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

A method for characterizing disease emergence curves from paired pathogen detection and serology data