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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.01374 |
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| _version_ | 1866917305238159360 |
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| author | Plank, M. J. Young, A. R. Senior, K. L. Tobin, R. J. O'Hara-Wild, M. Callaghan, F. Shearer, F. Eales, O. |
| author_facet | Plank, M. J. Young, A. R. Senior, K. L. Tobin, R. J. O'Hara-Wild, M. Callaghan, F. Shearer, F. Eales, O. |
| contents | 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). These models were run weekly from May to October 2025 on 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_01374 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Multi-pathogen situational assessment and forecasting of respiratory disease in Aotearoa New Zealand Plank, M. J. Young, A. R. Senior, K. L. Tobin, R. J. O'Hara-Wild, M. Callaghan, F. Shearer, F. Eales, O. Applications 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). These models were run weekly from May to October 2025 on 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. |
| title | Multi-pathogen situational assessment and forecasting of respiratory disease in Aotearoa New Zealand |
| topic | Applications |
| url | https://arxiv.org/abs/2603.01374 |