[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3545":3,"related-3545":53},{"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":18,"tags":20,"search_phrases":25,"slug":28,"view_count":15,"doi":29,"paper":30,"created_at":52},3545,"AI-POWERED PERSONAL FINANANCIAL HEALTH AND PREDICTIVE DECISION SUPPORT DASHBOARD","https:\u002F\u002Fdoi.org\u002F10.62643\u002Fijerst.2026.v22.n3.4661","The AI-Powered Personal Financial Health and Predictive Decision Support Dashboard is an intelligent financial-management system designed to help individuals understand their financial position and make better-informed personal financial decisions. Managing income, expenses, savings, budgets, investments, and financial goals can become difficult when information is distributed across different accounts and records. The proposed dashboard brings important financial information together in a single interface. The system uses Artificial Intelligence, Machine Learning, data analytics, predictive analysis, and visualization techniques to analyze personal financial data. Users can provide information about income, expenses, savings, budgets, recurring payments, financial goals, and other relevant transactions. The system processes this information to generate a structured view of the users financial activities. A financial analytics module categorizes transactions and identifies spending patterns, income trends, recurring expenses, savings behavior, and budget utilization. The system can generate financial-health indicators based on clearly defined metrics such as spending-to-income ratio, savings rate, budget adherence, and emergency-fund progress. These indicators help users understand their current financial situation. The predictive component can analyze historical financial patterns to estimate future cash flow, upcoming expenses, and potential budget deviations. An AI-based decisionsupport layer can provide explanations and scenario analysis, such as showing how changes in monthly spending could affect savings over time. Predictions are presented as estimates based on available historical data rather than guaranteed financial outcomes. Overall, the proposed system provides a centralized dashboard for financial monitoring, trend analysis, budgeting, predictive insights, and goal tracking. It can be useful for students, employees, families, and individuals who want to organize their personal finances. Future enhancements can include secure bank-data integration, advanced anomaly detection, personalized financial education, scenario simulation, multilingual assistance, and improved forecasting models.","AI驱动的个人财务健康与预测性决策支持仪表盘是一种智能财务管理系统，旨在帮助个人了解自身财务状况并做出更为明智的个人财务决策。当信息分散在不同账户和记录中时，管理收入、支出、储蓄、预算、投资和财务目标可能变得困难。所提出的仪表盘将重要财务信息整合到一个单一界面中。该系统利用人工智能（Artificial Intelligence）、机器学习（Machine Learning）、数据分析、预测分析和可视化技术来分析个人财务数据。用户可以提供有关收入、支出、储蓄、预算、周期性付款、财务目标及其他相关交易的信息。系统处理这些信息，以生成用户财务活动的结构化视图。财务分析模块对交易进行分类，并识别支出模式、收入趋势、周期性支出、储蓄行为和预算使用情况。系统可以基于明确界定的指标生成财务健康指标，例如支出收入比、储蓄率、预算遵守情况和应急基金进展。这些指标帮助用户了解其当前财务状况。预测组件可以分析历史财务模式，以估算未来现金流、即将发生的支出和潜在预算偏差。基于AI的决策支持层可以提供解释和情景分析，例如展示月度支出变化可能如何随时间影响储蓄。预测以基于可用历史数据的估计形式呈现，而非有保证的财务结果。总体而言，所提出的系统提供了一个集中式仪表盘，用于财务监控、趋势分析、预算编制、预测性洞察和目标跟踪。它可以对学生、员工、家庭以及希望整理个人财务的个人有所帮助。未来的增强功能可以包括安全的银行数据集成、高级异常检测、个性化金融教育、情景模拟、多语言辅助以及改进的预测模型。",null,"International Journal of Engineering Research and Science & Technology","2026-09-24T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该论文聚焦个人财务健康与AI预测决策支持，与三农、农业信息化、智慧农业、数字乡村等主题无关，不建议进入每日精选。",[19],{"name":10,"url":6},[21,22,23,24],"金融科技","决策支持","预测分析","个人理财",[26,27],"个人理财 决策支持 金融科技 预测分析","个人理财 决策支持","个人理财决策支持金融科技预测分析-3545","10.62643\u002Fijerst.2026.v22.n3.4661",{"doi":29,"openalex_id":31,"authors":32,"venue":10,"cited_by_count":15,"oa_url":43,"card":44,"direction":50,"ingested_from":51},"W7214244276",[33,35,37,39,41],{"name":34,"orcid":9},"K Madhunika",{"name":36,"orcid":9},"G Laxmi",{"name":38,"orcid":9},"J Shiva Kumar",{"name":40,"orcid":9},"M Sai Ram",{"name":42,"orcid":9},"D Madhavan","https:\u002F\u002Fijerst.org\u002Findex.php\u002Fijerst\u002Farticle\u002Fdownload\u002F4661\u002F4303",{"tldr":45,"method":46,"finding":47,"direction":48,"opportunity":49},"构建AI个人财务健康仪表盘，整合收支并预测现金流与预算偏差。","用AI、机器学习、数据分析和可视化分析个人财务数据。","系统能生成财务健康指标并预测未来现金流和预算偏差。","其他","可迁移其预测与决策支持框架至农业经营主体财务健康管理场景。","智慧农业 \u002F 农业物联网","openalex","2026-09-26T23:30:12.700014Z",{"total":54,"page":55,"page_size":54,"items":56},6,1,[57,91,124,154,187,221],{"id":58,"title":59,"url":60,"summary":61,"summary_zh":9,"content":9,"source_name":62,"source_url":60,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":63,"score_detail":64,"sources":69,"tags":71,"search_phrases":75,"slug":78,"view_count":15,"doi":79,"paper":80,"created_at":90},3518,"Soil physical data integration in commercial precision agriculture platforms: barriers to compaction-relevant decision support","https:\u002F\u002Fdoi.org\u002F10.13140\u002Frg.2.2.22005.74727","Soil physical data integration in commercial precision agriculture platforms: barriers to compaction-relevant decision support。OpenAlex","OpenAlex",47,{"impact":65,"substance":54,"depth":66,"authority":66,"freshness":67,"relevant":55,"comment":68},8,12,9,"学术论文探讨商业精准农业平台土壤物理数据整合障碍，与农业信息化相关但偏学术、公共价值有限，时效新。",[70],{"name":62,"url":60},[72,73,22,74],"智慧农业","精准农业","土壤数据",[76,77],"精准农业 土壤物理数据 平台","决策支持 土壤数据 智慧农业 精准农业","精准农业土壤物理数据平台-3518","10.13140\u002Frg.2.2.22005.74727",{"doi":79,"openalex_id":81,"authors":82,"venue":9,"cited_by_count":15,"oa_url":60,"card":9,"direction":89,"ingested_from":51},"W7214201555",[83,86],{"name":84,"orcid":85},"Hanna Radziuk","https:\u002F\u002Forcid.org\u002F0000-0001-5279-2175",{"name":87,"orcid":88},"Marcin Świtoniak","https:\u002F\u002Forcid.org\u002F0000-0002-9907-7088","农业人工智能与决策模型","2026-09-25T23:30:59.544316Z",{"id":92,"title":93,"url":94,"summary":95,"summary_zh":9,"content":9,"source_name":96,"source_url":9,"published_at":97,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":98,"score_detail":99,"sources":105,"tags":107,"search_phrases":111,"slug":114,"view_count":15,"doi":9,"paper":115,"created_at":123},3247,"From Phenotyping to Supervised Agentic Decision Support: A Review of Sensing and Artificial Intelligence for Greenhouse Strawberry Cultivation（从表型到监督智能体决策支持：温室草莓栽培的传感与人工智能综述）","https:\u002F\u002Fm2.mtmt.hu\u002Fapi\u002Fpublication\u002F37426142","MDPI Horticulturae 12(7) 765 发表综述：温室草莓栽培越来越依赖传感技术、人工智能和决策支持基础设施，但其在园艺上的价值取决于异构测量能否转化为生物学上有意义的作物状态和实际管理决策。本综述综合草莓表型、多模态传感、AI作物状态解释和监督智能体协调，提出表型到行动的温室草莓栽培框架。综述研究表明，通过成像、光谱、环境、根区和建模方法在测量和解释营养、生殖、果实品质、压力和环境作物状态方面取得实质性进展。然而，大部分文献仍强调测量精度、模型性能或基础设施能力，而较少研究验证AI输出是否改善作物响应、管理决策、工作流、资源使用或生产结果。综述区分数据采集传感技术与AI解释预测方法，并研究作物状态信息如何与实际温室决策相连接。","MDPI Horticulturae","2026-09-21T00:00:00Z",77,{"impact":100,"substance":101,"depth":102,"authority":103,"freshness":67,"relevant":55,"comment":104},18,20,17,13,"系统梳理温室草莓从表型感知到智能体决策支持的研究进展，指出AI输出缺乏生产结果验证这一关键缺口，对智慧园艺研究有参考价值。",[106],{"name":96,"url":94},[72,108,22,109,110],"农业人工智能","温室草莓","多模态传感",[112,113],"温室草莓 人工智能 决策支持","草莓表型 多模态传感","温室草莓人工智能决策支持-3247",{"doi":9,"openalex_id":9,"authors":116,"venue":9,"cited_by_count":15,"oa_url":9,"card":117,"direction":50,"ingested_from":122},[],{"tldr":118,"method":119,"finding":120,"direction":50,"opportunity":121},"综述温室草莓从表型感知到监督智能体决策支持的传感与AI技术，提出表型到行动框架。","综合草莓表型、多模态传感、AI状态解释与监督智能体协调的文献综述。","感知与AI解释进展显著，但少有研究验证AI输出是否真正改善作物响应与管理决策。","可研究AI决策输出对温室草莓实际管理、资源利用与产量结果的闭环验证与效果评估。","agent","2026-09-23T00:04:33.259764Z",{"id":125,"title":126,"url":127,"summary":128,"summary_zh":9,"content":9,"source_name":129,"source_url":9,"published_at":130,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":131,"score_detail":132,"sources":136,"tags":138,"search_phrases":141,"slug":144,"view_count":15,"doi":9,"paper":145,"created_at":153},3246,"Geospatial Artificial Intelligence in Precision Agriculture: A Systematic Review（精准农业中的地理空间人工智能：系统综述）","https:\u002F\u002Fwww.mdpi.com\u002F3043-1204\u002F1\u002F1\u002F4","MDPI AI Precis. Agric. 1(1) 4 发表系统综述：综述2019-2026年Scopus、Web of Science及补充检索文献，考察精准农业中的应用、多模态数据集成与决策支持。综述表明研究范式从孤立的制图与预测转向集成工作流，整合卫星与无人机影像、田间传感器、气象、土壤与管理记录。报告效益包括产量预测改善、压力与病害更早检测、土壤制图更精细、灌溉与投入品更精准。然而，产量、环境和经济效益仍依赖当地条件和运行验证。核心发现是：地理参考数据本身不能确保真正的地理空间AI；可靠工作流必须解决空间依赖性、尺度不匹配、可迁移性和不确定性。可解释性和融入农场运营对可执行推荐同样重要。采用仍受数据互操作性、连接性、可负担性、隐私和技术能力限制。","MDPI AI in Precision Agriculture","2026-09-22T00:00:00Z",82,{"impact":100,"substance":133,"depth":100,"authority":134,"freshness":13,"relevant":55,"comment":135},22,14,"系统综述梳理2019-2026年GeoAI在精准农业的集成工作流与落地瓶颈，结论扎实、时效性强，对智慧农业技术路线有参考价值。",[137],{"name":129,"url":127},[72,108,139,22,140],"遥感","多模态数据",[142,143],"GeoAI 精准农业 系统综述","地理空间人工智能 精准农业","GeoAI精准农业系统综述-3246",{"doi":9,"openalex_id":9,"authors":146,"venue":9,"cited_by_count":15,"oa_url":9,"card":147,"direction":151,"ingested_from":122},[],{"tldr":148,"method":149,"finding":150,"direction":151,"opportunity":152},"系统综述2019-2026年地理空间AI在精准农业的应用、多模态数据集成与决策支持。","系统综述Scopus、Web of Science等文献，分析多模态数据集成与决","地理参考数据不等于地理空间AI，可靠工作流须解决空间依赖、尺度、可迁移性与不确定性。","农业遥感与作物表型","可研究跨尺度空间依赖建模与可迁移性评估，提升模型在不同农场条件下的泛化能力。","2026-09-23T00:04:33.172821Z",{"id":155,"title":156,"url":157,"summary":158,"summary_zh":159,"content":9,"source_name":160,"source_url":157,"published_at":161,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":162,"score_detail":163,"sources":165,"tags":167,"search_phrases":170,"slug":173,"view_count":55,"doi":174,"paper":175,"created_at":186},2806,"Evidence-Weighted Multi-Criteria Decision Support for Subjective Quality Assessment Under Sparse and Unbalanced Information","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fapp16189222","Decision making for complex products often depends on subjective user experience, while competing alternatives may be supported by strongly unequal numbers of observations. This study develops an evidence-weighted multi-criteria decision-making methodology for such sparse and unbalanced information. A seven-category rating scale is aggregated at the respondent level and transformed affinely to [0, 1]. Domain-specific variance components are estimated by restricted maximum likelihood and used for empirical Bayes partial pooling, so that sparse estimates are moderated without excluding valid alternatives. AHP preference weights are kept conceptually separate from evidence weights, inherent product quality is separated from non-inherent ownership attributes, and uncertainty is propagated through Monte Carlo simulation. The empirical demonstration comprised 140 unique questionnaire records for 21 agricultural tractor brands with sample sizes from 1 to 50. Compared with direct averaging, the empirical Bayes ranking was highly preserved (Spearman ρ=0.992) while unsupported extremes were reduced. A controlled simulation showed lower RMSE for empirical Bayes than for the arithmetic mean, median, and fixed shrinkage under both Gaussian and bounded non-Gaussian data generation, with the largest benefit at n=1. Sensitivity analyses showed high ranking stability to the upper-level quality weight, perturbations of AHP weights, and bounded score transformations. The framework therefore provides reproducible uncertainty-aware decision support without treating weak evidence as either absent or equally strong as data-rich evidence.","复杂产品的决策往往依赖主观用户体验，而相互竞争的备选方案可能由数量极不均衡的观测值所支持。本研究针对此类稀疏且不均衡的信息，开发了一种证据加权多准则决策方法。将七类评分量表在受访者层面进行聚合，并通过仿射变换映射至[0, 1]区间。通过限制最大似然估计领域特定的方差分量，并将其用于经验贝叶斯部分池化，从而在不排除有效备选方案的前提下对稀疏估计进行适度调整。AHP偏好权重在概念上与证据权重保持分离，固有产品质量与非固有所有权属性相区分，不确定性通过蒙特卡洛模拟进行传播。实证演示包含21个农用拖拉机品牌的140份独立问卷记录，样本量从1到50不等。与直接平均法相比，经验贝叶斯排序结果高度保持（Spearman ρ=0.992），同时缺乏支持的极端值有所减少。一项受控模拟表明，在高斯和有界非高斯数据生成条件下，经验贝叶斯的RMSE均低于算术平均、中位数和固定收缩法，且在n=1时获益最大。敏感性分析表明，排序对上层质量权重、AHP权重的扰动以及有界得分变换均具有高度稳定性。因此，该框架提供了可重复的、考虑不确定性的决策支持，而不会将弱证据视为不存在或与数据丰富的证据同等有力。","Applied Sciences","2026-09-17T00:00:00Z",68,{"impact":65,"substance":101,"depth":102,"authority":103,"freshness":13,"relevant":55,"comment":164},"方法新颖、数据扎实的农机主观质量评价决策支持论文，对农业装备质量评估有参考价值，但属方法学研究，产业影响面有限。",[166],{"name":160,"url":157},[72,168,22,169],"农机装备","质量评价",[171,172],"农机装备 决策支持 智慧农业 质量评价","农机装备 决策支持","农机装备决策支持智慧农业质量评价-2806","10.3390\u002Fapp16189222",{"doi":174,"openalex_id":176,"authors":177,"venue":160,"cited_by_count":15,"oa_url":157,"card":181,"direction":89,"ingested_from":51},"W7213447409",[178],{"name":179,"orcid":180},"K. Durczak","https:\u002F\u002Forcid.org\u002F0000-0003-4811-005X",{"tldr":182,"method":183,"finding":184,"direction":89,"opportunity":185},"提出证据加权多准则决策方法，解决稀疏不平衡主观评价下的产品排序问题。","经验贝叶斯部分池化、AHP权重分离、蒙特卡洛模拟，基于21个拖拉机品牌140份问","经验贝叶斯排序与直接平均高度一致（ρ=0.992），但能降低无支持极端值，n=1时RMSE改善最大。","可将该证据加权框架迁移到农机用户体验、智能装备评价等小样本主观决策场景，结合在线数据动态更新。","2026-09-17T23:31:04.245524Z",{"id":188,"title":189,"url":190,"summary":191,"summary_zh":192,"content":9,"source_name":193,"source_url":190,"published_at":194,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":195,"sources":197,"tags":199,"search_phrases":203,"slug":206,"view_count":15,"doi":207,"paper":208,"created_at":220},2413,"Predictive and Conceptual Models for KPI Dashboard Integration and Multi-Vendor Risk Monitoring in Government Systems","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fijcsmt.vol.12.no3.2026.pg263.294","Government institutions increasingly rely on complex digital ecosystems involving multiple technology vendors, outsourced service providers, and distributed information systems. While these environments enable scalability and operational efficiency, they also introduce significant risks related to performance monitoring, accountability, and vendor coordination. Key Performance Indicator (KPI) dashboards have emerged as essential tools for real-time monitoring and decision support; however, many government implementations remain fragmented, lacking integrated predictive capabilities and standardized risk monitoring frameworks across multiple vendors. This review paper examines existing predictive and conceptual models designed to support KPI dashboard integration and multi-vendor risk monitoring in government information systems. The study synthesizes literature on performance analytics, risk management architectures, business intelligence dashboards, and predictive modeling techniques applied to public-sector digital governance. Particular attention is given to machine learning–driven forecasting models, data integration frameworks, and conceptual governance architectures that support continuous monitoring of vendor performance, service-level compliance, and operational risks. The review further explores how integrated dashboards can consolidate heterogeneous data streams from procurement systems, contract management platforms, cybersecurity monitoring tools, and operational service metrics. By analyzing existing approaches, the paper proposes a structured conceptual model that links predictive analytics with dashboard visualization layers to enhance transparency, accountability, and proactive risk mitigation in government technology ecosystems. The findings highlight the importance of interoperable data architectures, standardized KPI frameworks, and predictive risk scoring mechanisms for improving oversight of multi-vendor environments. Ultimately, this review contributes to the development of more resilient, data-driven governance models capable of supporting complex government digital infrastructures.","政府机构日益依赖由多个技术供应商、外包服务提供商和分布式信息系统构成的复杂数字生态系统。尽管这些环境能够实现可扩展性和运营效率，但也带来了与绩效监测、问责制和供应商协调相关的重大风险。关键绩效指标（KPI）仪表盘已成为实时监测和决策支持的重要工具；然而，许多政府实施仍然较为分散，缺乏跨多个供应商的集成预测能力和标准化风险监测框架。本文综述了旨在支持政府信息系统中KPI仪表盘集成和多供应商风险监测的现有预测模型和概念模型。本研究综合了应用于公共部门数字治理的绩效分析、风险管理架构、商业智能仪表盘和预测建模技术方面的文献。研究特别关注机器学习驱动的预测模型、数据集成框架以及支持持续监测供应商绩效、服务水平合规性和运营风险的概念治理架构。本文进一步探讨了集成仪表盘如何整合来自采购系统、合同管理平台、网络安全监测工具和运营服务指标的异构数据流。通过分析现有方法，本文提出了一个结构化概念模型，将预测分析与仪表盘可视化层相连接，以增强政府技术生态系统中的透明度、问责制和主动风险缓解能力。研究结果强调了可互操作的数据架构、标准化KPI框架和预测性风险评分机制对于改善多供应商环境监督的重要性。最终，本综述有助于开发更具韧性、数据驱动的治理模型，以支持复杂的政府数字基础设施。","INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND MATHEMATICAL THEORY E-ISSN","2026-09-11T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":196},"主题为政府信息系统KPI仪表盘与多供应商风险监控，与三农、农业信息化、智慧农业、数字乡村无直接关联，相关性门槛未通过。",[198],{"name":193,"url":190},[200,201,202,23],"政府信息化","KPI仪表盘","多供应商风险",[204,205],"多供应商风险 政府信息化 预测分析 KPI仪表盘","多供应商风险 政府信息化","多供应商风险政府信息化预测分析KPI仪表盘-2413","10.56201\u002Fijcsmt.vol.12.no3.2026.pg263.294",{"doi":207,"openalex_id":209,"authors":210,"venue":193,"cited_by_count":15,"oa_url":213,"card":214,"direction":50,"ingested_from":51},"W7212218080",[211],{"name":212,"orcid":9},"Peter Chibwaye Irene","https:\u002F\u002Fiiardjournals.org\u002Fget\u002FIJCSMT\u002FVOL. 12 NO. 3 2026\u002FPredictive and Conceptual Models for KPI 263-294.pdf",{"tldr":215,"method":216,"finding":217,"direction":218,"opportunity":219},"综述政府KPI仪表盘集成与多供应商风险监控的预测与概念模型。","文献综述，综合绩效分析、风险管理架构、商业智能与机器学习预测模型。","提出连接预测分析与仪表盘可视化的概念模型，强调互操作数据架构与标准化KPI。","数字乡村与农业信息化","可将政府多供应商风险监控框架迁移至农业信息化项目，研究涉农多主体数据集成与KPI预警。","2026-09-14T23:30:09.787050Z",{"id":222,"title":223,"url":224,"summary":225,"summary_zh":226,"content":9,"source_name":227,"source_url":224,"published_at":228,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":229,"score_detail":230,"sources":233,"tags":235,"search_phrases":240,"slug":243,"view_count":55,"doi":244,"paper":245,"created_at":282},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。","PLoS Computational Biology","2026-09-10T00:00:00Z",79,{"impact":100,"substance":133,"depth":100,"authority":134,"freshness":231,"relevant":55,"comment":232},7,"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。",[234],{"name":227,"url":224},[22,236,237,238,239],"空间分析","动物疫病","疫情预测","智慧畜牧",[241,242],"决策支持 动物疫病 智慧畜牧 疫情预测","决策支持 动物疫病","决策支持动物疫病智慧畜牧疫情预测-2340","10.1371\u002Fjournal.pcbi.1014716",{"doi":244,"openalex_id":246,"authors":247,"venue":227,"cited_by_count":15,"oa_url":276,"card":277,"direction":89,"ingested_from":51},"W7212183958",[248,251,254,257,259,262,264,266,269,271,273],{"name":249,"orcid":250},"Meryl Theng","https:\u002F\u002Forcid.org\u002F0000-0002-1016-1487",{"name":252,"orcid":253},"Simin Lee","https:\u002F\u002Forcid.org\u002F0000-0003-2631-9899",{"name":255,"orcid":256},"Andrew C. Breed","https:\u002F\u002Forcid.org\u002F0000-0002-3439-9510",{"name":258,"orcid":9},"Sharon Roche",{"name":260,"orcid":261},"Emily Sellens","https:\u002F\u002Forcid.org\u002F0000-0001-9360-1419",{"name":263,"orcid":9},"Catherine Fraser",{"name":265,"orcid":9},"Kelly Wood",{"name":267,"orcid":268},"Chris Jewell","https:\u002F\u002Forcid.org\u002F0000-0002-7902-2178",{"name":270,"orcid":9},"Mark A. Stevenson",{"name":272,"orcid":9},"Chris Baker",{"name":274,"orcid":275},"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":278,"method":279,"finding":280,"direction":89,"opportunity":281},"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。","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。","2026-09-13T23:30:46.953723Z"]