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.

翻译：识别景观中个体具有较高疾病感染风险的区域是疾病管理的关键。与传统依赖生态假设的方法不同，我们提出了一种新颖的流行病学层析成像方法，用于估计景观对疾病感染的易感性，其方式类似于正电子发射断层扫描（PET）中的层析技术。我们将追踪数据视为随机Radon变换，分析了一个狩猎保护区内鹿科动物的活动轨迹，并结合了流行性出血病病毒（EHDV）的抗体水平数据——这是一种由蠓虫叮咬传播的媒介传播疾病。在对区域进行离散化，并构建每头鹿（行）在网格各点（列）停留时间的回归矩阵后，我们将二元响应（感染与否）建模为一个二项式线性逆问题，其中通过全变差正则化来增强空间连贯性。重建的易感性地图的平滑度通过分位数通用阈值进行选择。为应对小样本量的限制并评估估计的显著性，我们采用了一种基于自助法的数据增强程序来量化不确定性。在使用模拟和真实数据时，我们的方法优于其他替代方法。该层析成像框架是新颖的，目前尚无专门针对此类数据建立的统计方法。