Pokutnaya, Darya
(2023)
Developing and validating a comprehensive implementation framework for reporting reproducible infectious disease computational modeling studies.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
In the wake of the coronavirus disease 2019 (COVID-19) pandemic, policymakers have relied heavily on computational models to inform decisions concerning public health interventions. Unfortunately, reproducibility of computational modeling studies is limited due to methodological complexity and lack of transparent reporting practices. We filled this critical gap in the literature by developing an implementation framework for representing infectious disease computational models in a reproducible way, grounded in previous research on reproducibility from a broad range of scientific disciplines. The implementation framework provides a foundation that can be further developed into tools, such as checklists or machine-interpretable metadata, for sharing computational models in a reproducible manner.
We formatted the implementation framework into the Infectious Disease Modeling Reproducibility Checklist (IDMRC) and validated the checklist through an iterative process by evaluating random samples of infectious disease modeling studies. In addition to our framework and the IDMRC, we evaluated several workflow tools, for representing, evaluating, and reproducing models which may lead to useful insights for improving the coordination of modeling resources. We tested the feasibility of reproducing a COVID-19 model using the Open Curation for Computer Architecture Modeling (Occam), an open-source workflow platform that encapsulates and preserves the complete experimental workflow of a modeling study.
For years, attempts have been made to develop a comprehensive tool that can be adopted by researchers, journal editors, and scientific organizations with minimal success in preventing irreproducible models from being published. Our implementation framework and the IDMRC are the first reproducibility tools that can be used by researchers to assess infectious disease computational modeling studies starting from the description of the model and ending with the obtainment of the results. By easily comparing models and their output, researchers will be able to efficiently identify the best models to inform life-saving interventions.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
|
| Date: |
9 May 2023 |
| Date Type: |
Publication |
| Defense Date: |
6 April 2023 |
| Approval Date: |
9 May 2023 |
| Submission Date: |
24 April 2023 |
| Access Restriction: |
1 year -- Restrict access to University of Pittsburgh for a period of 1 year. |
| Number of Pages: |
161 |
| 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: |
reproducibility, infectious disease, covid-19, modeling, computational, epidemiology, checklist |
| Date Deposited: |
10 May 2023 02:04 |
| Last Modified: |
10 May 2023 02:04 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/44688 |
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