Tomographic reconstruction of a disease transmission landscape via GPS recorded random paths

2024-04-05

AI Summary

This study addresses the challenge of identifying high-risk areas for Epizootic Hemorrhagic Disease Virus (EHDV) infection in deer populations at the landscape scale to enable precision disease management. We propose a novel “epidemiological tomography” framework: modeling GPS-tracked stochastic movement paths as random Radon transforms and formulating serological antibody data as a binomial linear inverse problem. To ensure sparse and robust reconstruction, we incorporate total variation regularization coupled with quantile universal thresholding, and augment uncertainty quantification—particularly under small-sample conditions—via bootstrap resampling. Evaluated on both synthetic and empirical datasets, our method consistently outperforms existing approaches. It achieves the first spatial tomographic reconstruction of EHDV infection risk, markedly improving the accuracy and spatial resolution of vector-borne transmission hotspot identification.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

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. The smoothness of the reconstructed propensity map is selected by the quantile universal threshold. To address limitations of small sample sizes and evaluate significance of our estimates, we quantify uncertainty using a bootstrap-based data augmentation procedure. Our method outperforms alternative ones when using simulated and real data. This tomographic framework is novel, with no established statistical methods tailored for such data.

Problem

• Estimating disease infection landscape using GPS animal tracks
• Modeling disease spread via Radon transforms and spatial regularization
• Addressing small sample limitations with bootstrap-based uncertainty quantification

Innovation

• GPS animal tracks for disease landscape tomography
• Total variation regularization for spatial coherence
• Bootstrap-based data augmentation for uncertainty quantification