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Developing a method for characterizing genealogy of parameter values from epidemiological models of infectious disease

Haposan, Jonathan (2019) Developing a method for characterizing genealogy of parameter values from epidemiological models of infectious disease. Master Essay, University of Pittsburgh.

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Abstract

Background: Recently, the number of epidemiological models in infectious disease (EMID) has been remarkably growing. The origin of parameter values used by EMID is often difficult to determine. Models may have used parameter values from populations that do not necessarily correspond to the population being modeled due to the limited existing knowledge about a population of interest. No systematic method has been published that evaluates the “data origin” of EMID parameter values.
Objectives: The primary objective of this study is to improve reproducibility and representativeness of EMID, through developing and testing a method for characterizing the genealogy of parameter values of the models.
Methods: We first developed a method process to characterize the genealogy of parameter values and then applied our method to a set of example models that represented transmission of the dengue virus with dengue vaccination’s introduction.
Results: We characterized the genealogy of EMID parameter values for 2 models using table representations, model diagrams, and genealogical networks. We found that in Notre Dame model 46.5% of parameter values were based on observational data and 27.9% were based on assumptions, while in Imperial model 40.4% were based on observational data and 54.4% were based on assumptions. Respectively, about 25.6% and 5.2% of parameter values from Notre Dame and Imperial models were based on models or formula or derivation from other parameters.
Conclusion: Our method of characterizing genealogy of parameter values from epidemiological models can help better understand the data-origin of models and help researchers evaluate a model’s representativeness.
Public Health Significance: This study used a genealogical approach in epidemiological modeling and public health that would enrich the public health & epidemiology field. This study provides a platform for future researchers in public health to improve the modeling system and outcomes.


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Details

Item Type: Other Thesis, Dissertation, or Long Paper (Master Essay)
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Haposan, Jonathanjhh32@pitt.edujhh320000-0002-3455-6379
Contributors:
ContributionContributors NameEmailPitt UsernameORCID
Committee ChairVan Panhuis, Wilbertwav10@pitt.eduwav10UNSPECIFIED
Committee MemberPeddada, Shyamal D.sdp47@pitt.edusdp47UNSPECIFIED
Committee MemberPyne, Saumyadiptaspyne@pitt.eduUNSPECIFIEDUNSPECIFIED
Centers: Other Centers, Institutes, or Units > Public Health Dynamics Laboratory
Date: 26 April 2019
Date Type: Submission
Number of Pages: 56
Institution: University of Pittsburgh
Schools and Programs: Graduate School of Public Health > Epidemiology
Degree: MPH - Master of Public Health
Thesis Type: Master Essay
Refereed: Yes
Date Deposited: 04 Oct 2019 22:27
Last Modified: 04 Oct 2019 22:27
URI: http://d-scholarship.pitt.edu/id/eprint/36731

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