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Unfortunately, currently there is no literature contribution describing the frequency and distribution of potential, seasonal, Aedes (Ae.) aegypti, superbreeder, capture point covariates and their effect on epidemics of emerging diseases in county abatements. Ae. aegypti is one of the most significant mosquito species as it is capable of transmitting dengue fever, chikungunya, Zika, and yellow fever viruses. Here we regress an empirical dataset of potential, superbreeder, multivariate, Ae. aegypti, habitat, estimators (e.g., container volume, temperature, and relative humidity) spatiotemporally associated with immature, abundance counts of geographically sampled (henceforth geosampled), county abatement, capture points, to simulate seasonal prolific habitats in a single eco-geographic foci in Hillsborough County Florida. We employ an inferential, hierarchical, Bayesian paradigm with a subjective, maximum, likelihood (ML) estimator to unbiasedly parameterize the epi-entomological, time series dataset of seasonal sampled, aquatic habitat, optimizable signature prognosticators. A probabilistic, framework for distinguishing chaotic, random, geometric uncertainties about covariance matrices is demonstrated in PROC MCMC.
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This page is a summary of: Gaussianization of Variational Bayesian Approximations with Correlated Non-nested Non-negligible Posterior Mean Random Effects Employing Non-negativity Constraint Analogs and Analytical Depossinization for Iteratively Fitting Capture Point, Aedes aegyp..., January 2020, Springer Science + Business Media,
DOI: 10.1007/978-3-030-23491-1_12.
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