A decade of genomic surveillance tracks the disappearance and reintroduction of seasonal influenza virus in Aotearoa New Zealand

A decade of genomic surveillance tracks the disappearance and reintroduction of seasonal influenza virus in Aotearoa New Zealand | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A decade of genomic surveillance tracks the disappearance and reintroduction of seasonal influenza virus in Aotearoa New Zealand Lauren Jelley, Jordan Douglas, Margot Allais, Jing Wang, Meaghan O'Neill, and 24 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7503135/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Seasonal influenza virus circulation was eliminated in Aotearoa (New Zealand) from 2020 to 2022, following the nation's stringent public health response to the COVID-19 pandemic. Here, we generate nearly 4,000 influenza virus genomes captured over a decade of national surveillance (2013–2022), encompassing influenza A(H3N2) and A(H1N1)pdm09 subtypes and B/Victoria and B/Yamagata lineages. We show that the reintroduction of influenza virus in 2022, was marked by an early and sharp epidemic dominated by the influenza A/H3N2 clade 3C.2a1b.2a.2a.1. Bayesian phylodynamic inference revealed a collapse in influenza virus diversity during border closures, with the diversity of influenza A(H3N2) partially recovering in 2022. Spatial diffusion modelling of the monophyletic A(H3N2) subclade in 2022 revealed strong source-sink dynamics, with the three healthcare regions in the North Island seeding transmission into the South Island. B/Yamagata lineages remained absent from our surveillance post-pandemic, supporting global evidence of its extinction. These findings highlight how prolonged interruption of viral transmission can reshape epidemic dynamics and reduce viral diversity, underscoring the importance of genomic surveillance in understanding and anticipating the evolution and re-emergence of seasonal pathogens. Biological sciences/Microbiology/Microbial genetics/Viral genetics Biological sciences/Genetics/Genomics/Genome evolution Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction In early 2020, the New Zealand government implemented some of the world’s most stringent public health measures in response to the emerging COVID-19 pandemic. These interventions included the closure of international borders to all but New Zealand citizens and permanent residents, the introduction of mandatory managed isolation and quarantine for incoming travelers, and a strict nationwide lockdown lasting four weeks 1 . The early, rapid and decisive nature of these measures not only enabled New Zealand to successfully contain the first waves of SARS-CoV-2 2–6 , but also had an unprecedented impact on the circulation of other respiratory pathogens. Notably, detections of influenza virus declined by 99.9%, respiratory syncytial virus (RSV) by 98%, human metapneumovirus by 92%, enterovirus by 82%, adenovirus by 81%, parainfluenza virus by 80%, and rhinovirus by 75% 7 . These declines highlighted the broader impact of COVID-19 mitigation strategies on respiratory virus spread and introduction dynamics. The near-elimination of circulating respiratory viruses also resulted in a population with reduced immunity to these pathogens. This became evident when borders partially reopened to Australia in April 2021 and New Zealand experienced an unusually large RSV outbreak, driven by an immunologically naïve population 8 , 9 . Indeed, during the 2021 winter that followed, RSV incidence was approximately five times higher than that typically seen 10 . Genomic surveillance revealed a vast reduction in the genetic diversity of circulating RSV lineages, indicative of a genomic bottleneck and rapid clonal expansion following its reintroduction 11 . This outbreak served as a harbinger of the epidemiological consequences of the reintroduction of previously endemic viruses into a population with reduced immunity. Seasonal influenza virus, in contrast to RSV, did not re-establish in New Zealand until 2022, following a two-year absence 9 . The delay in its circulation was largely mirrored globally, as international travel restrictions and pandemic mitigation measures also disrupted typical global influenza virus dynamics 12 . Globally, the proportion of respiratory disease specimens testing positive for influenza virus decreased from over 20% to around 2% between September 2020 and January 2021 13 . During this period, sustained influenza A virus transmission persisted in only a few geographic hotspots, primarily in Southeast Asia and West Africa 14 . Influenza B virus circulation was even more limited, with the once-abundant B/Yamagata lineage remaining undetected since early 2020 and now seemingly extinct 15 . When influenza virus eventually re-emerged in New Zealand in 2022 after the progressive relaxation of border restrictions, the seasonal epidemic was both early and brief 16 . Widespread community transmission began in April and peaked at the start of winter in early June – approximately 6–8 weeks earlier than the typical seasonal peak 10 , 17 . The two-year absence of influenza virus circulation likely contributed to reduced population-level immunity. Like most pathogens, immunity to influenza viruses wanes over time and the virus undergoes antigenic drift of surface glycoproteins. In 2022, infants and children under two years of age likely had no prior exposure and a reduced level of maternally derived immunity. At the community level, there was an increase in influenza virus in children in 2022 9 , as well as an increased incidence of severe acute respiratory infections (SARI) in Māori and Pacific peoples 16 . Indeed, the resurgence of respiratory infections such as influenza virus has important implications for health equity. Māori and Pacific peoples in New Zealand are disproportionately affected by respiratory infections due to inequities in the social determinants of health, experiencing higher transmission rates of respiratory disease and severe outcomes, as demonstrated by past pandemic spread 18 – 20 . The increased transmission and severity of respiratory viruses following their absence and reintroduction is likely to exacerbate these disparities, further highlighting the need for targeted and responsive public health interventions. To better understand the epidemiology and evolution of seasonal influenza viruses before and after the COVID-19 pandemic, we leveraged the resources of the New Zealand based Southern Hemisphere Influenza and Vaccine Effectiveness Research and Surveillance (SHIVERS) programme, as well as other surveillance platforms across primary healthcare facilities, diagnostic laboratories and hospitals. These long-standing surveillance initiatives have collected and stored clinical samples that tested positive for influenza virus from individuals with influenza-like illness (ILI), acute respiratory infections (ARIs) and SARI since 2012. Each sample is accompanied by detailed metadata, including collection date, geographic location and patient demographics. Using this collection of samples, we generated 3,974 genomes of seasonal influenza viruses spanning the period from 2013 to 2022. These viruses included influenza A (subtypes H3N2 and H1N1pdm09) and B (lineages Yamagata and Victoria). This comprehensive genomic dataset enabled us to investigate the spatiotemporal dynamics of influenza virus transmission in New Zealand, providing insights into how virus evolution was shaped by the idiosyncratic disruption caused by the COVID-19 pandemic and the response to it. Methods Ethics The SHIVERS study has been approved by the New Zealand Northern Health and Disability Ethics Committee (NTX/11/11/102 and 0952). Ethics approval was not required for the additional virological analysis of pathogens collected through primary care, laboratory and hospital-based surveillance platforms as this work aligned with the primary purpose of their collection. Influenza virus sample collection Genomes used in this study were obtained from clinical samples collected between June 2013 to September 2022. During this time, the New Zealand Institute for Public Health and Forensic Science (PHF Science) had several surveillance and research platforms through which respiratory (nasopharyngeal, throat and nasal) samples were received by the National Influenza Centre of New Zealand at PHF Science. Samples were obtained via several surveillance platforms. First, through a community-based surveillance platform, enrolled general practices across New Zealand obtained respiratory samples from patients with ILI. Second, a hospital-based surveillance platform enrolled Auckland-based hospitals to collect samples from patients with SARI. Third, a passive surveillance platform, in which diagnostic laboratories across New Zealand referred positive influenza virus samples to PHF Science. Fourth, the SHIVERS research studies (II – IV), which obtained respiratory samples from participants in the Wellington-based community cohorts with ILI or ARI. Finally, the SHIVERS research study (V), which comprised eight participating general practices across Auckland, Wellington and Dunedin collated respiratory samples from participants with ARIs. In 2021, this study focused on returning travelers during the COVID-19 pandemic who entered managed isolation and quarantine while the international borders were closed. Viral RNA extraction and qRT-PCR Clinical samples were received by PHF Science during 2013–2022. RNA was extracted using either the Thermo Fisher Scientific™ MagMax Viral/Pathogen Nucleic Acid Isolation Kit (A48310) as per manufacturer's instructions on either the ZiXpress or the Kingfisher Flex automated extraction instruments, or manually extracted using the ZR Viral RNA extraction kit (ZR1035). Real-time reverse transcription PCR (rRT-PCR) was performed using either singleplex primers and probes synthesised by biosearch based on sequences published by the US Centers for Disease Control (CDC) and the AgPath-ID™ One step RT-PCR kit (4387424), or using the CDC Influenza/SARS-CoV-2 multiplex PCR kit (Flu SC2PPB-EUA) with Quantbio Ultraplex 1 step Toughmix (95166-05K). Samples with a cycle threshold (Ct.) below 30 were identified, re-extracted if necessary and referred to the PHF Science genomic sequencing department. Generating viral genomes from influenza virus positive samples All genomic segments of influenza A and B were amplified by PCR before sequencing following published protocols 21 , 22 . The PCR products for both influenza A and B segments were purified using AMPure XT beads (A63881) and quantified using the Quant iT Picogreen dsDNA assay kit (P11496). Amplicon libraries were prepared using the PlexWell 384 library prep kit (SEQPW384) and the 500/550 Midoutput kit (20024905) as per manufacturer's instructions. Sequencing was performed on the Illumina NextSeq platform. Consensus based assembly was performed using a standardised pipeline based on the Seattle Flu assembly pipeline, modified by PHF Science ( https://github.com/seattleflu/assembly ). A set of reference genomes were used for consensus calling), covering the diversity of each for the four different seasonal viruses: subtypes A(H3N2) [EPI_ISL_19182659] and A(H1N1)pdm09 [EPI_ISL_19182659], and lineages B/Victoria [EPI_ISL_8640755] and B/Yamagata [B/Wisconsin/1/2010]. The total number of sequencing reads and number of reads mapped to a flu reference genome were used to inspect the quality of consensus genomes, and those containing fewer than 10% Ns were retained. From this, 3,974 genomes were successfully generated and used in computational analysis. All genomic data generated here are available on NCBI’s GenBank database under BioProject PRJNA1304607. NextStrain ( https://clades.nextstrain.org/ ) was used to identify clade information for each genome based on the hemagglutinin (HA) segment. Inferring genomic diversity of influenza virus using phylodynamic analysis HA segments for each of the four main seasonal influenza viruses sampled in New Zealand between 2013 and 2022 were analysed in the context of HA sequences sampled from around the world. Global HA sequences were sourced from GISAID 23 , using ~ 1,000 non-New Zealand HA sequences for each subtype or lineage, sampled uniformly at random without replacement between the same time period (Supplementary Table 1). HA sequences were aligned using MAFFT (v7) 24 using the FFT-NS-2 algorithm. A time-calibrated maximum likelihood phylogenetic tree was estimated using IQ-TREE (v1.6.8) 25 using the HKY + Γ 26 nucleotide substitution model, identified as the best fitting model by ModelFinder 27 . The relative genetic diversity of each segment for each of the four influenza viruses was inferred using the Bayesian Integrated Coalescent Epoch PlotS (BICEPS) model (v1.1.2), with group count set to 20 28 , available in BEAST 2 (v2.7.6) 29 . An alignment was generated by MAFFT (v7) 24 for each segment and for each influenza virus subtype or lineage. Due to the number of genomes generated for A(H3N2), the genomes were subsampled, sampling ~ 100 genomes for each year between 2013 and 2022. We used a GTR substitution model with four gamma rate heterogeneity categories. The strict molecular clock rate was sampled from a lognormal prior with a mean of 0.003 substitutions per site per year for influenza B virus 30 , and 0.005 for influenza A virus 31 – 33 , in real-space and a standard deviation of 1 for each. We sampled the posterior distribution using Markov chain Monte Carlo (MCMC), running each analysis for 200 million steps, sampling states every 20,000 steps, and discarding the first 10% as burn-in. Convergence was assessed in Tracer (v1.7) 34 ensuring that all analyses had over 200 effective samples for all reported parameters. Analyses that had an ESS of less than 200 were repeated and log files combined using logcombiner 35 . Inferring national-level dispersal of A(H3N2) during its reintroduction We inferred the nation-level spread of A(H3N2) in 2022, using genetic data from the large monophyletic clade 3C.2a1b.2a.2a.1 following its reintroduction. As this clade consisted of 1,322 genomes, three alignments were generated from a random selection of 500 genomes (selected uniformly at random without replacement) using MAFFT (v7) 24 . The four health regions of New Zealand – three in the North Island (Northern, Te Manawa Taki and Central) and one in the South Island (Te Waipounamu), denoted four demes in the model, which uses sequence data as well as location data to approximate the migration rate (migrations/lineage/year) of influenza A(H3N2) clade 3C.2a1b.2a.2a.1 between the four health regions in New Zealand. Each alignment was subject to Bayesian phylodynamic analysis using BEAST 2 (v2.7.6) 29 with the Marginal Approximation of the Structured Coalescent (MASCOT) model (v3.0.7) 36 . We used bModelTest (v1.3.3) 37 to ensure the most appropriate site model(s). On this smaller clade, the strict molecular clock was also sampled from a lognormal prior with a mean of 0.005 31–33 in real space while the standard deviation was more constrained at 0.25, to avoid non-identifiability with the MASCOT coalescent and migration parameters. The (constant) effective population sizes of the four demes all had lognormal priors of 0, and a standard deviation of 1, both in log space. The posterior distribution was again sampled using MCMC as above. Each of these analyses were compared to ensure the robustness of the subsampling and MASCOT method. Convergence was assessed in Tracer (v1.7) 34 , ensuring that all analyses had over 200 effective samples for the posterior analysis. Analyses that had an ESS of less than 200 were repeated and log files combined using logcombiner 35 . The median values of the backwards migration rates for one of these analyses was plotted using the circlize package in R 38 . Phylogenetic trees were summarised using CCD-0 method 39 and plotted using the ape package in R 40 . Results and Discussion Between 2013 and 2022, we generated 3,974 influenza viral genomes comprising A(H3N2) (n = 2,614) and A(H1N1)pdm09 (n = 672) subtypes, and B/Victoria (n = 346) and B/Yamagata (n = 342) lineages, representing over 10% of detected cases over this timeframe (Fig. 1 ). New Zealand experiences the typical temperate climate epidemiology of influenza virus, with peak incidence occurring during mid-winter (late July to early August). However, following a two-year absence due to COVID-19 restrictions between 2020 and 2022, influenza virus was reintroduced in April 2022 following the opening of international borders (Fig. 1 ). This reintroduction led to an earlier and sharper peak compared to pre-pandemic years, peaking in early June with over 1,400 reported cases per week compared to an average peak of ~ 400 cases per week between 2013 and 2019 10 . The vast majority of genomes generated in this study were sampled from the Northern health region (67%), which included Auckland, New Zealand’s most populous city with the country’s largest international airport. Among the genomes with a known source, 79% were sampled from the community and 21% from hospitalised patients, with the four major hospitals involved in national surveillance also located in Auckland (Fig. 1 ). Between 2013 and 2019, virus genomes were largely obtained from samples collected via community surveillance, meaning that most patients likely experienced a non-severe infection (Supplementary Fig. 1). While the number of genomes generated from hospitalised cases dramatically increased for both A(H3N2) and A(H1N1)pdm09 subtypes in the 2022 reintroduction, it should be noted hospitals were likely oversampled in 2022 due to a change in diagnostic viral testing regimes during the COVID-19 pandemic where wider testing in hospitals as part of admission for respiratory viruses as well as SARS-CoV-2 was implemented 11 . The majority of genomes from 2022 were obtained from an unreported collection source precluding statistical comparison (Supplementary Fig. 1). Viral genomes were sampled from 43% male and 48% female patients, with the remainder from unreported sexes (Fig. 1 ). Across the ten years of sampling, we found that genomes were generated from very different age distributions among the four subtypes and lineages (where p < 10 − 6 in multiple two-sample Kolmogorov-Smirnov tests; Supplementary Fig. 2). The 2022 reintroduction of seasonal influenza virus into New Zealand was dominated by influenza A virus subtype A(H3N2) (92% of the genomes in 2022) and, to a lesser extent, A(H1N1)pdm09, while both influenza B viral lineages remained absent from our dataset (Fig. 2 ). The 2022 outbreak was driven by very few introductions leading to a marked reduction in genetic diversity compared to pre-pandemic years (Fig. 2 ). For example, our maximum likelihood phylogenetic analysis of HA segments estimated only six introductions of A(H3N2) in 2022, with five of these resulting in only small clusters or singletons (Fig. 2 ). One of these introductions, however, led to a large monophyletic clade, denoted 3C.2a1b.2a.2a.1, comprising 1,322 genomes (99% of the A(H3N2) genomes in 2022). This clade emerged globally in 2021, circulating across Eurasia and the Unites Sstates 41 , 42 . The 2022 dominance of this clade was also mirrored in Australia following the easing of COVID-19 restrictions and was attributed to a new introduction from an international source 43 . In 2022, 1,334 genomes were generated for subtype A(H3N2), falling across clades 3C.2a1b.2a.2a.1 (99%) as well as 3C.2a1b.2a.2a, 3C.2a1b.2a.2a.3, 3C.2a1b.2a.2a.3a.1 and 3C.2a1b.2a.2b (Fig. 2 ). Unlike previous years, all four health regions were well represented in these data, likely reflecting increased testing for respiratory illness following the COVID-19 pandemic (Supplementary Fig. 3). We also generated 30 genomes for subtype A(H1N1)pdm09 in 2022, all of which belonged to clade 6B.1A.5a.2a and originated from several (n = 8) introductions (Fig. 2 ). This clade emerged in late 2019, being detected in Asia in early 2020 and persisting globally during the pandemic to become dominant in 2022 44,45 . None of the A(H3N2) or A(H1N1)pdm09 clades identified in samples in 2022 had previously been detected in New Zealand, suggesting that these genomes were indeed new introductions rather than cryptically circulating lineages which remained undetected for two years. The dominance of the large monophyletic A(H3N2) clade 3C.2a1b.2a.2a.1 in 2022 provides a ‘natural experiment’ for the introduction and spread of a novel viral lineage to New Zealand. We took advantage of this clade to examine its migration between the four health regions across New Zealand. We found clear source-sink dynamics, where infections were largely sourced from North Island health regions (Northern, Te Manawa Taki and Central), with the South Island (Te Waipounamu) being a sink for viral transmission (Fig. 3 ). Transmission from Northern, Te Manawa Taki and Central regions had a mean migration rate of 1.29, 3.19, and 2.1 migrations per lineage per year (m/l/y), respectively, into Te Waipounamu (see Supplementary Table 3 for all means and 95% credible intervals). The mean migration rate of infections from Te Waipounamu back to the three North Island regions was much lower at 0.34, 0.45 and 0.42 m/l/y, respectively. The highest rates of migration were among North Island regions, with genomes from both the Central and Northern regions migrating to Te Manawa Taki at a mean migration rate of 3.09 and 3.76 m/l/y, respectively. Prior to the COVID-19 pandemic, multiple influenza virus lineages, including several different clades, typically cocirculated. In New Zealand, A(H3N2) was commonly the most dominant subtype, reflecting global dynamics 46 , with up to seven distinct subclades detected concurrently in 2017 (Fig. 2 ). We used Bayesian inference to estimate the genetic diversity, and thus the effective population size, of each genomic segment for each subtype and lineage over the ten years of sampling. We found that A(H3N2), A(H1N1)pdm09 and B/Victoria displayed typical seasonal dynamics, with B/Yamagata being less predictable (Fig. 4 ). Notably, we found that all genomic segments largely followed the same seasonal peaks. As expected, we found a complete reduction of influenza viral genomic diversity for all subtypes and lineages between 2020 and 2022, with only A(H3N2) recovering to pre-pandemic diversity for some segments in 2022 (Fig. 4 ). The 30 genomes generated for A(H1N1) in 2022, all denoted subclade 6B.1A.5a.2a, comprised very little genetic diversity (Fig. 4 ). Although surveillance platforms detected seven cases of B/Victoria in 2022, no genomes were successfully generated from these cases. Prior to the COVID-19 pandemic, in 2019, B/Victoria viruses accounted for a significant proportion (37%) of influenza genomes generated in New Zealand, with the majority belonging to clade V1A.3 (Fig. 2 ). This clade, characterised by a three-amino acid deletion at positions 162–164 in the HA protein, was the dominant global B/Victoria lineage during 2019 and 2020 46 . Notably, V1A.3 was not included in the 2019 influenza vaccine 47 , which may have contributed to its increased circulation and increased effective population size observed in New Zealand (Fig. 4 ). Since the onset of the COVID-19 pandemic, B/Yamagata viruses have not been detected in New Zealand, consistent with global trends 15 , 48 – 50 and supporting possible extinction of this lineage (Fig. 4 ). Overall, the extraordinary interruption of influenza virus circulation in New Zealand reshaped the trajectory of seasonal epidemics. After two silent winters, the 2022 resurgence was abrupt, early and overwhelmingly driven by a single A(H3N2) clade. These patterns underscore how fragile viral diversity can be when transmission is disrupted, and how quickly epidemics can reignite in immunologically naïve populations. Some caution is warranted, as the 2022 data were skewed toward hospital settings and international sources of introduction could not be fully resolved due to gaps in global sequencing efforts. Even so, the findings provide a rare window into how global events reshape local viral evolution, reinforcing the importance of continued genomic surveillance to anticipate the next waves of seasonal pathogens. Data availability Genomic data generated in this study are available on NCBI’s GenBank database (BioProject: PRJNA1304607). Declarations Data availability Genomic data generated in this study are available on NCBI’s GenBank database (BioProject: PRJNA1304607). Acknowledgements We gratefully acknowledge all data contributors, i.e. the Authors and their originating laboratories, responsible for obtaining the specimens, and their submitting laboratories for generating the genetic sequence and metadata and sharing via the GISAID Initiative, on which this research is based. We would like to thank the participants of the SHIVERS WellKiwi cohorts for their continued enrollment and support in our research. Second, we would like to thank the WellKiwi Clinical and Study teams for SHIVERS research, the organisation and collection of participant samples and metadata used in this study. We would also like to thank the clinical virology and genomic sequencing teams at PHF Science for processing, testing and sequencing all of the samples collected. We would like to acknowledge staff from all of the diagnostic laboratories and hospital laboratories in New Zealand that take the time and effort to collect, aliquot and refer samples to PHF Science. We would also like to acknowledge the New Zealand Ministry of Health which funds PHF Science to conduct respiratory surveillance activities which contributed to this study. Thank you to former PHF Science staff who contributed to this study. Funding LJ is funded by a University of Otago Doctoral Scholarship. JLG is funded by a New Zealand Royal Society Rutherford Discovery Fellowship (RDF-20-UOO-007). This study was supported by a New Zealand Health Research Council Grant (22/138) awarded to JLG, JdL, SH, LJ, AS, NF and DW. Our grateful thanks to FluLab for funding the SHIVERS-V team to undertake research encompassed by the project called “Influenza in a post-COVID world” which has allowed researchers in Aotearoa New Zealand to collect crucial data on influenza virus and produce local innovations and international impact with a Southern Hemisphere “population laboratory”. The SHIVERS/WellKiwis cohort is funded by the US National Institute of Allergy and Infectious Diseases (NIAID): SHIVERS-III infant funded by the US-NIAID (U01 AI 144616); SHIVERS-IV household funded by the US-NIAID (CEIRR contract: 75N93021C00016). References Jefferies S et al (2020) COVID-19 in New Zealand and the impact of the national response: a descriptive epidemiological study. Lancet Public Health 5:e612–e623 Geoghegan JL et al (2020) Genomic epidemiology reveals transmission patterns and dynamics of SARS-CoV-2 in Aotearoa New Zealand. Nat Commun 11:6351 Eichler N, Thornley C, Swadi T et al (2021) Transmission of severe acute respiratory syndrome coronavirus 2 during border quarantine and air travel, New Zealand (Aotearoa). Emerg Infect Dis 27(5):1274–1278 Fox-Lewis A, Williamson F, Harrower J et al (2022) Airborne transmission of SARS-CoV-2 delta variant within tightly monitored isolation facility, New Zealand (Aotearoa). Emerg Infect Dis 28(3):501–509 Douglas J et al (2021) Real-time genomics for tracking severe acute respiratory syndrome coronavirus 2 border incursions after virus elimination, New Zealand. Emerg Infect Dis 27:2361–2368 Geoghegan JL, Douglas J, Ren X et al (2021) Use of genomics to track coronavirus disease outbreaks, New Zealand. Emerg Infect Dis 27(5):1317–1322 Huang QS et al (2021) Impact of the COVID-19 nonpharmaceutical interventions on influenza and other respiratory viral infections in New Zealand. Nat Commun 12:1001 Grant CC, Huang QS, Trenholme A, Taylor S, Wood T (2021) What can we learn from our 2021 respiratory syncytial virus experience? NZ Med J 134:7–12 Huang QS, Wood T, Aminisani N et al (2025) Comparison of the incidence and risk factors of COVID-19 and influenza associated acute respiratory illnesses: Results of the SHIVERS-II, III, IV prospective community cohort study. J Infect Dis Huang QS et al (2024) Impact of the COVID-19 related border restrictions on influenza and other common respiratory viral infections in New Zealand. Influenza Other Respir Viruses 18:e13247 Jelley L, Douglas J, O'Neill M et al (2024) Spatial and temporal transmission dynamics of respiratory syncytial virus in New Zealand before and after the COVID-19 pandemic. Nat Commun 15(1):9758 Chen Z, Tsui JLH, Cai J et al (2025) Disruption of seasonal influenza circulation and evolution during the 2009 H1N1 and COVID-19 pandemics in Southeastern Asia. Nat Commun 16:475 Olsen SJ, Azziz-Baumgartner E, Budd AP et al (2020) Decreased influenza activity during the COVID-19 pandemic - United States, Australia, Chile, and South Africa, 2020. MMWR Morb Mortal Wkly Rep 69:1305–1309. http://dx.doi.org/10.15585/mmwr.mm6937a6 Zheng L, Lin Y, Yang J et al (2023) Global variability of influenza activity and virus subtype circulation from 2011 to 2023. BMJ Open Respir Res 10:e001638 WHO (2023) Recommended composition of influenza virus vaccines for use in the 2022–2023 northern hemisphere influenza season. in Recommendations For Influenza Vaccine Composition (World Health Organization, WHO, 2023) Cheung IMY, Paynter J, Broderick D et al (2024) Severe acute respiratory infection (SARI) due to influenza in post-COVID resurgence: disproportionate impact on older Māori and Pacific peoples. Influenza Other Respir Viruses 18(11):e70029 Huang et al (2008) Influenza surveillance and immunisation in New Zealand, 1997–2006. Influenza Other Respir Viruses 2(4):139–145 Steyn N, Binny RN, Hannah K et al Māori and Pacific people in New Zealand have a higher risk of hospitalisation for COVID-19. NZ Med J. 134(1538):28–43 Jefferies S, Gilkison C, Duff P et al (2025) Ethnic equity in Aotearoa New Zealand's COVID-19 response: A descriptive epidemiological study. Public Health 244:105732 Satherley N, Sporle A (2025) Individual, household structure, and socioeconomic predictors of COVID-19 testing and vaccination outcomes: a whole population linked data analysis. Int J Popul Data Sci 10:1 Zhou B, Donnelly ME, Scholes DT, St George K, Hatta M, Kawaoka Y, Wentworth DE (2009) Single-reaction genomic amplification accelerates sequencing and vaccine production for classical and Swine origin human influenza A viruses. J Virol 83(19):10309–10313 Zhou B, Lin X, Wang W, Halpin RA, Bera J, Stockwell TB, Barr IG, Wentworth DE (2014) Universal influenza B virus genomic amplification facilitates sequencing, diagnostics, and reverse genetics. J Clin Microbiol 52(5):1330–1337 Elbe S, Buckland-Merrett G (2017) Data, disease and diplomacy: GISAID’s innovative contribution to global health. Glob Chall 1:33–46 Katoh K, Standley DM (2013) MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol 30:772–780 Nguyen L-T, Schmidt HA, Von Haeseler A, Minh BQ (2015) IQ-TREE: a fast and effective stochastic algorithm for estimating maximum likelihood phylogenies. Mol Biol Evol 32:268–274 Hasegawa M, Kishino H, Yano T (1985) Dating of the human-ape splitting by a molecular clock of mitochondrial DNA. J Mol Evol 22:160–174 Kalyaanamoorthy S, Minh BQ, Wong TKF, Von Haeseler A, Jermiin LS (2017) ModelFinder: fast model selection for accurate phylogenetic estimates. Nat Methods 14:587–589 Bouckaert RR (2022) An efficient coalescent epoch model for Bayesian phylogenetic inference. Syst Biol 71(6):1549–1560 Bouckaert R, Vaughan TG, Barido-Sottani et al (2019) BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis. PLoS Comput Biol, 15(4), e1006650 Yoshihara K, Minh LN, Okada T et al (2020) Evolutionary dynamics of influenza B strains detected from paediatric acute respiratory infections in central Vietnam. Infect Genet Evol 81:104264 Jones S, Nelson-Sathi S, Wang Y et al (2019) Evolutionary, genetic, structural characterization and its functional implications for the influenza A (H1N1) infection outbreak in India from 2009 to 2017. Sci Rep 9:14690 Su Y, Bahl J, Joseph U et al (2015) Phylodynamics of H1N1/2009 influenza reveals the transition from host adaptation to immune-driven selection. Nat Commun 6:7952 Dorji T, Dorji K, Gyeltshen S (2024) Evolution of Influenza A(H3N2) Viruses in Bhutan for Two Consecutive Years, 2022 and 2023. Influenza Other Respir Viruses 18(10):e70028 Rambaut A, Drummond AJ, Xie D, Baele G, Suchard MA (2018) Posterior summarization in Bayesian phylogenetics using tracer 1.7. Syst Biol 67:901–904 Drummond AJ, Rambaut A (2007) BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol Biol 7:214 Müller NF, Rasmussen D, Stadler T (2018) MASCOT: parameter and state inference under the marginal structured coalescent approximation. Bioinformatics 34:3843–3848 Bouckaert RR, Drummond AJ (2017) bModelTest: Bayesian phylogenetic site model averaging and model comparison. BMC Evol Biol 17(1):42 Gu Z, Gu L, Eils R, Schlesner M, Brors B (2014) Circlize implements and enhances circular visualization in R. Bioinformatics 30(19):2811–2812 Berling L, Klawitter J, Bouckaert R, Xie D, Gavryushkin A et al (2025) Accurate Bayesian phylogenetic point estimation using a tree distribution parameterized by clade probabilities. PLoS Comput Biol 21(2):e1012789 Paradis E, Schliep K (2019) ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35(3):526–528 Kolosova NP, Ilyicheva TN, Unguryan VV et al (2022) Re-emergence of circulation of seasonal influenza during COVID-19 pandemic in Russia and receptor specificity of new and dominant clade 3C.2a1b.2a.2 A(H3N2) viruses in 2021–2022. Pathogens 11(11):1388 Fall A, Han L, Yunker M, Gong YN et al (2023) Evolution of influenza A(H3N2) viruses in 2 consecutive seasons of genomic surveillance, 2021–2023. Open Forum Infect Dis 10(12):ofad577 Wang X, Walker G, Kim KW et al (2024) The resurgence of influenza A/H3N2 virus in Australia after the relaxation of COVID-19 restrictions during the 2022 season. J Med Virol 96:e29922 Kolosova NP, Boldyrev ND, Svyatchenko SV et al (2023) An investigation of severe influenza cases in Russia during the 2022–2023 epidemic season and an analysis of HA-D222G/N polymorphism in newly emerged and dominant clade 6B.1A.5a.2a A(H1N1)pdm09 viruses. Pathogens 13(1):1 Influenza virus characterization summary report, Europe, October 2022. Copenhagen and Stockholm: World Health Organization Regional Office for Europe and European Centre for Disease Prevention and Control Xie R, Adam DC, Edwards KM et al (2022) Genomic epidemiology of seasonal influenza circulation in China during prolonged border closure from 2020 to 2021. Virus Evol 8(2):veac062 Wang X, Kim KW, Walker G et al (2024) Genome characterization of influenza A and B viruses in New South Wales, Australia, in 2019: A retrospective study using high-throughput whole genome sequencing. Influenza Other Respir Viruses 18(1):e13252 Dhanasekaran V, Sullivan S, Edwards KM et al (2022) Human seasonal influenza under COVID-19 and the potential consequences of influenza lineage elimination. Nat Commun 13(1):1721 Lu C, Barr IG, Lambert S et al (2025) Shifts in seasonal influenza patterns in Australia during and after COVID-19: A comprehensive analysis. J Infect Public Health 18(1):102620 Chen Z, Tsui JL, Gutierrez B et al (2024) COVID-19 pandemic interventions reshaped the global dispersal of seasonal influenza viruses. Science 386(6722):eadq3003 Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryFigure1.pdf Supplementary Figure 1. The sources from where influenza virus genomes were sampled, including from community surveillance (blue), hospital cases (red), and unknown (yellow), for each subtype and lineage over the ten years of sampling. SupplementaryFigure2.pdf Supplementary Figure 2. Age distribution of people from which viral genomes were sampled, along with a statistical pairwise comparison using multiple two-sample Kolmogorov-Smirnov tests, where p < 0.000001 in all comparisons. SupplementaryFigure3.pdf Supplementary Figure 3. The number of genomes sampled from the four health regions across New Zealand between 2013 and 2022. SupplementaryTable1.xlsx Supplementary Table 1. Accession numbers for global genomes used in this project. SupplementaryTable2.xlsx Supplementary Table 2. Influenza virus clades for each genome generated in this study. SupplementaryTable3.xlsx Supplementary Table 3. Mean and 95% credible interval migration rates for the large monophyletic A/H3N2 subclade 3C.2a1b.2a.2a.1 and its migration between the four health regions across New Zealand. Data were subsampled three times. SupplementaryTable4.xlsx Supplementary Table 4. Mean and 95% credible intervals for the relative genomic diversity for each viral segment over time. Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7503135","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":509159189,"identity":"ad3fc972-cb01-484a-a6dd-9f90d347c8a0","order_by":0,"name":"Lauren Jelley","email":"","orcid":"","institution":"New Zealand Institute for Public Health and Forensic Science","correspondingAuthor":false,"prefix":"","firstName":"Lauren","middleName":"","lastName":"Jelley","suffix":""},{"id":509159190,"identity":"56b21176-eb65-4180-bbec-7e6114a6b35b","order_by":1,"name":"Jordan 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The shaded grey area represents the timing at which international borders were closed during the COVID-19 pandemic. \u003cstrong\u003e(b)\u003c/strong\u003e Number of generated genomes, based on influenza viral subtype or lineage between 2013 and 2022. \u003cstrong\u003e(c) \u003c/strong\u003eMap of New Zealand, with each of the four regional health regions coloured by the number of genomes generated from each, corresponding to an adjacent colour bar. \u003cstrong\u003e(d) \u003c/strong\u003eThe age distribution of people from which influenza virus genomes were generated over the ten years. A box plot indicates the mean, lower and upper quartiles, and the scatter points show raw data. \u003cstrong\u003e(e) \u003c/strong\u003eThe number of genomes generated from cases in the community, hospital and unknown sources. \u003cstrong\u003e(f) \u003c/strong\u003eThe number of influenza virus genomes generated from female and male people.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7503135/v1/0b7bb770164993d47c7d051f.png"},{"id":90483126,"identity":"e4e360ca-8aa6-4e4f-8b1f-0c4db75eb8f3","added_by":"auto","created_at":"2025-09-03 08:30:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":161580,"visible":true,"origin":"","legend":"\u003cp\u003eTime calibrated maximum likelihood phylogenetic trees for each subtype and lineage: A(H3N2), A(H1N1)pdm09, B/Victoria and B/Yamagata, coloured by genomic clade (see Supplementary Table 2). Coloured genomes were generated in this study and sampled from New Zealand, while those in grey were downloaded from GISAID and sampled internationally (~1,000 per tree). A pie chart corresponding to each tree shows the proportion of genomes within each clade. A dashed box illustrates the timing of when international borders were closed due to the COVID-19 pandemic.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7503135/v1/cc411dd8b0bdac83318e41e5.png"},{"id":90483124,"identity":"90181aaa-6114-47cd-8bb2-2d453499bbda","added_by":"auto","created_at":"2025-09-03 08:30:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":73941,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a) \u003c/strong\u003eMap of New Zealand coloured by the four regional health regions, indicating the approximate population size in each.\u003cstrong\u003e (b) \u003c/strong\u003ePhylogenetic summary tree of the 2022 monophyletic New Zealand A(H3N2) clade 3C.2a1b.2a.2a.1 with branches coloured by sampling location shown in (a). \u003cstrong\u003e(c)\u003c/strong\u003e Chord graph showing the national level dispersal of this clade in 2022 among the four regional health regions, estimated using MASCOT available in BEAST 2. Colours indicate the source region as shown in (a), and the width of the line is proportional to the estimated mean of the migrations per lineage per year (m/l/y). Of the 1,314 genomes that fell into this clade, 500 genomes were randomly subsampled, with the analysis repeated three times (see Supplementary Table 3).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7503135/v1/e0e97e054d16aca6928148ba.png"},{"id":90483125,"identity":"f3e7ea5e-1e9e-4b2c-a975-5d8a29f0feb4","added_by":"auto","created_at":"2025-09-03 08:30:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":72297,"visible":true,"origin":"","legend":"\u003cp\u003eEffective population size for each influenza virus subtype or lineage, estimated using\u003cstrong\u003e \u003c/strong\u003ethe\u003cstrong\u003e \u003c/strong\u003eBICEPS model implemented in BEAST2. Each plot shows only the mean effective population size for each genomic segment between 2013 and 2022. For full results, including the 95% credible intervals for the MCMC run and for each segment, see Supplementary Table 4. Population sizes are in units of years.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7503135/v1/ab041e145885f1003b684d8c.png"},{"id":90624200,"identity":"c14bac76-bbeb-4fcf-9331-d043fff48bd5","added_by":"auto","created_at":"2025-09-04 22:33:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1078861,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7503135/v1/97a35b57-6f12-416f-a9e4-c4cfe766689d.pdf"},{"id":90483501,"identity":"9d2e3a40-6029-498b-b81c-036862a158a3","added_by":"auto","created_at":"2025-09-03 08:38:09","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":172393,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 1. \u003c/strong\u003eThe sources from where influenza virus genomes were sampled, including from community surveillance (blue), hospital cases (red), and unknown (yellow), for each subtype and lineage over the ten years of sampling.\u003c/p\u003e","description":"","filename":"SupplementaryFigure1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7503135/v1/57e6740e36d93cbee0be58f6.pdf"},{"id":90483129,"identity":"6afe7a55-07b4-49ec-b9d6-2c9d57e68ba5","added_by":"auto","created_at":"2025-09-03 08:30:09","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":170057,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 2. \u003c/strong\u003eAge distribution of people from which viral genomes were sampled, along with a statistical pairwise comparison using multiple two-sample Kolmogorov-Smirnov tests, where \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.000001 in all comparisons.\u003c/p\u003e","description":"","filename":"SupplementaryFigure2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7503135/v1/97ac2cac68452ed3b7b08c05.pdf"},{"id":90483504,"identity":"59b4ca23-e07f-4df0-a4ea-4111fd03afd1","added_by":"auto","created_at":"2025-09-03 08:38:09","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":2105337,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 3. \u003c/strong\u003eThe number of genomes sampled from the four health regions across New Zealand between 2013 and 2022.\u003c/p\u003e","description":"","filename":"SupplementaryFigure3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7503135/v1/310571e71ccdc97c7fac897d.pdf"},{"id":90483127,"identity":"341caff4-0f74-4158-8d23-9238c51bb3e2","added_by":"auto","created_at":"2025-09-03 08:30:09","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":102384,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 1. \u003c/strong\u003eAccession numbers for global genomes used in this project.\u003c/p\u003e","description":"","filename":"SupplementaryTable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7503135/v1/5b360505edbba25e45d0fef6.xlsx"},{"id":90483505,"identity":"e7f78b1f-b388-470a-8c55-93b7490a1652","added_by":"auto","created_at":"2025-09-03 08:38:09","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":72041,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 2. \u003c/strong\u003eInfluenza virus clades for each genome generated in this study.\u003c/p\u003e","description":"","filename":"SupplementaryTable2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7503135/v1/e75ec981b808c947e964017f.xlsx"},{"id":90483502,"identity":"e1b4098d-6ea9-4ae2-ae3d-759c89fe78fb","added_by":"auto","created_at":"2025-09-03 08:38:09","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":10200,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 3. \u003c/strong\u003eMean and 95% credible interval migration rates for the large monophyletic A/H3N2 subclade 3C.2a1b.2a.2a.1 and its migration between the four health regions across New Zealand. Data were subsampled three times.\u003c/p\u003e","description":"","filename":"SupplementaryTable3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7503135/v1/ee70677199420f7fef7aa7d5.xlsx"},{"id":90483507,"identity":"968bd8c8-cd7f-4cfb-ab64-83c858061fbc","added_by":"auto","created_at":"2025-09-03 08:38:09","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":396385,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 4. \u003c/strong\u003eMean and 95% credible intervals for the relative genomic diversity for each viral segment over time.\u003c/p\u003e","description":"","filename":"SupplementaryTable4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7503135/v1/c8a91cdcd532a42128fe38cc.xlsx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"A decade of genomic surveillance tracks the disappearance and reintroduction of seasonal influenza virus in Aotearoa New Zealand","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn early 2020, the New Zealand government implemented some of the world\u0026rsquo;s most stringent public health measures in response to the emerging COVID-19 pandemic. These interventions included the closure of international borders to all but New Zealand citizens and permanent residents, the introduction of mandatory managed isolation and quarantine for incoming travelers, and a strict nationwide lockdown lasting four weeks\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The early, rapid and decisive nature of these measures not only enabled New Zealand to successfully contain the first waves of SARS-CoV-2\u003csup\u003e2\u0026ndash;6\u003c/sup\u003e, but also had an unprecedented impact on the circulation of other respiratory pathogens. Notably, detections of influenza virus declined by 99.9%, respiratory syncytial virus (RSV) by 98%, human metapneumovirus by 92%, enterovirus by 82%, adenovirus by 81%, parainfluenza virus by 80%, and rhinovirus by 75%\u003csup\u003e7\u003c/sup\u003e. These declines highlighted the broader impact of COVID-19 mitigation strategies on respiratory virus spread and introduction dynamics.\u003c/p\u003e\u003cp\u003eThe near-elimination of circulating respiratory viruses also resulted in a population with reduced immunity to these pathogens. This became evident when borders partially reopened to Australia in April 2021 and New Zealand experienced an unusually large RSV outbreak, driven by an immunologically na\u0026iuml;ve population\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Indeed, during the 2021 winter that followed, RSV incidence was approximately five times higher than that typically seen\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Genomic surveillance revealed a vast reduction in the genetic diversity of circulating RSV lineages, indicative of a genomic bottleneck and rapid clonal expansion following its reintroduction\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. This outbreak served as a harbinger of the epidemiological consequences of the reintroduction of previously endemic viruses into a population with reduced immunity.\u003c/p\u003e\u003cp\u003eSeasonal influenza virus, in contrast to RSV, did not re-establish in New Zealand until 2022, following a two-year absence\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. The delay in its circulation was largely mirrored globally, as international travel restrictions and pandemic mitigation measures also disrupted typical global influenza virus dynamics\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Globally, the proportion of respiratory disease specimens testing positive for influenza virus decreased from over 20% to around 2% between September 2020 and January 2021\u003csup\u003e13\u003c/sup\u003e. During this period, sustained influenza A virus transmission persisted in only a few geographic hotspots, primarily in Southeast Asia and West Africa\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Influenza B virus circulation was even more limited, with the once-abundant B/Yamagata lineage remaining undetected since early 2020 and now seemingly extinct\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWhen influenza virus eventually re-emerged in New Zealand in 2022 after the progressive relaxation of border restrictions, the seasonal epidemic was both early and brief\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Widespread community transmission began in April and peaked at the start of winter in early June \u0026ndash; approximately 6\u0026ndash;8 weeks earlier than the typical seasonal peak\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. The two-year absence of influenza virus circulation likely contributed to reduced population-level immunity. Like most pathogens, immunity to influenza viruses wanes over time and the virus undergoes antigenic drift of surface glycoproteins. In 2022, infants and children under two years of age likely had no prior exposure and a reduced level of maternally derived immunity. At the community level, there was an increase in influenza virus in children in 2022\u003csup\u003e9\u003c/sup\u003e, as well as an increased incidence of severe acute respiratory infections (SARI) in Māori and Pacific peoples\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Indeed, the resurgence of respiratory infections such as influenza virus has important implications for health equity. Māori and Pacific peoples in New Zealand are disproportionately affected by respiratory infections due to inequities in the social determinants of health, experiencing higher transmission rates of respiratory disease and severe outcomes, as demonstrated by past pandemic spread\u003csup\u003e\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. The increased transmission and severity of respiratory viruses following their absence and reintroduction is likely to exacerbate these disparities, further highlighting the need for targeted and responsive public health interventions.\u003c/p\u003e\u003cp\u003eTo better understand the epidemiology and evolution of seasonal influenza viruses before and after the COVID-19 pandemic, we leveraged the resources of the New Zealand based Southern Hemisphere Influenza and Vaccine Effectiveness Research and Surveillance (SHIVERS) programme, as well as other surveillance platforms across primary healthcare facilities, diagnostic laboratories and hospitals. These long-standing surveillance initiatives have collected and stored clinical samples that tested positive for influenza virus from individuals with influenza-like illness (ILI), acute respiratory infections (ARIs) and SARI since 2012. Each sample is accompanied by detailed metadata, including collection date, geographic location and patient demographics. Using this collection of samples, we generated 3,974 genomes of seasonal influenza viruses spanning the period from 2013 to 2022. These viruses included influenza A (subtypes H3N2 and H1N1pdm09) and B (lineages Yamagata and Victoria). This comprehensive genomic dataset enabled us to investigate the spatiotemporal dynamics of influenza virus transmission in New Zealand, providing insights into how virus evolution was shaped by the idiosyncratic disruption caused by the COVID-19 pandemic and the response to it.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eEthics\u003c/h2\u003e\u003cp\u003eThe SHIVERS study has been approved by the New Zealand Northern Health and Disability Ethics Committee (NTX/11/11/102 and 0952). Ethics approval was not required for the additional virological analysis of pathogens collected through primary care, laboratory and hospital-based surveillance platforms as this work aligned with the primary purpose of their collection.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eInfluenza virus sample collection\u003c/h3\u003e\n\u003cp\u003eGenomes used in this study were obtained from clinical samples collected between June 2013 to September 2022. During this time, the New Zealand Institute for Public Health and Forensic Science (PHF Science) had several surveillance and research platforms through which respiratory (nasopharyngeal, throat and nasal) samples were received by the National Influenza Centre of New Zealand at PHF Science. Samples were obtained via several surveillance platforms. First, through a community-based surveillance platform, enrolled general practices across New Zealand obtained respiratory samples from patients with ILI. Second, a hospital-based surveillance platform enrolled Auckland-based hospitals to collect samples from patients with SARI. Third, a passive surveillance platform, in which diagnostic laboratories across New Zealand referred positive influenza virus samples to PHF Science. Fourth, the SHIVERS research studies (II \u0026ndash; IV), which obtained respiratory samples from participants in the Wellington-based community cohorts with ILI or ARI. Finally, the SHIVERS research study (V), which comprised eight participating general practices across Auckland, Wellington and Dunedin collated respiratory samples from participants with ARIs. In 2021, this study focused on returning travelers during the COVID-19 pandemic who entered managed isolation and quarantine while the international borders were closed.\u003c/p\u003e\n\u003ch3\u003eViral RNA extraction and qRT-PCR\u003c/h3\u003e\n\u003cp\u003eClinical samples were received by PHF Science during 2013\u0026ndash;2022. RNA was extracted using either the Thermo Fisher Scientific\u0026trade; MagMax Viral/Pathogen Nucleic Acid Isolation Kit (A48310) as per manufacturer's instructions on either the ZiXpress or the Kingfisher Flex automated extraction instruments, or manually extracted using the ZR Viral RNA extraction kit (ZR1035). Real-time reverse transcription PCR (rRT-PCR) was performed using either singleplex primers and probes synthesised by biosearch based on sequences published by the US Centers for Disease Control (CDC) and the AgPath-ID\u0026trade; One step RT-PCR kit (4387424), or using the CDC Influenza/SARS-CoV-2 multiplex PCR kit (Flu SC2PPB-EUA) with Quantbio Ultraplex 1 step Toughmix (95166-05K). Samples with a cycle threshold (Ct.) below 30 were identified, re-extracted if necessary and referred to the PHF Science genomic sequencing department.\u003c/p\u003e\n\u003ch3\u003eGenerating viral genomes from influenza virus positive samples\u003c/h3\u003e\n\u003cp\u003eAll genomic segments of influenza A and B were amplified by PCR before sequencing following published protocols\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. The PCR products for both influenza A and B segments were purified using AMPure XT beads (A63881) and quantified using the Quant iT Picogreen dsDNA assay kit (P11496). Amplicon libraries were prepared using the PlexWell 384 library prep kit (SEQPW384) and the 500/550 Midoutput kit (20024905) as per manufacturer's instructions. Sequencing was performed on the Illumina NextSeq platform. Consensus based assembly was performed using a standardised pipeline based on the Seattle Flu assembly pipeline, modified by PHF Science (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/seattleflu/assembly\u003c/span\u003e\u003cspan address=\"https://github.com/seattleflu/assembly\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e A set of reference genomes were used for consensus calling), covering the diversity of each for the four different seasonal viruses: subtypes A(H3N2) [EPI_ISL_19182659] and A(H1N1)pdm09 [EPI_ISL_19182659], and lineages B/Victoria [EPI_ISL_8640755] and B/Yamagata [B/Wisconsin/1/2010]. The total number of sequencing reads and number of reads mapped to a flu reference genome were used to inspect the quality of consensus genomes, and those containing fewer than 10% Ns were retained. From this, 3,974 genomes were successfully generated and used in computational analysis. All genomic data generated here are available on NCBI\u0026rsquo;s GenBank database under BioProject PRJNA1304607. NextStrain (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://clades.nextstrain.org/\u003c/span\u003e\u003cspan address=\"https://clades.nextstrain.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e was used to identify clade information for each genome based on the hemagglutinin (HA) segment.\u003c/p\u003e\n\u003ch3\u003eInferring genomic diversity of influenza virus using phylodynamic analysis\u003c/h3\u003e\n\u003cp\u003eHA segments for each of the four main seasonal influenza viruses sampled in New Zealand between 2013 and 2022 were analysed in the context of HA sequences sampled from around the world. Global HA sequences were sourced from GISAID\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, using\u0026thinsp;~\u0026thinsp;1,000 non-New Zealand HA sequences for each subtype or lineage, sampled uniformly at random without replacement between the same time period (Supplementary Table\u0026nbsp;1). HA sequences were aligned using MAFFT (v7)\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e using the FFT-NS-2 algorithm. A time-calibrated maximum likelihood phylogenetic tree was estimated using IQ-TREE (v1.6.8)\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e using the HKY\u0026thinsp;+\u0026thinsp;Γ\u003csup\u003e26\u003c/sup\u003e nucleotide substitution model, identified as the best fitting model by ModelFinder\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe relative genetic diversity of each segment for each of the four influenza viruses was inferred using the Bayesian Integrated Coalescent Epoch PlotS (BICEPS) model (v1.1.2), with group count set to 20\u003csup\u003e28\u003c/sup\u003e, available in BEAST 2 (v2.7.6)\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. An alignment was generated by MAFFT (v7)\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e for each segment and for each influenza virus subtype or lineage. Due to the number of genomes generated for A(H3N2), the genomes were subsampled, sampling\u0026thinsp;~\u0026thinsp;100 genomes for each year between 2013 and 2022. We used a GTR substitution model with four gamma rate heterogeneity categories. The strict molecular clock rate was sampled from a lognormal prior with a mean of 0.003 substitutions per site per year for influenza B virus\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, and 0.005 for influenza A virus\u003csup\u003e\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, in real-space and a standard deviation of 1 for each. We sampled the posterior distribution using Markov chain Monte Carlo (MCMC), running each analysis for 200\u0026nbsp;million steps, sampling states every 20,000 steps, and discarding the first 10% as burn-in. Convergence was assessed in Tracer (v1.7)\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e ensuring that all analyses had over 200 effective samples for all reported parameters. Analyses that had an ESS of less than 200 were repeated and log files combined using logcombiner\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eInferring national-level dispersal of A(H3N2) during its reintroduction\u003c/h2\u003e\u003cp\u003eWe inferred the nation-level spread of A(H3N2) in 2022, using genetic data from the large monophyletic clade 3C.2a1b.2a.2a.1 following its reintroduction. As this clade consisted of 1,322 genomes, three alignments were generated from a random selection of 500 genomes (selected uniformly at random without replacement) using MAFFT (v7)\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. The four health regions of New Zealand \u0026ndash; three in the North Island (Northern, Te Manawa Taki and Central) and one in the South Island (Te Waipounamu), denoted four demes in the model, which uses sequence data as well as location data to approximate the migration rate (migrations/lineage/year) of influenza A(H3N2) clade 3C.2a1b.2a.2a.1 between the four health regions in New Zealand. Each alignment was subject to Bayesian phylodynamic analysis using BEAST 2 (v2.7.6)\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e with the Marginal Approximation of the Structured Coalescent (MASCOT) model (v3.0.7)\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. We used bModelTest (v1.3.3)\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e to ensure the most appropriate site model(s). On this smaller clade, the strict molecular clock was also sampled from a lognormal prior with a mean of 0.005\u003csup\u003e31\u0026ndash;33\u003c/sup\u003e in real space while the standard deviation was more constrained at 0.25, to avoid non-identifiability with the MASCOT coalescent and migration parameters. The (constant) effective population sizes of the four demes all had lognormal priors of 0, and a standard deviation of 1, both in log space. The posterior distribution was again sampled using MCMC as above. Each of these analyses were compared to ensure the robustness of the subsampling and MASCOT method. Convergence was assessed in Tracer (v1.7)\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, ensuring that all analyses had over 200 effective samples for the posterior analysis. Analyses that had an ESS of less than 200 were repeated and log files combined using logcombiner\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. The median values of the backwards migration rates for one of these analyses was plotted using the \u003cem\u003ecirclize\u003c/em\u003e package in R\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Phylogenetic trees were summarised using CCD-0 method\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e and plotted using the \u003cem\u003eape\u003c/em\u003e package in R\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003eBetween 2013 and 2022, we generated 3,974 influenza viral genomes comprising A(H3N2) (n\u0026thinsp;=\u0026thinsp;2,614) and A(H1N1)pdm09 (n\u0026thinsp;=\u0026thinsp;672) subtypes, and B/Victoria (n\u0026thinsp;=\u0026thinsp;346) and B/Yamagata (n\u0026thinsp;=\u0026thinsp;342) lineages, representing over 10% of detected cases over this timeframe (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). New Zealand experiences the typical temperate climate epidemiology of influenza virus, with peak incidence occurring during mid-winter (late July to early August). However, following a two-year absence due to COVID-19 restrictions between 2020 and 2022, influenza virus was reintroduced in April 2022 following the opening of international borders (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This reintroduction led to an earlier and sharper peak compared to pre-pandemic years, peaking in early June with over 1,400 reported cases per week compared to an average peak of ~\u0026thinsp;400 cases per week between 2013 and 2019\u003csup\u003e10\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe vast majority of genomes generated in this study were sampled from the Northern health region (67%), which included Auckland, New Zealand\u0026rsquo;s most populous city with the country\u0026rsquo;s largest international airport. Among the genomes with a known source, 79% were sampled from the community and 21% from hospitalised patients, with the four major hospitals involved in national surveillance also located in Auckland (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Between 2013 and 2019, virus genomes were largely obtained from samples collected via community surveillance, meaning that most patients likely experienced a non-severe infection (Supplementary Fig.\u0026nbsp;1). While the number of genomes generated from hospitalised cases dramatically increased for both A(H3N2) and A(H1N1)pdm09 subtypes in the 2022 reintroduction, it should be noted hospitals were likely oversampled in 2022 due to a change in diagnostic viral testing regimes during the COVID-19 pandemic where wider testing in hospitals as part of admission for respiratory viruses as well as SARS-CoV-2 was implemented\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. The majority of genomes from 2022 were obtained from an unreported collection source precluding statistical comparison (Supplementary Fig.\u0026nbsp;1). Viral genomes were sampled from 43% male and 48% female patients, with the remainder from unreported sexes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Across the ten years of sampling, we found that genomes were generated from very different age distributions among the four subtypes and lineages (where \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e in multiple two-sample Kolmogorov-Smirnov tests; Supplementary Fig.\u0026nbsp;2).\u003c/p\u003e\u003cp\u003eThe 2022 reintroduction of seasonal influenza virus into New Zealand was dominated by influenza A virus subtype A(H3N2) (92% of the genomes in 2022) and, to a lesser extent, A(H1N1)pdm09, while both influenza B viral lineages remained absent from our dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The 2022 outbreak was driven by very few introductions leading to a marked reduction in genetic diversity compared to pre-pandemic years (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For example, our maximum likelihood phylogenetic analysis of HA segments estimated only six introductions of A(H3N2) in 2022, with five of these resulting in only small clusters or singletons (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). One of these introductions, however, led to a large monophyletic clade, denoted 3C.2a1b.2a.2a.1, comprising 1,322 genomes (99% of the A(H3N2) genomes in 2022). This clade emerged globally in 2021, circulating across Eurasia and the Unites Sstates\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. The 2022 dominance of this clade was also mirrored in Australia following the easing of COVID-19 restrictions and was attributed to a new introduction from an international source\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn 2022, 1,334 genomes were generated for subtype A(H3N2), falling across clades 3C.2a1b.2a.2a.1 (99%) as well as 3C.2a1b.2a.2a, 3C.2a1b.2a.2a.3, 3C.2a1b.2a.2a.3a.1 and 3C.2a1b.2a.2b (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Unlike previous years, all four health regions were well represented in these data, likely reflecting increased testing for respiratory illness following the COVID-19 pandemic (Supplementary Fig.\u0026nbsp;3). We also generated 30 genomes for subtype A(H1N1)pdm09 in 2022, all of which belonged to clade 6B.1A.5a.2a and originated from several (n\u0026thinsp;=\u0026thinsp;8) introductions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This clade emerged in late 2019, being detected in Asia in early 2020 and persisting globally during the pandemic to become dominant in 2022\u003csup\u003e44,45\u003c/sup\u003e. None of the A(H3N2) or A(H1N1)pdm09 clades identified in samples in 2022 had previously been detected in New Zealand, suggesting that these genomes were indeed new introductions rather than cryptically circulating lineages which remained undetected for two years.\u003c/p\u003e\u003cp\u003eThe dominance of the large monophyletic A(H3N2) clade 3C.2a1b.2a.2a.1 in 2022 provides a \u0026lsquo;natural experiment\u0026rsquo; for the introduction and spread of a novel viral lineage to New Zealand. We took advantage of this clade to examine its migration between the four health regions across New Zealand. We found clear source-sink dynamics, where infections were largely sourced from North Island health regions (Northern, Te Manawa Taki and Central), with the South Island (Te Waipounamu) being a sink for viral transmission (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Transmission from Northern, Te Manawa Taki and Central regions had a mean migration rate of 1.29, 3.19, and 2.1 migrations per lineage per year (m/l/y), respectively, into Te Waipounamu (see Supplementary Table\u0026nbsp;3 for all means and 95% credible intervals). The mean migration rate of infections from Te Waipounamu back to the three North Island regions was much lower at 0.34, 0.45 and 0.42 m/l/y, respectively. The highest rates of migration were among North Island regions, with genomes from both the Central and Northern regions migrating to Te Manawa Taki at a mean migration rate of 3.09 and 3.76 m/l/y, respectively.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ePrior to the COVID-19 pandemic, multiple influenza virus lineages, including several different clades, typically cocirculated. In New Zealand, A(H3N2) was commonly the most dominant subtype, reflecting global dynamics\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e, with up to seven distinct subclades detected concurrently in 2017 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). We used Bayesian inference to estimate the genetic diversity, and thus the effective population size, of each genomic segment for each subtype and lineage over the ten years of sampling. We found that A(H3N2), A(H1N1)pdm09 and B/Victoria displayed typical seasonal dynamics, with B/Yamagata being less predictable (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Notably, we found that all genomic segments largely followed the same seasonal peaks. As expected, we found a complete reduction of influenza viral genomic diversity for all subtypes and lineages between 2020 and 2022, with only A(H3N2) recovering to pre-pandemic diversity for some segments in 2022 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The 30 genomes generated for A(H1N1) in 2022, all denoted subclade 6B.1A.5a.2a, comprised very little genetic diversity (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAlthough surveillance platforms detected seven cases of B/Victoria in 2022, no genomes were successfully generated from these cases. Prior to the COVID-19 pandemic, in 2019, B/Victoria viruses accounted for a significant proportion (37%) of influenza genomes generated in New Zealand, with the majority belonging to clade V1A.3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This clade, characterised by a three-amino acid deletion at positions 162\u0026ndash;164 in the HA protein, was the dominant global B/Victoria lineage during 2019 and 2020\u003csup\u003e46\u003c/sup\u003e. Notably, V1A.3 was not included in the 2019 influenza vaccine\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, which may have contributed to its increased circulation and increased effective population size observed in New Zealand (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Since the onset of the COVID-19 pandemic, B/Yamagata viruses have not been detected in New Zealand, consistent with global trends\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e and supporting possible extinction of this lineage (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOverall, the extraordinary interruption of influenza virus circulation in New Zealand reshaped the trajectory of seasonal epidemics. After two silent winters, the 2022 resurgence was abrupt, early and overwhelmingly driven by a single A(H3N2) clade. These patterns underscore how fragile viral diversity can be when transmission is disrupted, and how quickly epidemics can reignite in immunologically na\u0026iuml;ve populations. Some caution is warranted, as the 2022 data were skewed toward hospital settings and international sources of introduction could not be fully resolved due to gaps in global sequencing efforts. Even so, the findings provide a rare window into how global events reshape local viral evolution, reinforcing the importance of continued genomic surveillance to anticipate the next waves of seasonal pathogens.\u003c/p\u003e"},{"header":"Data availability","content":"\u003cp\u003eGenomic data generated in this study are available on NCBI\u0026rsquo;s GenBank database (BioProject: PRJNA1304607).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenomic data generated in this study are available on NCBI’s GenBank database (BioProject: PRJNA1304607).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge all data contributors, i.e. the Authors and their originating laboratories, responsible for obtaining the specimens, and their submitting laboratories for generating the genetic sequence and metadata and sharing via the GISAID Initiative, on which this research is based.\u003c/p\u003e\n\u003cp\u003eWe would like to thank the participants of the SHIVERS WellKiwi cohorts for their continued enrollment and support in our research. Second, we would like to thank the WellKiwi Clinical and Study teams for SHIVERS research, the organisation and collection of participant samples and metadata used in this study. We would also like to thank the clinical virology and genomic sequencing teams at PHF Science for processing, testing and sequencing all of the samples collected.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge staff from all of the diagnostic laboratories and hospital laboratories in New Zealand that take the time and effort to collect, aliquot and refer samples to PHF Science. We would also like to acknowledge the New Zealand Ministry of Health which funds PHF Science to conduct respiratory surveillance activities which contributed to this study. Thank you to former PHF Science staff who contributed to this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLJ is funded by a University of Otago Doctoral Scholarship. JLG is funded by a New Zealand Royal Society Rutherford Discovery Fellowship (RDF-20-UOO-007). This study was supported by a New Zealand Health Research Council Grant (22/138) awarded to JLG, JdL, SH, LJ, AS, NF and DW. Our grateful thanks to FluLab for funding the SHIVERS-V team to undertake research encompassed by the project called “Influenza in a post-COVID world” which has allowed researchers in Aotearoa New Zealand to collect crucial data on influenza virus and produce local innovations and international impact with a Southern Hemisphere “population laboratory”. The SHIVERS/WellKiwis cohort is funded by the US National Institute of Allergy and Infectious Diseases (NIAID): SHIVERS-III infant funded by the US-NIAID (U01 AI 144616); SHIVERS-IV household funded by the US-NIAID (CEIRR contract: 75N93021C00016).\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJefferies S et al (2020) COVID-19 in New Zealand and the impact of the national response: a descriptive epidemiological study. Lancet Public Health 5:e612\u0026ndash;e623\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGeoghegan JL et al (2020) Genomic epidemiology reveals transmission patterns and dynamics of SARS-CoV-2 in Aotearoa New Zealand. Nat Commun 11:6351\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEichler N, Thornley C, Swadi T et al (2021) Transmission of severe acute respiratory syndrome coronavirus 2 during border quarantine and air travel, New Zealand (Aotearoa). Emerg Infect Dis 27(5):1274\u0026ndash;1278\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFox-Lewis A, Williamson F, Harrower J et al (2022) Airborne transmission of SARS-CoV-2 delta variant within tightly monitored isolation facility, New Zealand (Aotearoa). Emerg Infect Dis 28(3):501\u0026ndash;509\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDouglas J et al (2021) Real-time genomics for tracking severe acute respiratory syndrome coronavirus 2 border incursions after virus elimination, New Zealand. Emerg Infect Dis 27:2361\u0026ndash;2368\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGeoghegan JL, Douglas J, Ren X et al (2021) Use of genomics to track coronavirus disease outbreaks, New Zealand. Emerg Infect Dis 27(5):1317\u0026ndash;1322\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang QS et al (2021) Impact of the COVID-19 nonpharmaceutical interventions on influenza and other respiratory viral infections in New Zealand. Nat Commun 12:1001\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGrant CC, Huang QS, Trenholme A, Taylor S, Wood T (2021) What can we learn from our 2021 respiratory syncytial virus experience? NZ Med J 134:7\u0026ndash;12\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang QS, Wood T, Aminisani N et al (2025) Comparison of the incidence and risk factors of COVID-19 and influenza associated acute respiratory illnesses: Results of the SHIVERS-II, III, IV prospective community cohort study. J Infect Dis\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang QS et al (2024) Impact of the COVID-19 related border restrictions on influenza and other common respiratory viral infections in New Zealand. Influenza Other Respir Viruses 18:e13247\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJelley L, Douglas J, O'Neill M et al (2024) Spatial and temporal transmission dynamics of respiratory syncytial virus in New Zealand before and after the COVID-19 pandemic. Nat Commun 15(1):9758\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen Z, Tsui JLH, Cai J et al (2025) Disruption of seasonal influenza circulation and evolution during the 2009 H1N1 and COVID-19 pandemics in Southeastern Asia. Nat Commun 16:475\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOlsen SJ, Azziz-Baumgartner E, Budd AP et al (2020) Decreased influenza activity during the COVID-19 pandemic - United States, Australia, Chile, and South Africa, 2020. MMWR Morb Mortal Wkly Rep 69:1305\u0026ndash;1309. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.15585/mmwr.mm6937a6\u003c/span\u003e\u003cspan address=\"10.15585/mmwr.mm6937a6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZheng L, Lin Y, Yang J et al (2023) Global variability of influenza activity and virus subtype circulation from 2011 to 2023. BMJ Open Respir Res 10:e001638\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWHO (2023) Recommended composition of influenza virus vaccines for use in the 2022\u0026ndash;2023 northern hemisphere influenza season. in \u003cem\u003eRecommendations For Influenza Vaccine Composition\u003c/em\u003e (World Health Organization, WHO, 2023)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCheung IMY, Paynter J, Broderick D et al (2024) Severe acute respiratory infection (SARI) due to influenza in post-COVID resurgence: disproportionate impact on older Māori and Pacific peoples. Influenza Other Respir Viruses 18(11):e70029\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang et al (2008) Influenza surveillance and immunisation in New Zealand, 1997\u0026ndash;2006. Influenza Other Respir Viruses 2(4):139\u0026ndash;145\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSteyn N, Binny RN, Hannah K et al Māori and Pacific people in New Zealand have a higher risk of hospitalisation for COVID-19. NZ Med J. 134(1538):28\u0026ndash;43\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJefferies S, Gilkison C, Duff P et al (2025) Ethnic equity in Aotearoa New Zealand's COVID-19 response: A descriptive epidemiological study. Public Health 244:105732\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSatherley N, Sporle A (2025) Individual, household structure, and socioeconomic predictors of COVID-19 testing and vaccination outcomes: a whole population linked data analysis. Int J Popul Data Sci 10:1\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou B, Donnelly ME, Scholes DT, St George K, Hatta M, Kawaoka Y, Wentworth DE (2009) Single-reaction genomic amplification accelerates sequencing and vaccine production for classical and Swine origin human influenza A viruses. J Virol 83(19):10309\u0026ndash;10313\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou B, Lin X, Wang W, Halpin RA, Bera J, Stockwell TB, Barr IG, Wentworth DE (2014) Universal influenza B virus genomic amplification facilitates sequencing, diagnostics, and reverse genetics. J Clin Microbiol 52(5):1330\u0026ndash;1337\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eElbe S, Buckland-Merrett G (2017) Data, disease and diplomacy: GISAID\u0026rsquo;s innovative contribution to global health. Glob Chall 1:33\u0026ndash;46\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKatoh K, Standley DM (2013) MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol 30:772\u0026ndash;780\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNguyen L-T, Schmidt HA, Von Haeseler A, Minh BQ (2015) IQ-TREE: a fast and effective stochastic algorithm for estimating maximum likelihood phylogenies. Mol Biol Evol 32:268\u0026ndash;274\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHasegawa M, Kishino H, Yano T (1985) Dating of the human-ape splitting by a molecular clock of mitochondrial DNA. J Mol Evol 22:160\u0026ndash;174\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKalyaanamoorthy S, Minh BQ, Wong TKF, Von Haeseler A, Jermiin LS (2017) ModelFinder: fast model selection for accurate phylogenetic estimates. Nat Methods 14:587\u0026ndash;589\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBouckaert RR (2022) An efficient coalescent epoch model for Bayesian phylogenetic inference. Syst Biol 71(6):1549\u0026ndash;1560\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBouckaert R, Vaughan TG, Barido-Sottani et al (2019) BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis. PLoS Comput Biol, 15(4), e1006650\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYoshihara K, Minh LN, Okada T et al (2020) Evolutionary dynamics of influenza B strains detected from paediatric acute respiratory infections in central Vietnam. Infect Genet Evol 81:104264\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJones S, Nelson-Sathi S, Wang Y et al (2019) Evolutionary, genetic, structural characterization and its functional implications for the influenza A (H1N1) infection outbreak in India from 2009 to 2017. Sci Rep 9:14690\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSu Y, Bahl J, Joseph U et al (2015) Phylodynamics of H1N1/2009 influenza reveals the transition from host adaptation to immune-driven selection. Nat Commun 6:7952\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDorji T, Dorji K, Gyeltshen S (2024) Evolution of Influenza A(H3N2) Viruses in Bhutan for Two Consecutive Years, 2022 and 2023. Influenza Other Respir Viruses 18(10):e70028\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRambaut A, Drummond AJ, Xie D, Baele G, Suchard MA (2018) Posterior summarization in Bayesian phylogenetics using tracer 1.7. Syst Biol 67:901\u0026ndash;904\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDrummond AJ, Rambaut A (2007) BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol Biol 7:214\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eM\u0026uuml;ller NF, Rasmussen D, Stadler T (2018) MASCOT: parameter and state inference under the marginal structured coalescent approximation. Bioinformatics 34:3843\u0026ndash;3848\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBouckaert RR, Drummond AJ (2017) bModelTest: Bayesian phylogenetic site model averaging and model comparison. BMC Evol Biol 17(1):42\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGu Z, Gu L, Eils R, Schlesner M, Brors B (2014) Circlize implements and enhances circular visualization in R. Bioinformatics 30(19):2811\u0026ndash;2812\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBerling L, Klawitter J, Bouckaert R, Xie D, Gavryushkin A et al (2025) Accurate Bayesian phylogenetic point estimation using a tree distribution parameterized by clade probabilities. PLoS Comput Biol 21(2):e1012789\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eParadis E, Schliep K (2019) ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35(3):526\u0026ndash;528\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKolosova NP, Ilyicheva TN, Unguryan VV et al (2022) Re-emergence of circulation of seasonal influenza during COVID-19 pandemic in Russia and receptor specificity of new and dominant clade 3C.2a1b.2a.2 A(H3N2) viruses in 2021\u0026ndash;2022. Pathogens 11(11):1388\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFall A, Han L, Yunker M, Gong YN et al (2023) Evolution of influenza A(H3N2) viruses in 2 consecutive seasons of genomic surveillance, 2021\u0026ndash;2023. Open Forum Infect Dis 10(12):ofad577\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang X, Walker G, Kim KW et al (2024) The resurgence of influenza A/H3N2 virus in Australia after the relaxation of COVID-19 restrictions during the 2022 season. J Med Virol 96:e29922\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKolosova NP, Boldyrev ND, Svyatchenko SV et al (2023) An investigation of severe influenza cases in Russia during the 2022\u0026ndash;2023 epidemic season and an analysis of HA-D222G/N polymorphism in newly emerged and dominant clade 6B.1A.5a.2a A(H1N1)pdm09 viruses. Pathogens 13(1):1\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eInfluenza virus characterization summary report, Europe, October 2022. Copenhagen and Stockholm: World Health Organization Regional Office for Europe and European Centre for Disease Prevention and Control\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXie R, Adam DC, Edwards KM et al (2022) Genomic epidemiology of seasonal influenza circulation in China during prolonged border closure from 2020 to 2021. Virus Evol 8(2):veac062\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang X, Kim KW, Walker G et al (2024) Genome characterization of influenza A and B viruses in New South Wales, Australia, in 2019: A retrospective study using high-throughput whole genome sequencing. Influenza Other Respir Viruses 18(1):e13252\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDhanasekaran V, Sullivan S, Edwards KM et al (2022) Human seasonal influenza under COVID-19 and the potential consequences of influenza lineage elimination. Nat Commun 13(1):1721\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLu C, Barr IG, Lambert S et al (2025) Shifts in seasonal influenza patterns in Australia during and after COVID-19: A comprehensive analysis. J Infect Public Health 18(1):102620\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen Z, Tsui JL, Gutierrez B et al (2024) COVID-19 pandemic interventions reshaped the global dispersal of seasonal influenza viruses. Science 386(6722):eadq3003\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7503135/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7503135/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSeasonal influenza virus circulation was eliminated in Aotearoa (New Zealand) from 2020 to 2022, following the nation's stringent public health response to the COVID-19 pandemic. Here, we generate nearly 4,000 influenza virus genomes captured over a decade of national surveillance (2013\u0026ndash;2022), encompassing influenza A(H3N2) and A(H1N1)pdm09 subtypes and B/Victoria and B/Yamagata lineages. We show that the reintroduction of influenza virus in 2022, was marked by an early and sharp epidemic dominated by the influenza A/H3N2 clade 3C.2a1b.2a.2a.1. Bayesian phylodynamic inference revealed a collapse in influenza virus diversity during border closures, with the diversity of influenza A(H3N2) partially recovering in 2022. Spatial diffusion modelling of the monophyletic A(H3N2) subclade in 2022 revealed strong source-sink dynamics, with the three healthcare regions in the North Island seeding transmission into the South Island. B/Yamagata lineages remained absent from our surveillance post-pandemic, supporting global evidence of its extinction. These findings highlight how prolonged interruption of viral transmission can reshape epidemic dynamics and reduce viral diversity, underscoring the importance of genomic surveillance in understanding and anticipating the evolution and re-emergence of seasonal pathogens.\u003c/p\u003e","manuscriptTitle":"A decade of genomic surveillance tracks the disappearance and reintroduction of seasonal influenza virus in Aotearoa New Zealand","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-03 08:30:04","doi":"10.21203/rs.3.rs-7503135/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"52542551-218a-417c-9868-d9de64f049f7","owner":[],"postedDate":"September 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":54075827,"name":"Biological sciences/Microbiology/Microbial genetics/Viral genetics"},{"id":54075828,"name":"Biological sciences/Genetics/Genomics/Genome evolution"}],"tags":[],"updatedAt":"2026-04-16T22:25:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-03 08:30:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7503135","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7503135","identity":"rs-7503135","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}