaded to the Global Initiative on Sharing Avian Influenza Data (GISAID). Yet the ways in which these technologies have been applied to manage the pandemic are varied. Aotearoa New Zealand was one of a small number of countries to adopt an elimination strategy for COVID-19, establishing a hotel-based managed isolation and quarantine system for all international arrivals. To aid our response, we rapidly set up and scaled our use of genomic technologies to help identify community cases of COVID-19, to understand how they had arisen, and to determine the appropriate action to maintain elimination. Once New Zealand pivoted from elimination to suppression as a strategy in late 2021, our genomic response changed to focusing on identifying new variants arriving at the border, tracking their incidence around the country, and examining any links between specific variants and increased disease severity. Here we explore New Zealand's genomic journey through the pandemic and discuss the lessons learned for how we should continue to respond to COVID-19 and how we should prepare for future pandemics.
Subject(s)
COVID-19ABSTRACT
Recently there has been a surge in emergent SARS-CoV-2 lineages that are able to evade both vaccine induced immunity as well as prior infection from the founding Omicron BA.1 and BA.2 lineages. These highly transmissible and evasive lineages are on the rise and include Omicron variants BA.2.12.1, BA.4, and BA.5. Aotearoa New Zealand recently reopened its borders to many travellers, without their need to enter quarantine. By generating 10,403 complete SARS-CoV-2 genomes classified as Omicron, we show that New Zealand is observing an influx of these immune-evasive variants through the border. Specifically, there has been a recent surge of BA.5 and BA.2.12.1 introductions into the community and these can be explained by the gradual return to pre-pandemic levels of international traveller arrival rates. We estimate there is one Omicron transmission event from the border to the community for every ~5,000 passenger arrivals into the country, or around one introduction event per day at the current levels of travel. Given the waning levels of population immunity, this rate of importation presents the risk of a large wave in New Zealand during the second half of 2022. Genomic surveillance, coupled with modelling the rate at which new variants cross the border into the community, provides a lens on the rate at which new variants might gain a foothold and trigger new waves of infection.
ABSTRACT
New Zealand's COVID-19 elimination strategy heavily relied on the use of genomics to inform contact tracing, linking cases to the border and to clusters during community outbreaks. In August 2021, New Zealand entered its second nationwide lockdown after the detection of a single community case with no immediately apparent epidemiological link to the border. This incursion resulted in the largest outbreak seen in New Zealand caused by the Delta Variant of Concern. Here we generated 3806 high quality SARS-CoV-2 genomes from cases reported in New Zealand between 17 August and 1 December 2021, representing 43% of reported cases. We detected wide geographical spread coupled with undetected community transmission, characterised by the apparent extinction and reappearance of genomically linked clusters. We also identified the emergence, and near replacement, of genomes possessing a 10-nucleotide frameshift deletion that caused the likely truncation of accessory protein ORF7a. By early October, New Zealand moved from elimination to suppression and the role of genomics changed markedly from being used to track and trace, towards population-level surveillance.
Subject(s)
COVID-19ABSTRACT
There have been thirteen known COVID-19 community outbreaks in Aotearoa New Zealand since the virus was first eliminated in May 2020, two of which led to stay-at-home orders being issued by health officials. These outbreaks originated at the border; via isolating returnees, airline workers, and cargo vessels. With a public health system informed by real-time viral genomic sequencing which typically had complete genomes within 12 hours after a community-based positive COVID-19 test, every outbreak was well-contained with a total of 225 community cases, resulting in three deaths. Real-time genomics were essential for establishing links between cases when epidemiological data could not, and for identifying when concurrent outbreaks had different origins. By reconstructing the viral transmission history from genomic sequences, here we recount all thirteen community outbreaks and demonstrate how genomics played a vital role in containing them.
Subject(s)
COVID-19ABSTRACT
Background. Real-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. Methods. We generated the genomes of 80% of the laboratory-confirmed samples of SARS-CoV-2 from New Zealand's August 2020 outbreak and compared these genomes to the available global genomic data. Findings. Genomic 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. Interpretation. Access to a broader and more heterogenous sample of global genomic data would strengthen efforts to locate the source of any new outbreaks.
Subject(s)
COVID-19ABSTRACT
BackgroundNew Zealand, Australia, Iceland, and Taiwan all saw success at controlling the first wave of the COVID-19 pandemic. As islands, they make excellent case studies for exploring the effects of international travel and human movement on the spread of COVID-19. MethodsWe employed a range of robust phylodynamic methods and genome subsampling strategies to infer the epidemiological history of SARS-CoV-2 in these four countries. We compared these results to transmission clusters identified by the New Zealand Ministry of Health by contract tracing strategies. FindingsWe estimated the effective reproduction number of COVID-19 as 1-1.4 during early stages of the pandemic, and show that it declined below 1 as human movement was restricted. We also showed that this disease was introduced many times into each country, and that introductions slowed down markedly following the reduction of international travel in mid March 2020. Finally, we confirmed that New Zealand transmission clusters identified via standard health surveillance strategies largely agree with those defined by genomic data. InterpretationWe have demonstrated how the use of genomic data and computational biology methods can assist health officials in characterising the epidemiology of viral epidemics, and for contact tracing. FundingThis research was funded by the Health Research Council of New Zealand, the Ministry of Business, Innovation, and Employment, the Royal Society of New Zealand, and the New Zealand Ministry of Health. Research in ContextO_ST_ABSEvidence before this studyC_ST_ABSOur study looks at the early months of the COVID-19 pandemic, a period in which the first wave was controlled in four "island" nations - New Zealand, Australia, Taiwan, and Iceland. All prior data used in this study was collected from late 2019 until the end of April 2020. This includes over 3000 SARS-CoV-2 genomic sequences which were collected in this period (and subsequently deposited into GISAID), as well as arrival and departure information (provided by official statistics from each country), human mobility data collected from mobile phones (by Apple), and COVID-19 case data (released by the World Health Organisation). Even early on during the COVID-19 pandemic, the properties of SARS-CoV-2 - including the reproduction number and mutation rate - were well characterised, and a range of these estimates have been covered in our article. Our Bayesian phylodynamic models, including their prior distributions, are informed by all of the above sources of information. Finally, we have incorporated all of the available information on COVID-19 transmission clusters identified by the New Zealand Ministry of Health during this period. Added value of this studyWe quantified the decline in the reproduction number of SARS-CoV-2, following the decline in human mobility, in four "island" countries. We also demonstrated how importation events of SARS-CoV-2 into each considered country declined markedly following the reduction of international travel. Our results shed a different light on these patterns because of (i) our locations of choice - the four countries had success in dealing with the first pandemic wave, with their geographic isolation contributing to cleaner signals of human mobility, and (ii) our novel and empirically driven phylodynamic model, which we built from explicitly modelling mobile phone data in the four islands. Furthermore, by crossing epidemiological against ge3nomic data, our paper quantitatively assesses the ability of contact tracing, as implemented by the New Zealand Ministry of Health (NZMH), in identifying COVID-19 transmission clusters. We find evidence for a high efficacy of the specific measures taken - and when they were taken - by the NZMH in identifying transmission clusters, considered worldwide to have been successful in its response to the pandemic. Our analyses also illustrate the power of viral genomic data in assisting contact tracing. Implications of all the available evidenceThe conclusions drawn from this research inform effective policy for locations pursuing an elimination strategy. We confirm the accuracy of standard contact tracing methods at identifying clusters and show how these methods are improved using genomic data. We demonstrate how the overseas introduction rates and domestic transmission rates of an infectious viral agent can be surveilled using genomic data, and the important role each plays in overall transmission. Specifically, we have quantified these processes for four countries and have shown that they did decline significantly following declines in human travel and mobility. The phylodynamic methods used in this work is shown to be robust and applicable to a range of scenarios where appropriate subsampling is used.
Subject(s)
COVID-19ABSTRACT
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.
Subject(s)
COVID-19ABSTRACT
The SARS-CoV-2 epidemic has rapidly spread outside China with major outbreaks occurring in Italy, South Korea and Iran. Phylogenetic analyses of whole genome sequencing data identified a distinct SARS-CoV-2 clade linked to travellers returning from Iran to Australia and New Zealand. This study highlights potential viral diversity driving the epidemic in Iran, and underscores the power of rapid genome sequencing and public data sharing to improve the detection and management of emerging infectious diseases.