A multi-state occupancy modeling framework for robust estimation of disease prevalence in multi-tissue disease systems

1. Given the public health, economic, and conservation
implications of zoonotic diseases, their effective surveillance is of
paramount importance. The traditional approach to estimating pathogen
prevalence as the proportion of infected individuals in the population is
biased because it fails to account for imperfect detection. A
statistically robust way to reduce bias in prevalence estimates is to
obtain repeated samples (or sample many tissues in multi-tissue disease
systems) and to apply statistical methods that account for imperfect
detection and permit the interdependence of the infection process across
multiple tissues. 2. We developed a multi-state occupancy modeling
framework which considers two scenarios about the infection process, one
where no assumptions about the dependencies among the tissues are made
(general), and another where dependence among tissues is not permitted
(constrained). 3. We applied this model to pseudorabies virus (PrV) DNA
detection data obtained from whole blood; and oral, nasal, and genital
mucosa of 510 feral swine (Sus scrofa) during the years 2014-2016 in
Florida, USA. 4. The constrained model was better supported by data.
Estimated PrV prevalence varied among tissues, ranging from to 0.06 (CI:
0.02-0.14) in genital to 0.54 (CI: 0.14-0.82) in nasal tissue. Probability
of PrV detection ranged from 0.11 (CI: 0.06-0.18) in nasal to 0.51 (CI:
0.21-0.81) in genital tissue. Estimates of PrV prevalence after accounting
for imperfect detection were higher than the naïve estimates for all four
tissues. 5. PrV prevalence was not affected by the age or sex of the
animal or the year of sampling, but prevalence increased as drought
severity increased. 6. The conditional probability of detecting PrV given
infection in at least one tissue type within an individual was highest for
nasal tissue, suggesting that nasal is the best tissue to sample for PrV
surveillance if only one tissue can be sampled, at least for systems with
tissue-specific prevalence and detection probabilities similar to ours. 7.
We found that pathogen prevalence in multi-tissue disease systems can vary
across tissues. Our results emphasize the importance of sampling multiple
tissues, and the application of robust statistical models to account for
imperfect detection in the surveillance of systemic diseases. The
multi-state modeling framework is broadly applicable to the surveillance
of pathogens that infect multiple tissues and where the infection status
or detection of the pathogen in one tissue may depend on the infection
status of the pathogen in other tissues). 29-Jul-2020