Title |
Modeling the transmission of community-associated methicillin-resistant Staphylococcus aureus: a dynamic agent-based simulation
|
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Published in |
Journal of Translational Medicine, May 2014
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DOI | 10.1186/1479-5876-12-124 |
Pubmed ID | |
Authors |
Charles M Macal, Michael J North, Nicholson Collier, Vanja M Dukic, Duane T Wegener, Michael Z David, Robert S Daum, Philip Schumm, James A Evans, Jocelyn R Wilder, Loren G Miller, Samantha J Eells, Diane S Lauderdale |
Abstract |
Methicillin-resistant Staphylococcus aureus (MRSA) has been a deadly pathogen in healthcare settings since the 1960s, but MRSA epidemiology changed since 1990 with new genetically distinct strain types circulating among previously healthy people outside healthcare settings. Community-associated (CA) MRSA strains primarily cause skin and soft tissue infections, but may also cause life-threatening invasive infections. First seen in Australia and the U.S., it is a growing problem around the world. The U.S. has had the most widespread CA-MRSA epidemic, with strain type USA300 causing the great majority of infections. Individuals with either asymptomatic colonization or infection may transmit CA-MRSA to others, largely by skin-to-skin contact. Control measures have focused on hospital transmission. Limited public health education has focused on care for skin infections. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 1 | <1% |
United States | 1 | <1% |
Australia | 1 | <1% |
Brazil | 1 | <1% |
Unknown | 112 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 26 | 22% |
Researcher | 19 | 16% |
Student > Bachelor | 14 | 12% |
Professor | 9 | 8% |
Other | 9 | 8% |
Other | 24 | 21% |
Unknown | 15 | 13% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 22 | 19% |
Agricultural and Biological Sciences | 16 | 14% |
Engineering | 9 | 8% |
Social Sciences | 7 | 6% |
Computer Science | 7 | 6% |
Other | 36 | 31% |
Unknown | 19 | 16% |