从制图到预测：对评估家禽中禽流感侵入风险的方法学途径的系统综述与批判性评价。
完整摘要 · 中文译文
高致病性禽流感（HPAI）病毒持续威胁全球家禽生产和公共卫生。为考察方法学手段如何被用于评估这一风险，我们对评估家禽中禽流感病毒（AIV）传入风险的同行评议研究进行了系统综述和批判性评价，检索使用了PubMed和Web of Science数据库。定量建模方法涵盖从统计性、数据驱动方法到机制性框架，并在不同的空间和时间尺度上运行，以养殖场或地理区域作为感兴趣的流行病学单元。在各研究中，分析选择在很大程度上受数据可得性所塑造，而数据可得性限制了可达到的分辨率并影响了模型能够回答的问题。大多数应用集中于区域层面分析，往往描述历史空间风险模式，而非支持前瞻性推断。统计模型经常依赖与既往传入相关的代理变量，限制了生物学可解释性和稳健性。近期野生鸟类监测的改进——包括公民科学倡议——使得对野生鸟类动态的表述更为明确，尽管家禽种群数据的持续缺口仍在许多环境中限制模型开发。在养殖场层面数据有限的环境中，通常采用机制性方法，依赖关于 underlying 疾病传播动态的假设。各研究共同面临的一个持续挑战是模型验证有限，反映出疫情数据稀疏。 以及疫情暴发的空间和时间聚集性。当在农场层面和区域层面的输出之间进行外推时，尺度不匹配以及生态谬误和原子谬误的风险使解释进一步复杂化。总体而言，本综述强调了确保风险问题和预测期望反映可用数据及验证可行性的重要性。
完整摘要 · 英文原文
Highly pathogenic avian influenza (HPAI) viruses continue to threaten global poultry production and public health. To examine how methodological approaches have been used to assess this risk, we conducted a systematic review and critical appraisal of peer-reviewed studies assessing avian influenza virus (AIV) incursion risk in domestic poultry, using PubMed and Web of Science databases. Quantitative modelling approaches ranged from statistical, data-driven methods to mechanistic frameworks and operated at different spatial and temporal scales, using farms or geographical areas as the epidemiological unit of interest. Across studies, analytical choices were largely shaped by data availability, which constrained achievable resolution and influenced the questions models could address. Most applications focused on area-level analyses, often describing historical spatial risk patterns rather than supporting prospective inference. Statistical models frequently relied on proxy variables linked to past incursions, limiting biological interpretability and robustness. Recent improvements in wild bird surveillance, including citizen-science initiatives, have enabled more explicit representation of wild bird dynamics, although persistent gaps in poultry population data continue to restrict model development in many settings. In settings where farm-level data are limited, mechanistic approaches are commonly applied, relying on assumptions regarding underlying disease transmission dynamics. A consistent challenge across studies was limited model validation, reflecting sparse outbreak data and spatial and temporal clustering of outbreaks. Interpretation was further complicated by scale mismatches and risks of ecological and atomistic fallacies when extrapolating between farm-level and area-level outputs. Overall, the review highlights the importance of ensuring that risk questions and predictive expectations reflect the available data and the feasibility of validation.