[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2340":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"view_count":31,"doi":32,"paper":33,"created_at":72},2340,"A real-time forecasting framework for emerging infectious diseases affecting animal populations","https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pcbi.1014716","Infectious disease forecasting has become increasingly important in public health. However, forecasting tools for emergency animal diseases, particularly those offering real-time decision support when parameters governing disease dynamics are unknown, remain limited. We introduce a generalised modelling framework for near-real-time forecasting of the temporal and spatial spread of infectious livestock diseases using data from the early stages of an outbreak. We applied the framework to the 2007 equine influenza outbreak in Australia, generating forecasts at three timepoints across four regional clusters. Prediction targets included future daily case counts, outbreak size, peak timing and duration, and spatial distributions of future spread. We evaluated how well the forecasts predicted daily cases and the spatial distribution of case counts, using skill scores (a measure of probabilistic forecast accuracy) as a benchmark for future model improvements. Forecast accuracy, certainty, and skill improved after formation of the outbreak's peak, while early forecasts were more uncertain or prone to overestimation, highlighting the need for caution when interpreting pre-peak predictions, particularly when the impacts of control policies on future transmission are not adequately represented in the model. Spatial forecasts of broad, relative risk patterns were more robust than precise predictions of risk at the individual premises level or exact daily cases counts, supporting geographically targeted response strategies. Overall, this framework supports real-time decision-making in livestock disease outbreaks when applied with appropriate consideration of uncertainty, and establishes a foundation for future refinements and applications to other animal diseases.","传染病预测在公共卫生领域日益重要。然而，针对紧急动物疫病的预测工具，尤其是能够在疾病动态参数未知情况下提供实时决策支持的工具，仍然十分有限。我们提出了一种广义建模框架，可利用疫情早期阶段的数据对牲畜传染病的时空传播进行近实时预测。我们将该框架应用于2007年澳大利亚马流感疫情，在四个区域集群的三个时间节点上生成了预测。预测目标包括未来每日病例数、疫情规模、峰值时间和持续时间，以及未来传播的空间分布。我们评估了预测对每日病例数和病例数空间分布的预测效果，并使用技巧评分（skill score，一种概率预测准确度的度量）作为未来模型改进的基准。在疫情峰值形成后，预测的准确性、确定性和技巧均有所提高，而早期预测则更为不确定或倾向于高估，这凸显了在解读峰值前预测时需谨慎，尤其是当控制政策对未来传播的影响未在模型中得到充分体现时。对宏观相对风险模式的空间预测比对个体场所层面风险的精确预测或精确每日病例数的预测更为稳健，这为地理靶向应对策略提供了支持。总体而言，该框架在适当考虑不确定性的前提下，可支持牲畜疫病暴发期间的实时决策，并为未来的改进及应用于其他动物疫病奠定了基础。",null,"PLoS Computational Biology","2026-09-10T00:00:00Z","论文",10,false,79,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,7,1,"核心期刊发表的动物疫病实时时空预测建模研究，方法新颖、结论可靠，对畜牧疫病精准防控与应急决策有实质参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"决策支持","空间分析","动物疫病","疫情预测","智慧畜牧",0,"10.1371\u002Fjournal.pcbi.1014716",{"doi":32,"openalex_id":34,"authors":35,"venue":10,"cited_by_count":31,"oa_url":64,"card":65,"direction":69,"ingested_from":71},"W7212183958",[36,39,42,45,47,50,52,54,57,59,61],{"name":37,"orcid":38},"Meryl Theng","https:\u002F\u002Forcid.org\u002F0000-0002-1016-1487",{"name":40,"orcid":41},"Simin Lee","https:\u002F\u002Forcid.org\u002F0000-0003-2631-9899",{"name":43,"orcid":44},"Andrew C. Breed","https:\u002F\u002Forcid.org\u002F0000-0002-3439-9510",{"name":46,"orcid":9},"Sharon Roche",{"name":48,"orcid":49},"Emily Sellens","https:\u002F\u002Forcid.org\u002F0000-0001-9360-1419",{"name":51,"orcid":9},"Catherine Fraser",{"name":53,"orcid":9},"Kelly Wood",{"name":55,"orcid":56},"Chris Jewell","https:\u002F\u002Forcid.org\u002F0000-0002-7902-2178",{"name":58,"orcid":9},"Mark A. Stevenson",{"name":60,"orcid":9},"Chris Baker",{"name":62,"orcid":63},"Simon M. Firestone","https:\u002F\u002Forcid.org\u002F0000-0002-3239-1419","https:\u002F\u002Fjournals.plos.org\u002Fploscompbiol\u002Farticle\u002Ffile?id=10.1371\u002Fjournal.pcbi.1014716&type=printable",{"tldr":66,"method":67,"finding":68,"direction":69,"opportunity":70},"提出一个利用疫情早期数据实时预测牲畜传染病时空传播的通用建模框架。","基于2007年澳大利亚马流感疫情早期数据，在四个区域三个时间点生成概率预测。","峰值后预测精度与确定性提升，早期预测易高估；空间相对风险预测比精确病例数更稳健。","农业人工智能与决策模型","可将该实时预测框架迁移至其他动物疫病，并融合物联网监测数据提升早期预警能力。","openalex","2026-09-13T23:30:46.953723Z"]