A Method for Characterizing Disease Emergence Curves from Paired Pathogen Detection and Serology Data

Authors

Joshua Hewitt, Utah State UniversityFollow
Grete Wilson-Henjum, Utah State University
Derek T. Collins, United States Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, National Wildlife Disease Program, Fort Collins, Colorado
Jourdan M. Ringenberg, United States Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, National Wildlife Disease Program, Fort Collins, ColoradoFollow
Christopher A. Quintanal, United States Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, National Wildlife Diagnostic Laboratory, Fort Collins, Colorado
Robert Pleszewski, United States Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, National Wildlife Diagnostic Laboratory, Fort Collins, Colorado
Jeffrey C. Chandler, United States Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, National Wildlife Diagnostic Laboratory, Fort Collins, ColoradoFollow
Thomas J. DeLiberto, United States Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, Fort Collins, ColoradoFollow
Kim M. Pepin, United States Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, National Wildlife Research Center, Fort Collins, ColoradoFollow

ORCID IDs

Joshua Hewitt https://orcid.org/0000-0002-0844-7769

Grete Wilson-Henjum https://orcid.org/0000-0002-2284-8745

Derek T. Collins https://orcid.org/0000-0002-3723-2353

Jourdan M. Ringenberg https://orcid.org/0000-0001-9492-3874

Christopher A. Quintanal https://orcid. org/0000-0003-0687-0528

Robert Pleszewski https://orcid.org/0000-0003-1481-0242

Jeffrey C. Chandler https://orcid.org/0000-0002-3318-135X

Thomas J. DeLiberto https://orcid.org/0000-0003-1115-1472

Kim M. Pepin https://orcid.org/0000-0002-9931-8312

Document Type

Article

Date of this Version

2024

Citation

Methods in Ecology and Evolution (2024) 15: 1,677-1,690

doi: 10.1111/2041-210X.14370

Data and code used for this manuscript are available at https://doi.org/10.15482/USDA.ADC/25922608

Comments

United States government work

Abstract

1. 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 analyse multiple data streams to study disease emergence and drivers of infection via epidemiological process models remain underdeveloped. Joint-analysis methods can more thoroughly analyse all available data, more precisely quantifying epidemic processes, outbreak status, and risks.

2. We contribute a paired data modelling approach that analyses 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. Our approach can also incorporate test sensitivity and specificity, and we propose model fit diagnostics. 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.

3. We find via simulation that paired data can more efficiently estimate SIR parameters than unpaired data, requiring samples from 5 to 10 times fewer individuals. We use our method to 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. The estimated average cumulative proportion of infected deer across the three counties is 73%, and the basic reproductive number (R₀) is 1.88.

4. Wildlife disease surveillance programs and research studies can use our methods to jointly analyze paired data to estimate epidemiological process parameters and track outbreaks. Paired data analyses can improve precision and accuracy when sampling is limited. Our methods use general statistical theory to let applications extend beyond the SIR model we consider and to more complicated examples of paired data. The methods can also be embedded in larger hierarchical models to provide landscape-scale risk assessment and identify drivers of infection.