Introduction: One of the major barriers to developing an accurate tuberculosis (TB) surveillance program for Florida is the design and implementation of a sampling system that will adequately monitor and predict varying sizes and characteristics of county-level vulnerable endemic sub-populations and their explanatory covariates (e.g., living or working in a residential care facility). The aim of this research study is to envision an endemic, tuberculosis-related web-based interface for use by public health officials in the State of Florida which includes generating essential information such as a real-time syndrome-based reporting to regulate automated and immediate 'Alerts' to public health officials, doctors, hospitals and local community in ArcGIS. This study demonstrates the capability of an autocorrelation, time series, epidemiological, interpolative, and vulnerable predictive ArcGIS model to target tuberculosis at the county-level in the state of Florida.
Methodology: The data for constructing an autocorrelation, probabilistic paradigm was acquired from the Centers for Disease Control and Prevention [CDC] in Atlanta, Georgia. The full dataset contained two points in time, allowing estimation of a mixed binomial model that aided in predicting the probability of tuberculosis by county. The random effects term in the ArcGIS model was comprised of spatially structured and stochastic effects (i.e., spatially unstructured) terms. These terms substituted for covariates in the model. The assumption was that random effects term in the endemic, TB–related, explanative, county-level, risk model had a frequency distribution that was bell-shaped (i.e., normally/Gaussian distributed) with a mean of zero.
Results: The results indicated the empirical estimate had a mean of 0.0197 with a Shapiro-Wilk normality probability of 0.0027. The mean in the model was not exactly zero, although the forecasts indicated 0.06, which was not significantly different from zero. It was noted that the frequency distribution deviated from a bell-shaped curve. This random effects term accounted for roughly 41% of the variability in the observed probability of TB by county and yielded an under dispersed binomial model. An eigenvector spatial filter description of the random effects term involved 5 of 18 total eigenvectors, which portrayed noticeable positive spatial autocorrelation. The decomposition algorithm also revealed 4 of 25 eigenvectors portraying noticeable negative spatial autocorrelation. These two spatial filter components accounted for, respectively, roughly 16% and 10% of the variability in the probability of TB by county. The spatially unstructured random effects component accounted for roughly 15% of this variability. The final model revealed that from 2015 to 2020, Duval, Orange, and Broward counties would require immediate intervention in order to prevent TB transmission. The model also revealed that from 2025 to 2040 Hillsborough and Palm Beach counties could become hyper-endemic without implementation of control strategies.
Conclusion: An endemic, TB-related, ArcGIS, autocorrelation eigenanalyses forecast, paradigm may be employed by public health officials in Florida to target, vulnerable, county level ,populations A Precede-Proceed model-based reporting mechanism may help disseminate the ArcGIS model results and help regulate automated and immediate 'Alerts' to public health officials, doctors, hospitals and local community at the county-level. An ArcGIS, web-based, epidemiological tool for data entry and communication can also allow real-time , predictive, real-time mapping of any TB county outbreaks Precede –Proceed model may be employed by county-level public health officials in Florida to disseminate and prioritize county-level, TB model, epidemiological, information to their constituents. In so doing, factors regulating outbreaks of county-level TB may be accurately identified.
| Identifer | oai:union.ndltd.org:USF/oai:scholarcommons.usf.edu:etd-7741 |
| Date | 02 November 2016 |
| Creators | Moradi, Ali |
| Publisher | Scholar Commons |
| Source Sets | University of South Flordia |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Graduate Theses and Dissertations |
| Rights | default |
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