Title |
An infectious disease model on empirical networks of human contact: bridging the gap between dynamic network data and contact matrices
|
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Published in |
BMC Infectious Diseases, April 2013
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DOI | 10.1186/1471-2334-13-185 |
Pubmed ID | |
Authors |
Anna Machens, Francesco Gesualdo, Caterina Rizzo, Alberto E Tozzi, Alain Barrat, Ciro Cattuto |
Abstract |
The integration of empirical data in computational frameworks designed to model the spread of infectious diseases poses a number of challenges that are becoming more pressing with the increasing availability of high-resolution information on human mobility and contacts. This deluge of data has the potential to revolutionize the computational efforts aimed at simulating scenarios, designing containment strategies, and evaluating outcomes. However, the integration of highly detailed data sources yields models that are less transparent and general in their applicability. Hence, given a specific disease model, it is crucial to assess which representations of the raw data work best to inform the model, striking a balance between simplicity and detail. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Spain | 3 | 16% |
France | 2 | 11% |
Portugal | 1 | 5% |
Finland | 1 | 5% |
United States | 1 | 5% |
Canada | 1 | 5% |
India | 1 | 5% |
Norway | 1 | 5% |
Unknown | 8 | 42% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 12 | 63% |
Scientists | 7 | 37% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 8 | 5% |
France | 2 | 1% |
Switzerland | 2 | 1% |
Italy | 2 | 1% |
Kenya | 1 | <1% |
Malaysia | 1 | <1% |
South Africa | 1 | <1% |
Australia | 1 | <1% |
Unknown | 141 | 89% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 39 | 25% |
Researcher | 32 | 20% |
Student > Master | 22 | 14% |
Student > Bachelor | 11 | 7% |
Student > Doctoral Student | 8 | 5% |
Other | 32 | 20% |
Unknown | 15 | 9% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 20 | 13% |
Physics and Astronomy | 19 | 12% |
Mathematics | 19 | 12% |
Medicine and Dentistry | 18 | 11% |
Computer Science | 16 | 10% |
Other | 43 | 27% |
Unknown | 24 | 15% |