Title |
FRED (A Framework for Reconstructing Epidemic Dynamics): an open-source software system for modeling infectious diseases and control strategies using census-based populations
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Published in |
BMC Public Health, October 2013
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DOI | 10.1186/1471-2458-13-940 |
Pubmed ID | |
Authors |
John J Grefenstette, Shawn T Brown, Roni Rosenfeld, Jay DePasse, Nathan TB Stone, Phillip C Cooley, William D Wheaton, Alona Fyshe, David D Galloway, Anuroop Sriram, Hasan Guclu, Thomas Abraham, Donald S Burke |
Abstract |
Mathematical and computational models provide valuable tools that help public health planners to evaluate competing health interventions, especially for novel circumstances that cannot be examined through observational or controlled studies, such as pandemic influenza. The spread of diseases like influenza depends on the mixing patterns within the population, and these mixing patterns depend in part on local factors including the spatial distribution and age structure of the population, the distribution of size and composition of households, employment status and commuting patterns of adults, and the size and age structure of schools. Finally, public health planners must take into account the health behavior patterns of the population, patterns that often vary according to socioeconomic factors such as race, household income, and education levels. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Turkey | 5 | 36% |
United States | 3 | 21% |
Mexico | 1 | 7% |
Italy | 1 | 7% |
Unknown | 4 | 29% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 9 | 64% |
Scientists | 4 | 29% |
Practitioners (doctors, other healthcare professionals) | 1 | 7% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 9 | 4% |
Israel | 1 | <1% |
United Kingdom | 1 | <1% |
Belgium | 1 | <1% |
Canada | 1 | <1% |
China | 1 | <1% |
Denmark | 1 | <1% |
Unknown | 214 | 93% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 47 | 21% |
Student > Ph. D. Student | 31 | 14% |
Student > Master | 25 | 11% |
Professor > Associate Professor | 16 | 7% |
Professor | 16 | 7% |
Other | 53 | 23% |
Unknown | 41 | 18% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 33 | 14% |
Medicine and Dentistry | 29 | 13% |
Social Sciences | 15 | 7% |
Agricultural and Biological Sciences | 13 | 6% |
Engineering | 11 | 5% |
Other | 68 | 30% |
Unknown | 60 | 26% |