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Jelley L, Douglas J, O'Neill M, Berquist K, Claasen A, Wang J, Utekar S, Johnston H, Judy B, Allais M, de Ligt J, Tan CE, Seeds R, Wood T, Aminisani N, Jennings T, Welch D, Turner N, McIntyre P, Dowell T, Trenholme A, Byrnes C, Webby R, French N, Winter D, Huang QS, Geoghegan JL. Spatial and temporal transmission dynamics of respiratory syncytial virus in New Zealand before and after the COVID-19 pandemic. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.15.24310412. [PMID: 39072023 PMCID: PMC11275701 DOI: 10.1101/2024.07.15.24310412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Human respiratory syncytial virus (RSV) is a major cause of acute respiratory infection. In 2020, RSV was effectively eliminated from the community in New Zealand due to non-pharmaceutical interventions (NPI) used to control the spread of COVID-19. However, in April 2021, following a brief quarantine-free travel agreement with Australia, there was a large-scale nationwide outbreak of RSV that led to reported cases more than five times higher, and hospitalisations more than three times higher, than the typical seasonal pattern. In this study, we generated 1,471 viral genomes of both RSV-A and RSV-B sampled between 2015 and 2022 from across New Zealand. Using a phylodynamics approach, we used these data to better understand RSV transmission patterns in New Zealand prior to 2020, and how RSV became re-established in the community following the relaxation of COVID-19 restrictions. We found that in 2021, there was a large epidemic of RSV in New Zealand that affected a broader age group range compared to the usual pattern of RSV infections. This epidemic was due to an increase in RSV importations, leading to several large genomic clusters of both RSV-A ON1 and RSV-B BA9 genotypes in New Zealand. However, while a number of importations were detected, there was also a major reduction in RSV genetic diversity compared to pre-pandemic seasonal outbreaks. These genomic clusters were temporally associated with the increase of migration in 2021 due to quarantine-free travel from Australia at the time. The closest genetic relatives to the New Zealand RSV genomes, when sampled, were viral genomes sampled in Australia during a large, off-season summer outbreak several months prior, rather than cryptic lineages that were sustained but not detected in New Zealand. These data reveal the impact of NPI used during the COVID-19 pandemic on other respiratory infections and highlight the important insights that can be gained from viral genomes.
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Watson LM, Plank MJ, Armstrong BA, Chapman JR, Hewitt J, Morris H, Orsi A, Bunce M, Donnelly CA, Steyn N. Jointly estimating epidemiological dynamics of Covid-19 from case and wastewater data in Aotearoa New Zealand. COMMUNICATIONS MEDICINE 2024; 4:143. [PMID: 39009723 PMCID: PMC11250817 DOI: 10.1038/s43856-024-00570-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 07/04/2024] [Indexed: 07/17/2024] Open
Abstract
BACKGROUND Timely and informed public health responses to infectious diseases such as COVID-19 necessitate reliable information about infection dynamics. The case ascertainment rate (CAR), the proportion of infections that are reported as cases, is typically much less than one and varies with testing practices and behaviours, making reported cases unreliable as the sole source of data. The concentration of viral RNA in wastewater samples provides an alternate measure of infection prevalence that is not affected by clinical testing, healthcare-seeking behaviour or access to care. METHODS We construct a state-space model with observed data of levels of SARS-CoV-2 in wastewater and reported case incidence and estimate the hidden states of the effective reproduction number, R, and CAR using sequential Monte Carlo methods. RESULTS We analyse data from 1 January 2022 to 31 March 2023 from Aotearoa New Zealand. Our model estimates that R peaks at 2.76 (95% CrI 2.20, 3.83) around 18 February 2022 and the CAR peaks around 12 March 2022. We calculate that New Zealand's second Omicron wave in July 2022 is similar in size to the first, despite fewer reported cases. We estimate that the CAR in the BA.5 Omicron wave in July 2022 is approximately 50% lower than in the BA.1/BA.2 Omicron wave in March 2022. CONCLUSIONS Estimating R, CAR, and cumulative number of infections provides useful information for planning public health responses and understanding the state of immunity in the population. This model is a useful disease surveillance tool, improving situational awareness of infectious disease dynamics in real-time.
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Affiliation(s)
- Leighton M Watson
- School of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand.
| | - Michael J Plank
- School of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand
| | | | - Joanne R Chapman
- Institute of Environmental Science and Research Ltd, Porirua, New Zealand
| | - Joanne Hewitt
- Institute of Environmental Science and Research Ltd, Porirua, New Zealand
| | - Helen Morris
- Institute of Environmental Science and Research Ltd, Porirua, New Zealand
| | - Alvaro Orsi
- Institute of Environmental Science and Research Ltd, Porirua, New Zealand
| | - Michael Bunce
- Institute of Environmental Science and Research Ltd, Porirua, New Zealand
| | - Christl A Donnelly
- Department of Statistics, University of Oxford, Oxford, United Kingdom
- Pandemic Sciences Institute, University of Oxford, Oxford, United Kingdom
| | - Nicholas Steyn
- Department of Statistics, University of Oxford, Oxford, United Kingdom
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3
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Datta S, Vattiato G, Maclaren OJ, Hua N, Sporle A, Plank MJ. The impact of Covid-19 vaccination in Aotearoa New Zealand: A modelling study. Vaccine 2024; 42:1383-1391. [PMID: 38307744 DOI: 10.1016/j.vaccine.2024.01.101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 01/25/2024] [Accepted: 01/30/2024] [Indexed: 02/04/2024]
Abstract
Aotearoa New Zealand implemented a Covid-19 elimination strategy in 2020 and 2021, which enabled a large majority of the population to be vaccinated before being exposed to the virus. This strategy delivered one of the lowest pandemic mortality rates in the world. However, quantitative estimates of the population-level health benefits of vaccination are lacking. Here, we use a validated mathematical model of Covid-19 in New Zealand to investigate counterfactual scenarios with differing levels of vaccine coverage in different age and ethnicity groups. The model builds on earlier research by adding age- and time-dependent case ascertainment, the effect of antiviral medications, improved hospitalisation rate estimates, and the impact of relaxing control measures. The model was used for scenario analysis and policy advice for the New Zealand Government in 2022 and 2023. We compare the number of Covid-19 hospitalisations, deaths, and years of life lost in each counterfactual scenario to a baseline scenario that is fitted to epidemiological data between January 2022 and June 2023. Our results estimate that vaccines saved 6650 (95% credible interval [4424, 10180]) lives, and prevented 74500 [51000, 115400] years of life lost and 45100 [34400, 55600] hospitalisations during this 18-month period. Making the same comparison before the benefit of antiviral medications is accounted for, the estimated number of lives saved by vaccines increases to 7604 [5080, 11942]. Due to inequities in the vaccine rollout, vaccination rates among Māori were lower than in people of European ethnicity. Our results show that, if vaccination rates had been equitable, an estimated 11%-26% of the 292 Māori Covid-19 deaths that were recorded in this time period could have been prevented. We conclude that Covid-19 vaccination greatly reduced health burden in New Zealand and that equity needs to be a key focus of future vaccination programmes.
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Affiliation(s)
- Samik Datta
- Population Modelling group, National Institute of Water and Atmospheric Research, Wellington, New Zealand
| | - Giorgia Vattiato
- School of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand; Manaaki Whenua, Lincoln, New Zealand
| | - Oliver J Maclaren
- Department of Engineering Science, University of Auckland, Auckland, New Zealand
| | - Ning Hua
- Precision Driven Health, Auckland, New Zealand
| | - Andrew Sporle
- Department of Statistics, University of Auckland, Auckland, New Zealand; iNZight Analytics Ltd., Auckland, New Zealand
| | - Michael J Plank
- School of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand.
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Chen Z, Lemey P, Yu H. Approaches and challenges to inferring the geographical source of infectious disease outbreaks using genomic data. THE LANCET. MICROBE 2024; 5:e81-e92. [PMID: 38042165 DOI: 10.1016/s2666-5247(23)00296-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 09/03/2023] [Accepted: 09/13/2023] [Indexed: 12/04/2023]
Abstract
Genomic data hold increasing potential in the elucidation of transmission dynamics and geographical sources of infectious disease outbreaks. Phylogeographic methods that use epidemiological and genomic data obtained from surveillance enable us to infer the history of spatial transmission that is naturally embedded in the topology of phylogenetic trees as a record of the dispersal of infectious agents between geographical locations. In this Review, we provide an overview of phylogeographic approaches widely used for reconstructing the geographical sources of outbreaks of interest. These approaches can be classified into ancestral trait or state reconstruction and structured population models, with structured population models including popular structured coalescent and birth-death models. We also describe the major challenges associated with sequencing technologies, surveillance strategies, data sharing, and analysis frameworks that became apparent during the generation of large-scale genomic data in recent years, extending beyond inference approaches. Finally, we highlight the role of genomic data in geographical source inference and clarify how this enhances understanding and molecular investigations of outbreak sources.
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Affiliation(s)
- Zhiyuan Chen
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory of Clinical and Evolutionary Virology, KU Leuven, Leuven, Belgium
| | - Hongjie Yu
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China.
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Plank MJ, Watson L, Maclaren OJ. Near-term forecasting of Covid-19 cases and hospitalisations in Aotearoa New Zealand. PLoS Comput Biol 2024; 20:e1011752. [PMID: 38190380 PMCID: PMC10798620 DOI: 10.1371/journal.pcbi.1011752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 01/19/2024] [Accepted: 12/12/2023] [Indexed: 01/10/2024] Open
Abstract
Near-term forecasting of infectious disease incidence and consequent demand for acute healthcare services can support capacity planning and public health responses. Despite well-developed scenario modelling to support the Covid-19 response, Aotearoa New Zealand lacks advanced infectious disease forecasting capacity. We develop a model using Aotearoa New Zealand's unique Covid-19 data streams to predict reported Covid-19 cases, hospital admissions and hospital occupancy. The method combines a semi-mechanistic model for disease transmission to predict cases with Gaussian process regression models to predict the fraction of reported cases that will require hospital treatment. We evaluate forecast performance against out-of-sample data over the period from 2 October 2022 to 23 July 2023. Our results show that forecast performance is reasonably good over a 1-3 week time horizon, although generally deteriorates as the time horizon is lengthened. The model has been operationalised to provide weekly national and regional forecasts in real-time. This study is an important step towards development of more sophisticated situational awareness and infectious disease forecasting tools in Aotearoa New Zealand.
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Affiliation(s)
- Michael J. Plank
- School of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand
| | - Leighton Watson
- School of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand
| | - Oliver J. Maclaren
- Department of Engineering Science, University of Auckland, Auckland, New Zealand
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6
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McGregor R, Carlton L, Paterson A, Hills T, Charlewood R, Moreland NJ. Neutralization capacity of convalescent plasma against SARS-CoV-2 omicron sublineages: Implications for donor selection. Vox Sang 2023; 118:1145-1147. [PMID: 37817295 DOI: 10.1111/vox.13539] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 09/07/2023] [Accepted: 09/12/2023] [Indexed: 10/12/2023]
Affiliation(s)
- Reuben McGregor
- Department of Molecular Medicine and Pathology, School of Medical Sciences, The University of Auckland, Auckland, New Zealand
| | - Lauren Carlton
- Department of Molecular Medicine and Pathology, School of Medical Sciences, The University of Auckland, Auckland, New Zealand
| | - Aimee Paterson
- Department of Molecular Medicine and Pathology, School of Medical Sciences, The University of Auckland, Auckland, New Zealand
| | - Thomas Hills
- Te Toka Tumai Auckland, Te Whatu Ora (Health New Zealand), Auckland, New Zealand
- Medical Research Institute of New Zealand, Wellington, New Zealand
| | - Richard Charlewood
- Department of Molecular Medicine and Pathology, School of Medical Sciences, The University of Auckland, Auckland, New Zealand
- New Zealand Blood Service, Auckland, New Zealand
| | - Nicole J Moreland
- Department of Molecular Medicine and Pathology, School of Medical Sciences, The University of Auckland, Auckland, New Zealand
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Shingleton JW, Lilley CJ, Wade MJ. Evaluating the theoretical performance of aircraft wastewater monitoring as a tool for SARS-CoV-2 surveillance. PLOS GLOBAL PUBLIC HEALTH 2023; 3:e0001975. [PMID: 37347725 DOI: 10.1371/journal.pgph.0001975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 06/01/2023] [Indexed: 06/24/2023]
Abstract
Air travel plays an important role in the cross-border spread of infectious diseases. During the SARS-CoV-2 pandemic many countries introduced strict border testing protocols to monitor the incursion of the virus. However, high implementation costs and significant inconvenience to passengers have led public health authorities to consider alternative methods of disease surveillance at borders. Aircraft wastewater monitoring has been proposed as one such alternative. In this paper we assess the theoretical limits of aircraft wastewater monitoring and compare its performance to post-arrival border screening approaches. Using an infectious disease model, we simulate an unmitigated SARS-CoV-2 epidemic originating in a seed country and spreading to the United Kingdom (UK) through daily flights. We use a probabilistic approach to estimate the time of first detection in the UK in aircraft wastewater and respiratory swab screening. Across a broad range of model parameters, our analysis indicates that the median time between the first incursion and detection in wastewater would be approximately 17 days (IQR: 7-28 days), resulting in a median of 25 cumulative cases (IQR: 6-84 cases) in the UK at the point of detection. Comparisons to respiratory swab screening suggest that aircraft wastewater monitoring is as effective as random screening of 20% of passengers at the border, using a test with 95% sensitivity. For testing regimes with sensitivity of 85% or less, the required coverage to outperform wastewater monitoring increases to 30%. Analysis of other model parameters suggests that wastewater monitoring is most effective when used on long-haul flights where probability of defecation is above 30%, and when the target pathogen has high faecal shedding rates and reasonable detectability in wastewater. These results demonstrate the potential use cases of aircraft wastewater monitoring and its utility in a wider system of public health surveillance.
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Affiliation(s)
- Joseph W Shingleton
- Analytics & Data Science Directorate, UK Health Security Agency, Nobel House, Smith Square, London, United Kingdom
| | - Chris J Lilley
- Analytics & Data Science Directorate, UK Health Security Agency, Nobel House, Smith Square, London, United Kingdom
| | - Matthew J Wade
- Analytics & Data Science Directorate, UK Health Security Agency, Nobel House, Smith Square, London, United Kingdom
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8
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Bunce M, Geoghegan JL, Winter D, de Ligt J, Wiles S. Exploring the depth and breadth of the genomics toolbox during the COVID-19 pandemic: insights from Aotearoa New Zealand. BMC Med 2023; 21:213. [PMID: 37316857 DOI: 10.1186/s12916-023-02909-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 04/13/2023] [Indexed: 06/16/2023] Open
Abstract
BACKGROUND Genomic technologies have become routine in the surveillance and monitoring of the coronavirus disease 2019 (COVID-19) pandemic, as evidenced by the millions of SARS-CoV-2 sequences uploaded to international databases. Yet the ways in which these technologies have been applied to manage the pandemic are varied. MAIN TEXT Aotearoa New Zealand was one of a small number of countries to adopt an elimination strategy for COVID-19, establishing a managed isolation and quarantine system for all international arrivals. To aid our response, we rapidly set up and scaled our use of genomic technologies to help identify community cases of COVID-19, to understand how they had arisen, and to determine the appropriate action to maintain elimination. Once New Zealand pivoted from elimination to suppression in late 2021, our genomic response changed to focusing on identifying new variants arriving at the border, tracking their incidence around the country, and examining any links between specific variants and increased disease severity. Wastewater detection, quantitation and variant detection were also phased into the response. Here, we explore New Zealand's genomic journey through the pandemic and provide a high-level overview of the lessons learned and potential future capabilities to better prepare for future pandemics. CONCLUSIONS Our commentary is aimed at health professionals and decision-makers who might not be familiar with genetic technologies, how they can be used, and why this is an area with great potential to assist in disease detection and tracking now and in the future.
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Affiliation(s)
- Michael Bunce
- Institute of Environmental Science and Research, Kenepuru, Porirua, 5022, New Zealand
- Department of Conservation, Wellington, 6011, New Zealand
| | - Jemma L Geoghegan
- Institute of Environmental Science and Research, Kenepuru, Porirua, 5022, New Zealand
- Department of Microbiology and Immunology, University of Otago, Dunedin, New Zealand
| | - David Winter
- Institute of Environmental Science and Research, Kenepuru, Porirua, 5022, New Zealand
| | - Joep de Ligt
- Institute of Environmental Science and Research, Kenepuru, Porirua, 5022, New Zealand.
| | - Siouxsie Wiles
- Bioluminescent Superbugs Lab, Department of Molecular Medicine and Pathology, University of Auckland, Auckland, New Zealand.
- Te Pūnaha Matatini, Auckland, New Zealand.
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Simon DS, Yew CW, Kumar VS. Multiplexed Reverse Transcription Loop-Mediated Isothermal Amplification Coupled with a Nucleic Acid-Based Lateral Flow Dipstick as a Rapid Diagnostic Method to Detect SARS-CoV-2. Microorganisms 2023; 11:1233. [PMID: 37317207 PMCID: PMC10223058 DOI: 10.3390/microorganisms11051233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 04/30/2023] [Accepted: 05/03/2023] [Indexed: 06/16/2023] Open
Abstract
Due to the high reproduction rate of COVID-19, it is important to identify and isolate infected patients at the early stages of infection. The limitations of current diagnostic methods are speed, cost, and accuracy. Furthermore, new viral variants have emerged with higher rates of infectivity and mortality, many with mutations at various primer binding sites, which may evade detection via conventional PCR kits. Therefore, a rapid method that is sensitive, specific, and cost-effective is needed for a point-of-care molecular test. Accordingly, we developed a rapid molecular SARS-CoV-2 detection kit with high specificity and sensitivity, RT-PCR, taking advantage of the loop-mediated isothermal amplification (LAMP) technique. Four sets of six primers were designed based on conserved regions of the SARS-CoV-2 genome: two outer, two inner and two loop primers. Using the optimized protocol, SARS-CoV-2 genes were detected as quickly as 10 min but were most sensitive at 30 min, detecting as little as 100 copies of template DNA. We then coupled the RT-LAMP with a lateral flow dipstick (LFD) for multiplex detection. The LFD could detect two genic amplifications on a single strip, making it suitable for multiplexed detection. The development of a multiplexed RT-LAMP-LFD reaction on crude VTM samples would be suitable for the point-of-care diagnosis of COVID-19 in diagnostic laboratories as well as in private homes.
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Affiliation(s)
| | | | - Vijay Subbiah Kumar
- Biotechnology Research Institute, Universiti Malaysia Sabah, Kota Kinabalu 88400, Malaysia; (D.S.S.); (C.-W.Y.)
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Lustig A, Vattiato G, Maclaren O, Watson LM, Datta S, Plank MJ. Modelling the impact of the Omicron BA.5 subvariant in New Zealand. J R Soc Interface 2023; 20:20220698. [PMID: 36722072 PMCID: PMC9890098 DOI: 10.1098/rsif.2022.0698] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 01/06/2023] [Indexed: 02/02/2023] Open
Abstract
New Zealand experienced a wave of the Omicron variant of SARS-CoV-2 in early 2022, which occurred against a backdrop of high two-dose vaccination rates, ongoing roll-out of boosters and paediatric doses, and negligible levels of prior infection. New Omicron subvariants have subsequently emerged with a significant growth advantage over the previously dominant BA.2. We investigated a mathematical model that included waning of vaccine-derived and infection-derived immunity, as well as the impact of the BA.5 subvariant which began spreading in New Zealand in May 2022. The model was used to provide scenarios to the New Zealand Government with differing levels of BA.5 growth advantage, helping to inform policy response and healthcare system preparedness during the winter period. In all scenarios investigated, the projected peak in new infections during the BA.5 wave was smaller than in the first Omicron wave in March 2022. However, results indicated that the peak hospital occupancy was likely to be higher than in March 2022, primarily due to a shift in the age distribution of infections to older groups. We compare model results with subsequent epidemiological data and show that the model provided a good projection of cases, hospitalizations and deaths during the BA.5 wave.
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Affiliation(s)
| | - Giorgia Vattiato
- Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand
- Department of Physics, The University of Auckland, Auckland, New Zealand
| | - Oliver Maclaren
- Department of Engineering Science, University of Auckland, Auckland, New Zealand
| | - Leighton M. Watson
- School of Earth and Environment, University of Canterbury, Christchurch, New Zealand
| | - Samik Datta
- National Institute of Water and Atmospheric Research, Wellington, New Zealand
| | - Michael J. Plank
- Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand
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