Title |
Toward Standardizing a Lexicon of Infectious Disease Modeling Terms
|
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Published in |
Frontiers in Public Health, September 2016
|
DOI | 10.3389/fpubh.2016.00213 |
Pubmed ID | |
Authors |
Rachael Milwid, Andreea Steriu, Julien Arino, Jane Heffernan, Ayaz Hyder, Dena Schanzer, Emma Gardner, Margaret Haworth-Brockman, Harpa Isfeld-Kiely, Joanne M. Langley, Seyed M. Moghadas |
Abstract |
Disease modeling is increasingly being used to evaluate the effect of health intervention strategies, particularly for infectious diseases. However, the utility and application of such models are hampered by the inconsistent use of infectious disease modeling terms between and within disciplines. We sought to standardize the lexicon of infectious disease modeling terms and develop a glossary of terms commonly used in describing models' assumptions, parameters, variables, and outcomes. We combined a comprehensive literature review of relevant terms with an online forum discussion in a virtual community of practice, mod4PH (Modeling for Public Health). Using a convergent discussion process and consensus amongst the members of mod4PH, a glossary of terms was developed as an online resource. We anticipate that the glossary will improve inter- and intradisciplinary communication and will result in a greater uptake and understanding of disease modeling outcomes in heath policy decision-making. We highlight the role of the mod4PH community of practice and the methodologies used in this endeavor to link theory, policy, and practice in the public health domain. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United Kingdom | 1 | 33% |
Switzerland | 1 | 33% |
Unknown | 1 | 33% |
Demographic breakdown
Type | Count | As % |
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Practitioners (doctors, other healthcare professionals) | 2 | 67% |
Members of the public | 1 | 33% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United States | 1 | 2% |
Switzerland | 1 | 2% |
Brazil | 1 | 2% |
Unknown | 49 | 94% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 11 | 21% |
Student > Ph. D. Student | 10 | 19% |
Student > Master | 7 | 13% |
Student > Bachelor | 4 | 8% |
Other | 4 | 8% |
Other | 7 | 13% |
Unknown | 9 | 17% |
Readers by discipline | Count | As % |
---|---|---|
Mathematics | 9 | 17% |
Medicine and Dentistry | 6 | 12% |
Nursing and Health Professions | 4 | 8% |
Veterinary Science and Veterinary Medicine | 4 | 8% |
Agricultural and Biological Sciences | 4 | 8% |
Other | 14 | 27% |
Unknown | 11 | 21% |