Multi-pathogen situational assessment and forecasting of respiratory disease in Aotearoa New Zealand

[2603.01374] Multi-pathogen situational assessment and forecasting of respiratory disease in Aotearoa New Zealand Skip to main content Search Submit Donate Log in Statistics > Applications arXiv:2603.01374 (stat) COVID-19 e-print Important: e-prints posted on arXiv are not peer-reviewed by arXiv; they should not be relied upon without context to guide clinical practice or health-related behavior and should not be reported in news media as established information without consulting multiple experts in the field. [Submitted on 2 Mar 2026 ( v1 ), last revised 9 Jun 2026 (this version, v2)] Title: Multi-pathogen situational assessment and forecasting of respiratory disease in Aotearoa New Zealand Authors: M.J. Plank , A.R. Young , K.L. Senior , R.J. Tobin , M. O'Hara-Wild , F. Callaghan , F. Shearer , O. Eales View PDF HTML (experimental) Abstract: Real-time analysis of epidemic trends and forecasts can help support public health planning and the response to seasonal respiratory disease. Here, we present two models that were used in a 2025 New Zealand winter situational assessment programme for three respiratory pathogens: SARS-CoV-2, influenza and respiratory syncytial virus (RSV). Data on SARS-CoV-2 were obtained from the national Covid-19 surveillance system; data on influenza and RSV were limited to a sentinel hospital surveillance programme. Models were run weekly from May to October 2025 on these real-time disease surveillance data and provided a quantitative representation of the current epidemic trend, along with estimates of the epidemic growth rate and 28-day ahead forecasts of case incidence. Model results and interpretation were provided in weekly reports to public health partners as part of a trans-Tasman winter programme run by the Australia--Aotearoa Consortium for Epidemic Forecasting and Analytics (ACEFA). We compare in-season results that were included in these reports to a retrospective analysis of the complete data for the season. We conclude that real-time analyses performed reasonably well, and identify some areas for improvement in future winter situational assessment programmes. Subjects: Applications (stat.AP) Cite as: arXiv:2603.01374 [stat.AP] (or arXiv:2603.01374v2 [stat.AP] for this version) https://doi.org/10.48550/arXiv.2603.01374 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Royal Society Open Science (2026) 13: 260536 Related DOI : https://doi.org/10.1098/rsos.260536 Focus to learn more DOI(s) linking to related resources Submission history From: Michael Plank [ view email ] [v1] Mon, 2 Mar 2026 02:15:51 UTC (4,615 KB) [v2] Tue, 9 Jun 2026 02:05:57 UTC (6,383 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: stat.AP < prev | next > new | recent | 2026-03 Change to browse by: stat References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from (opens in new tab) (opens in new tab) (opens in new tab)

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