  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.
