Anderson, Carrie
(2013)
Agent-based modeling of coccidioidomycosis.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
Coccidioidomycosis is a fungal infection with an estimated yearly incidence of 150,000 cases in the United States. Up to 50 percent of those cases are estimated to occur in Maricopa County, Arizona, the geographical focus of this dissertation. Maricopa County is a hotspot for coccidioidomycosis due to its unique environmental and climactic conditions including its soil type, geology, dust storms, and temperature. Although there has been a large amount of research on the epidemiology of coccidioidomycosis in Maricopa County and elsewhere, forecast modeling of disease incidence has not been well established. Current analyses focus on demographic and environmental factors and their effect sizes but have limited use for modeling the public health impact of events such as dust storms or vaccination strategies. However by incorporating results from previous studies, and including historical data from the Centers for Disease Control and Prevention and Maricopa County Department of Health, stochastic epidemiological agent-based modeling of coccidioidomycosis can be successfully performed. The development and validation of such a model and its public health significance in forecasting coccidioidomycosis incidence are described in this dissertation. Among the findings, a moderately sized dust storm in Maricopa County would be expected to increase coccidioidomycosis morbidity by 4,676 cases and mortality by 42 cases. The development of a vaccine against coccidioidomycosis could decrease annual morbidity by 5,979 cases if individuals get vaccinated at rates comparable to influenza. Even a vaccination campaign that is one-fourth as effective as an influenza campaign would still have a significant impact on public health with a reduction in annual morbidity by 2,361 cases. Further with the development of the web-based tool described in this dissertation, public health researchers and epidemiologists can use the model to forecast disease morbidity and mortality for other endemic regions. The tool can also be used to forecast disease burden for hypothetical scenarios such as the development of a vaccine against coccidioidomycosis. In summary this dissertation uses stochastic epidemiological agent-based modeling to forecast incidence and assess the public health impact of vaccination and natural events such as dust storms, and provides a valuable tool for epidemiologists and researchers.
Share
| Citation/Export: |
|
| Social Networking: |
|
Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
|
| Date: |
27 June 2013 |
| Date Type: |
Publication |
| Defense Date: |
3 April 2013 |
| Approval Date: |
27 June 2013 |
| Submission Date: |
2 April 2013 |
| Access Restriction: |
2 year -- Restrict access to University of Pittsburgh for a period of 2 years. |
| Number of Pages: |
135 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
School of Public Health > Epidemiology |
| Degree: |
PhD - Doctor of Philosophy |
| Thesis Type: |
Doctoral Dissertation |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
agent-based modeling coccidioidomycosis |
| Date Deposited: |
27 Jun 2013 18:03 |
| Last Modified: |
15 Nov 2016 14:10 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/18000 |
Metrics
Monthly Views for the past 3 years
Plum Analytics
Actions (login required)
 |
View Item |
![[feed]](/images/feed-icon-32x32.png){kind=icon}