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Agent-based modeling of coccidioidomycosis

Anderson, Carrie (2013) Agent-based modeling of coccidioidomycosis. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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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.


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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Anderson, Carriecma246@gmail.com
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairWisniewski, Stephenwisniew@edc.pitt.eduSTEVEWIS
Committee MemberTalbott, Evelyneot1@pitt.eduEOT1
Committee MemberShim, Eunhaeshim@pitt.eduESHIM
Committee MemberLake, Douglasdouglas.lake@asu.edu
Committee MemberGrefenstette, Johngref@pitt.eduGREF
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: Graduate 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

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