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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Disease surveillance using a hidden Markov model
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, August 2009
|
| DOI | 10.1186/1472-6947-9-39 |
| Pubmed ID | |
| Authors |
Rochelle E Watkins, Serryn Eagleson, Bert Veenendaal, Graeme Wright, Aileen J Plant |
| Abstract |
Routine surveillance of disease notification data can enable the early detection of localised disease outbreaks. Although hidden Markov models (HMMs) have been recognised as an appropriate method to model disease surveillance data, they have been rarely applied in public health practice. We aimed to develop and evaluate a simple flexible HMM for disease surveillance which is suitable for use with sparse small area count data and requires little baseline data. |
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.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 108 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 3% |
| Nigeria | 2 | 2% |
| Switzerland | 1 | <1% |
| United Kingdom | 1 | <1% |
| Canada | 1 | <1% |
| Denmark | 1 | <1% |
| Australia | 1 | <1% |
| Spain | 1 | <1% |
| Brazil | 1 | <1% |
| Other | 2 | 2% |
| Unknown | 94 | 87% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 25 | 23% |
| Researcher | 17 | 16% |
| Student > Doctoral Student | 10 | 9% |
| Student > Bachelor | 9 | 8% |
| Student > Postgraduate | 7 | 6% |
| Other | 24 | 22% |
| Unknown | 16 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 18 | 17% |
| Computer Science | 17 | 16% |
| Mathematics | 15 | 14% |
| Medicine and Dentistry | 12 | 11% |
| Business, Management and Accounting | 4 | 4% |
| Other | 22 | 20% |
| Unknown | 20 | 19% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 23 March 2013.
All research outputs
#18,332,122
of 22,701,287 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,563
of 1,980 outputs
Outputs of similar age
#102,361
of 111,405 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#4
of 5 outputs
Altmetric has tracked 22,701,287 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,980 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 9th percentile – i.e., 9% 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 111,405 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 3rd percentile – i.e., 3% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one.