时空机器学习实现蓝舌病疫情多时段预测
Spatio-temporal machine learning for multi-horizon prediction of bluetongue outbreaks
一个logit加权集成（LWE）机器学习框架可在行政单元—月尺度上提前1–6个月预测蓝舌病病毒（BTV）疫情发生。该研究使用2005–2024年法国、希腊和意大利的疫情通报数据与网格化气候数据，在侧重召回率的评估下，LWE在三国均取得最强且最稳定的预测表现，优于或持平14个基准模型，且从提前3个月起优势更明显。预测能力存在国别差异：希腊最高、法国较强、意大利较低且波动较大，反映各地疫情发生与传播规律的一致性不同。结果表明，融合气候信息并区分预测时段的预报可提前至多6个月识别高风险月份与地点，支持欧洲异质环境下的监测规划与准备。
来源：PLOS Computational Biology · journals.plos.org
by Lucy M. Devlin, Phi H. Nguyen, Ross N. Cuthbert, Phu Ngoc Doan, Viet Hung Tran, Zichi Zhang, Archie K. Murchie, Connor G. G. Bamford, Jaimie T. A. Dick, Eric R. Morgan, Thai S. Mai
Reliable early warning of infectious disease outbreaks remains a major challenge for surveillance systems, particularly for vector-borne pathogens whose transmission depends on interactions among hosts, vectors, and climate-sensitive environmental conditions. Data-driven forecasting offers a promising approach for predicting outbreak risk using surveillance and environmental data. This study develops a logit-weighted ensemble (LWE), a machine-learning framework that predicts outbreak occurrence 1–6 months ahead at the administrative unit–month scale using routinely available outbreak notifications and gridded climate data. Bluetongue virus (BTV), an arbovirus of ruminants transmitted by Culicoides biting midges, provides a well-characterised system in which transmission is strongly shaped by climate, making it a useful system for applying and testing this approach. The framework is evaluated using surveillance data collected between 2005 and 2024 from France, Greece, and Italy, selected for their long-running and high-quality outbreak surveillance records. Across all three countries, the LWE achieved the strongest and most stable predictive performance under a recall-focused evaluation that prioritises correctly identifying outbreak months. It outperformed or matched 14 benchmark models, with differences becoming more pronounced at longer lead times (month +3 onward), when predictions are more uncertain and outbreaks are relatively rare. Predictability varied across countries, with the highest performance in Greece, strong performance in France, and lower, more variable performance in Italy, reflecting differences in how consistently outbreaks occur and spread across regions. Overall, the results demonstrate that horizon-aware, climate-informed forecasting can reliably identify months and locations at elevated risk of outbreak occurrence up to six months in advance, supporting surveillance planning and preparedness across heterogeneous European settings. The ensemble framework provides a robust and portable strategy for outbreak prediction using routinely collected surveillance and environmental data.