| Title |
Why We Need Crowdsourced Data in Infectious Disease Surveillance
|
|---|---|
| Published in |
Current Infectious Disease Reports, May 2013
|
| DOI | 10.1007/s11908-013-0341-5 |
| Pubmed ID | |
| Authors |
Rumi Chunara, Mark S. Smolinski, John S. Brownstein |
| Abstract |
In infectious disease surveillance, public health data such as environmental, hospital, or census data have been extensively explored to create robust models of disease dynamics. However, this information is also subject to its own biases, including latency, high cost, contributor biases, and imprecise resolution. Simultaneously, new technologies including Internet and mobile phone based tools, now enable information to be garnered directly from individuals at the point of care. Here, we consider how these crowdsourced data offer the opportunity to fill gaps in and augment current epidemiological models. Challenges and methods for overcoming limitations of the data are also reviewed. As more new information sources become mature, incorporating these novel data into epidemiological frameworks will enable us to learn more about infectious disease dynamics. |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 40% |
| United Kingdom | 1 | 20% |
| Unknown | 2 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 80% |
| Scientists | 1 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Australia | 1 | 2% |
| Unknown | 49 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 11 | 22% |
| Student > Master | 6 | 12% |
| Student > Doctoral Student | 6 | 12% |
| Researcher | 5 | 10% |
| Student > Bachelor | 3 | 6% |
| Other | 11 | 22% |
| Unknown | 8 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 12 | 24% |
| Computer Science | 11 | 22% |
| Social Sciences | 4 | 8% |
| Agricultural and Biological Sciences | 3 | 6% |
| Psychology | 2 | 4% |
| Other | 9 | 18% |
| Unknown | 9 | 18% |
Attention Score in Context
This research output has an Altmetric Attention Score of 13. 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 07 April 2020.
All research outputs
#2,270,997
of 22,715,151 outputs
Outputs from Current Infectious Disease Reports
#42
of 488 outputs
Outputs of similar age
#20,414
of 195,536 outputs
Outputs of similar age from Current Infectious Disease Reports
#1
of 9 outputs
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