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The AFHSC-Division of GEIS Operations Predictive Surveillance Program: a multidisciplinary approach for the early detection and response to disease outbreaks

Overview of attention for article published in BMC Public Health, March 2011
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (64th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (52nd percentile)

Mentioned by

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1 policy source
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1 X user

Citations

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29 Dimensions

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218 Mendeley
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Title
The AFHSC-Division of GEIS Operations Predictive Surveillance Program: a multidisciplinary approach for the early detection and response to disease outbreaks
Published in
BMC Public Health, March 2011
DOI 10.1186/1471-2458-11-s2-s10
Pubmed ID
Authors

Clara J Witt, Allen L Richards, Penny M Masuoka, Desmond H Foley, Anna L Buczak, Lillian A Musila, Jason H Richardson, Michelle G Colacicco-Mayhugh, Leopoldo M Rueda, Terry A Klein, Assaf Anyamba, Jennifer Small, Julie A Pavlin, Mark M Fukuda, Joel Gaydos, Kevin L Russell, the AFHSC-GEIS Predictive Surveillance Writing Group

Abstract

The Armed Forces Health Surveillance Center, Division of Global Emerging Infections Surveillance and Response System Operations (AFHSC-GEIS) initiated a coordinated, multidisciplinary program to link data sets and information derived from eco-climatic remote sensing activities, ecologic niche modeling, arthropod vector, animal disease-host/reservoir, and human disease surveillance for febrile illnesses, into a predictive surveillance program that generates advisories and alerts on emerging infectious disease outbreaks. The program's ultimate goal is pro-active public health practice through pre-event preparedness, prevention and control, and response decision-making and prioritization. This multidisciplinary program is rooted in over 10 years experience in predictive surveillance for Rift Valley fever outbreaks in Eastern Africa. The AFHSC-GEIS Rift Valley fever project is based on the identification and use of disease-emergence critical detection points as reliable signals for increased outbreak risk. The AFHSC-GEIS predictive surveillance program has formalized the Rift Valley fever project into a structured template for extending predictive surveillance capability to other Department of Defense (DoD)-priority vector- and water-borne, and zoonotic diseases and geographic areas. These include leishmaniasis, malaria, and Crimea-Congo and other viral hemorrhagic fevers in Central Asia and Africa, dengue fever in Asia and the Americas, Japanese encephalitis (JE) and chikungunya fever in Asia, and rickettsial and other tick-borne infections in the U.S., Africa and Asia.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 218 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 3%
United Kingdom 4 2%
Australia 2 <1%
Netherlands 1 <1%
Pakistan 1 <1%
Brazil 1 <1%
Sweden 1 <1%
Germany 1 <1%
Peru 1 <1%
Other 3 1%
Unknown 197 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 41 19%
Student > Master 39 18%
Student > Ph. D. Student 26 12%
Student > Doctoral Student 17 8%
Student > Bachelor 16 7%
Other 49 22%
Unknown 30 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 50 23%
Medicine and Dentistry 41 19%
Veterinary Science and Veterinary Medicine 14 6%
Social Sciences 11 5%
Biochemistry, Genetics and Molecular Biology 9 4%
Other 53 24%
Unknown 40 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 April 2014.
All research outputs
#7,959,659
of 25,371,288 outputs
Outputs from BMC Public Health
#8,820
of 17,508 outputs
Outputs of similar age
#41,435
of 120,005 outputs
Outputs of similar age from BMC Public Health
#65
of 146 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 17,508 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.4. This one is in the 48th percentile – i.e., 48% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 120,005 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 64% of its contemporaries.
We're also able to compare this research output to 146 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.