Occupancy models are widely used in camera trap studies to analyze species presence, abundance, and geographic distribution, among other important ecological quantities. These models account for imperfect detection using a latent variable to distinguish between true presence/absence and observed detection of a species. Under certain experimental setups, parameter estimation in a latent variable framework can be challenging. Several studies have issued guidelines on the number of independent replicated observations (surveys) needed for each unchanging occupancy field (season) to ensure reliable estimation. In this paper, we present a spatio-temporal occupancy model, and show through a simulation study that it can be fit to data obtained from a single survey per season, so long as the number of seasons is sufficiently large. We include an application using camera-trap data on the Thomson's gazelle in the Serengeti in Tanzania.
We present a multivariate occupancy model to simultaneously model the presence/absence of multiple species, and demonstrate its use with a goal of estimating parameters related to occupancy. The proposed model accounts for both spatial and temporal dependence within each species, as well as dependence across all species. These dependencies are addressed through random effects, defined so there is no confounding with estimating occupancy covariate effects. Data augmentation and specific choices for the random effects permit all Gibbs updates in the Markov chain Monte Carlo algorithm, making the model computationally efficient and scalable with the number of species and size of spatial domain. A simulation study shows that the model outperforms single‐species spatiotemporal occupancy models with regard to estimating occupancy parameters. We demonstrate the model with a three species camera trap study on Thomson's gazelle, wildebeest, and zebra in the Serengeti National Park of Tanzania, Africa.
Purpose-Opioid misuse is a national epidemic, and Ohio is one of the states most impacted by this crisis. Ohio collects county-level counts of opioid associated deaths and treatment admissions. We jointly model these two outcomes and assess the association of each rate with social and structural factors. Methods-We use a joint spatial rates model of death and treatment counts using a generalized common spatial factor model. In addition to covariate effects, we estimate a spatial factor for each county that characterizes structural factors not accounted for by other covariates in the model that are associated with both outcomes. Results-We observed an association of health professional shortage area with death rates and the rate of people 18-64 on disability with treatment rates. The proportion of single female households was associated with both outcomes. We estimated the presence of unmeasured risk factors in the southwestern part of the state and unmeasured protective factors in the eastern region. Conclusions-We described associations of social and structural covariates with the death and treatment rates. We also characterized counties with latent risk that can provide a launching point for future investigations to determine potential sources of that risk.
Background: The opioid epidemic continues to be an ongoing public health crisis in the United States. Initially, large increases in overdose death rates were observed in largely rural, White communities, leading to the initial perception that the opioid epidemic was primarily a problem for the White population. Recent findings have shown increasing rates of overdose death among Blacks. We compare overdose rates between Blacks and Whites and explore county-level spatiotemporal heterogeneity in Ohio. Methods: We obtained county-level opioid overdose death counts for Whites and Blacks from 2007 to 2018 in Ohio. We fit a Bayesian multivariate spatial rates model to estimate annual standardized mortality ratios for Whites and Blacks for each county. We accounted for correlation between racial groups in the same county and across space and time. We also estimated differences in the mean trends between urban and rural counties for each racial group. Results: The overall overdose death rate in the state was increasing until 2018. County-level death rates for Whites were higher than Blacks throughout the state early in the study period. Death rates for Blacks increased throughout the study period and were comparable to the rates for Whites by the end of the study in many counties. Conclusions: County-level opioid overdose death rates increased faster for Blacks than Whites during the study. By 2018, death rates were comparable for Blacks and Whites in many counties. The opioid epidemic spans racial groups in Ohio and trends indicate that overdose is a growing problem among Blacks.
Background: Opioid misuse is a major public health issue in the United States and in particular Ohio. However, the burden of the epidemic is challenging to quantify as public health surveillance measures capture different aspects of the problem. Here we synthesize countylevel death and treatment counts to compare the relative burden across counties and assess associations with social environmental covariates. Methods: We construct a generalized spatial factor model to jointly model death and treatment rates for each county. For each outcome, we specify a spatial rates parameterization for a Poisson regression model with spatially varying factor loadings. We use a conditional autoregressive model to account for spatial dependence within a Bayesian framework. Results: The estimated spatial factor was highest in the southern and southwestern counties of the state, representing a higher burden of the opioid epidemic.We found that relatively high rates of treatment contributed to the factor in the southern part of the state; whereas, relatively higher rates of death contributed in the southwest. The estimated 1 arXiv:1806.05232v1 [stat.AP]
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