Identifying areas in a landscape where individuals have higher probability of becoming infected with a pathogen is a crucial step towards disease management. 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. Our study data consists of individual tracks of white-tailed deer (Odocoileus virginianus) and three exotic Cervidae species moving freely in a high-fenced game preserve over given time periods. A serological test was performed on each individual to measure antibody concentration of epizootic hemorrhagic disease viruses (EHDV) at the beginning and at the end of each tracking period. EHDV is a vector-borne viral disease indirectly transmitted between ruminant hosts by biting midges. We model the data 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, which can also test the null hypothesis that the propensity map is spatially constant. We apply our method to simulated and real data, showing good statistical properties during simulations and consistent results and interpretations compared to intensive field estimations.

翻译：识别景观中个体更易感染病原体的区域是疾病管理的关键步骤。我们采用一种新颖的流行病学层析成像方法，通过使用GPS动物轨迹数据来估计景观的疾病感染倾向性，其原理类似于正电子发射断层扫描中的层析技术。本研究数据包含白尾鹿（Odocoileus virginianus）和三种外来鹿科物种在特定时间段内于高围栏狩猎保护区内自由活动的个体轨迹。在每个追踪期开始和结束时，对每个个体进行血清学检测以测量流行性出血病病毒（EHDV）的抗体浓度。EHDV是一种由媒介传播的病毒性疾病，通过蠓虫叮咬在反刍动物宿主间间接传播。我们将数据建模为二项式线性逆问题，并通过全变分正则化强制空间连贯性。重建的倾向性图的平滑度通过分位数通用阈值进行选择，该方法亦可检验倾向性图在空间上保持恒定的零假设。我们将本方法应用于模拟数据和真实数据，在模拟中展现出良好的统计特性，与密集实地评估相比获得了具有一致性的结果和解释。