Wildlife disease surveillance programs and research studies track infection and identify risk factors for wild populations, humans, and agriculture. Often, several types of samples are collected from individuals to provide more complete information about an animal's infection history. Methods that jointly analyze multiple data streams to study disease emergence and drivers of infection via epidemiological process models remain underdeveloped. Joint-analysis methods can more thoroughly analyze all available data, more precisely quantifying epidemic processes, outbreak status, and risks. We contribute a paired data modeling approach that analyzes multiple samples from individuals. We use "characterization maps" to link paired data to epidemiological processes through a hierarchical statistical observation model. Our approach can provide both Bayesian and frequentist estimates of epidemiological parameters and state. We motivate our approach through the need to use paired pathogen and antibody detection tests to estimate parameters and infection trajectories for the widely applicable susceptible, infectious, recovered (SIR) model. We contribute general formulas to link characterization maps to arbitrary process models and datasets and an extended SIR model that better accommodates paired data. We find via simulation that paired data can more efficiently estimate SIR parameters than unpaired data, requiring samples from 5-10 times fewer individuals. We then study SARS-CoV-2 in wild White-tailed deer (Odocoileus virginianus) from three counties in the United States. Estimates for average infectious times corroborate captive animal studies. Our methods use general statistical theory to let applications extend beyond the SIR model we consider, and to more complicated examples of paired data.

翻译：野生动物疾病监测项目与相关研究追踪感染动态，识别野生种群、人类及农业系统中的风险因素。通常从个体采集多种类型样本，以更完整了解动物的感染史。利用流行病学过程模型联合分析多源数据流以研究疾病爆发与感染驱动因素的现有方法仍不成熟。联合分析方法能更全面地利用所有可用数据，更精确地量化流行病过程、爆发状态及风险。我们提出一种配对数据建模方法，用于分析个体多类型样本。通过"表征图谱"构建分层统计观测模型，将配对数据与流行病学过程相关联。该方法可提供流行病学参数与状态的贝叶斯及频率学派估计值。本研究以需要利用配对病原检测与抗体检测数据来估计易感-感染-恢复(SIR)模型的参数与感染轨迹的实际需求为动机，提出将表征图谱与任意过程模型及数据集关联的通用公式，并开发能更好适配配对数据的扩展SIR模型。模拟研究表明：配对数据较非配对数据能更高效地估计SIR参数，所需样本量可减少5-10倍。我们随后对美国三个县野生白尾鹿(Odocoileus virginianus)的SARS-CoV-2进行研究，平均感染时间的估计结果与圈养动物研究相符。该方法基于通用统计学理论，应用范围可扩展至SIR模型之外更复杂的配对数据场景。