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.

翻译：实时分析疫情趋势与预测，有助于支持季节性呼吸道疾病的公共卫生规划与应对。本文介绍了两款模型，它们被应用于2025年新西兰冬季态势评估计划中，针对三种呼吸道病原体：SARS-CoV-2、流感病毒和呼吸道合胞病毒（RSV）。其中，SARS-CoV-2数据来自国家COVID-19监测系统；流感与RSV的数据则仅限于哨点医院监测计划。模型自2025年5月至10月每周基于实时疾病监测数据运行，提供当前疫情趋势的定量表征，以及疫情增长率估算与28天发病例数预测。模型结果与解读以周报形式提交给公共卫生合作方，作为由澳大利亚-新西兰流行病预测与分析联盟（ACEFA）运营的跨塔斯曼冬季计划的一部分。我们将这些报告中包含的季中结果与完整季度的回顾性分析进行了比较。结论表明，实时分析表现尚可，并指出未来冬季态势评估计划中需改进的若干方向。