{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,28]],"date-time":"2025-08-28T03:10:10Z","timestamp":1756350610348,"version":"3.44.0"},"reference-count":48,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2025,7,29]],"date-time":"2025-07-29T00:00:00Z","timestamp":1753747200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Influenza Resp Viruses"],"published-print":{"date-parts":[[2025,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Public health surveillance systems need to monitor influenza activity and guide measures to mitigate its high impact on morbidity, mortality and healthcare systems. There is an increasing expectation that surveillance data will support the modeling of future short\u2010term disease scenarios using artificial intelligence (AI) and machine learning (ML). This study examines how influenza surveillance can support AI\/ML\u2010based short\u2010term forecasting for influenza at the community and hospital levels in a high\u2010income country setting (Aotearoa\/New Zealand).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This study used a two\u2010phase approach. The first phase involved a comprehensive review of government reports, official websites, and literature to characterize existing influenza surveillance systems. The second phase evaluated systems against eight key attributes\u2014timeliness, sensitivity, specificity, representativeness, coverage, robustness, completeness, and historical data\u2014using a five\u2010level ranking system. Attribute selection was informed by experts' knowledge, ML requirements, and established frameworks. Weighted scores for training and short\u2010term forecasting capabilities were calculated to determine alignment with AI\/ML requirements.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The Southern Hemisphere Influenza and Vaccine Effectiveness Research and Surveillance (SHIVERS) community cohort and Severe Acute Respiratory Infection (SARI) hospital surveillance emerged as the most useful systems, achieving the highest scores in both training and short\u2010term forecasting in community and hospital settings, respectively. The National Minimum Dataset of hospitalizations and mortality datasets demonstrated strong training potential but are limited in short\u2010term forecasting due to timeliness constraints. Additionally, laboratory\u2010based surveillance performs a useful role in bridging community and hospital datasets.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>A set of key attributes is useful for assessing which influenza surveillance systems are best aligned with AI\/ML training and short\u2010term forecasting requirements. These attributes distinguished systems that are likely to be the most suitable for modeling future short\u2010term disease scenarios for influenza at the community and hospital levels in New Zealand. Integrating these data sources could enhance influenza forecasts to improve public health responses and intervention planning.<\/jats:p><\/jats:sec>","DOI":"10.1111\/irv.70144","type":"journal-article","created":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T02:04:33Z","timestamp":1753841073000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Framework for Evaluating the Use of Surveillance Systems for Short\u2010Term Influenza Forecasting"],"prefix":"10.1111","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-0714-6159","authenticated-orcid":false,"given":"Negin","family":"Maroufi","sequence":"first","affiliation":[{"name":"University of Otago  Wellington New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lucy\u00a0Telfar","family":"Barnard","sequence":"additional","affiliation":[{"name":"University of Otago  Wellington New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3923-5195","authenticated-orcid":false,"given":"Qiu\u00a0Sue","family":"Huang","sequence":"additional","affiliation":[{"name":"University of Otago  Wellington New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7245-0367","authenticated-orcid":false,"given":"Gillian","family":"Dobbie","sequence":"additional","affiliation":[{"name":"University of Otago  Wellington New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nayyereh","family":"Aminisani","sequence":"additional","affiliation":[{"name":"Institute of Environmental Science and Research  Wellington New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9765-4136","authenticated-orcid":false,"given":"Steffen","family":"Albrecht","sequence":"additional","affiliation":[{"name":"University of Auckland  Auckland New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nhung","family":"Nghiem","sequence":"additional","affiliation":[{"name":"University of Otago  Wellington New Zealand"},{"name":"Australian National University  Canberra Australian Capital Territory Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael\u00a0G.","family":"Baker","sequence":"additional","affiliation":[{"name":"University of Otago  Wellington New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,7,29]]},"reference":[{"volume-title":"Influenza (Seasonal)","key":"e_1_2_11_2_1"},{"volume-title":"Influenza Surveillance","key":"e_1_2_11_3_1"},{"issue":"13","key":"e_1_2_11_4_1","first-page":"1","article-title":"Updated Guidelines for Evaluating Public Health Surveillance Systems: Recommendations From the Guidelines Working Group. 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