Spatial estimation of virus infection propensity in hosts determined from GPS-based space-time locations
arxiv(2024)
摘要
Identifying areas in a landscape where individuals have a higher probability
of becoming infected with a pathogen is a crucial step towards disease
management. Our study data consists of GPS-based tracks of individual
white-tailed deer (Odocoileus virginianus) and three exotic Cervid
species moving freely in a 172-ha high-fenced game preserve over given time
periods. A serological test was performed on each individual to measure the
antibody concentration of epizootic hemorrhagic disease virus (EHDV) for each
of three serotypes (EHDV-1, -2, and -6) 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 (Culicoides spp.). The purpose
of this study is to estimate the spatial distribution of infection propensity
by performing an epidemiological tomography of a region using tracers. 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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要