TOMOGRAPHIC RECONSTRUCTION OF DISEASE TRANSMISSION LANDSCAPES FROM GPS-RECORDED RANDOM PATHS

• 成果类型: Article
• 署名作者: Diaz-Rodriguez, Jairo; Gomez, Juan Pablo; Orange, Jeremy P.; Burkett-Cadena, Nathan D.; Wisely, Samantha M.; Blackburn, Jason K.; Sardy, Sylvain
• 署名单位: York University - Canada; Universidad del Norte Colombia; State University System of Florida; University of Florida; State University System of Florida; University of Florida; State University System of Florida; University of Florida; State University System of Florida; University of Florida; University of Geneva
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157; 1941-7330
• DOI: 10.1214/25-AOAS2091
• 发表日期: 2025-12
• 页码: 3244-3260
• 关键词: GPS movements; inverse problem; radon transform; total variation; regularization; TOTAL VARIATION MINIMIZATION; white-tailed deer; HEMORRHAGIC-DISEASE; RISK

摘要

Identifying areas in a landscape where individuals have a higher likelihood of disease infection is key to managing diseases. Unlike conventional methods relying on ecological assumptions, we perform a novel epidemiological tomography for the estimation of landscape propensity to disease infection, using GPS animal tracks in a manner analogous to tomographic techniques in positron emission tomography (PET). Treating tracking data as random Radon transforms, we analyze Cervid movements in a game preserve, paired with antibody levels for epizootic hemorrhagic disease virus (EHDV)-a vector-borne disease transmitted by biting midges. After discretizing the field and building the regression matrix of the time spent by each deer (row) at each point of the lattice (column), we model the binary response (infected or not) as a binomial linear inverse problem where spatial coherence is enforced with a total variation regularization. To address limitations of small sample sizes and evaluate significance of our estimate, we quantify uncertainty using a bootstrap-based data augmentation procedure. Our method achieved superior performance in the majority of simulated scenarios and real data. Notably, our tomographic estimator frequently reduces the mean squared error by about half compared to direct empirical estimators. This tomographic framework is novel, with no established statistical methods tailored for such data.