↓ Skip to main content

Spatial model for risk prediction and sub-national prioritization to aid poliovirus eradication in Pakistan

Overview of attention for article published in BMC Medicine, October 2017
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (73rd percentile)

Mentioned by

twitter
6 tweeters
wikipedia
1 Wikipedia page

Citations

dimensions_citation
8 Dimensions

Readers on

mendeley
31 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Spatial model for risk prediction and sub-national prioritization to aid poliovirus eradication in Pakistan
Published in
BMC Medicine, October 2017
DOI 10.1186/s12916-017-0941-2
Pubmed ID
Authors

Laina D. Mercer, Rana M. Safdar, Jamal Ahmed, Abdirahman Mahamud, M. Muzaffar Khan, Sue Gerber, Aiden O’Leary, Mike Ryan, Frank Salet, Steve J. Kroiss, Hil Lyons, Alexander Upfill-Brown, Guillaume Chabot-Couture

Abstract

Pakistan is one of only three countries where poliovirus circulation remains endemic. For the Pakistan Polio Eradication Program, identifying high risk districts is essential to target interventions and allocate limited resources. Using a hierarchical Bayesian framework we developed a spatial Poisson hurdle model to jointly model the probability of one or more paralytic polio cases, and the number of cases that would be detected in the event of an outbreak. Rates of underimmunization, routine immunization, and population immunity, as well as seasonality and a history of cases were used to project future risk of cases. The expected number of cases in each district in a 6-month period was predicted using indicators from the previous 6-months and the estimated coefficients from the model. The model achieves an average of 90% predictive accuracy as measured by area under the receiver operating characteristic (ROC) curve, for the past 3 years of cases. The risk of poliovirus has decreased dramatically in many of the key reservoir areas in Pakistan. The results of this model have been used to prioritize sub-national areas in Pakistan to receive additional immunization activities, additional monitoring, or other special interventions.

Twitter Demographics

The data shown below were collected from the profiles of 6 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 31 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 29%
Student > Master 6 19%
Student > Ph. D. Student 5 16%
Student > Doctoral Student 2 6%
Student > Postgraduate 2 6%
Other 3 10%
Unknown 4 13%
Readers by discipline Count As %
Medicine and Dentistry 6 19%
Agricultural and Biological Sciences 4 13%
Mathematics 3 10%
Psychology 3 10%
Environmental Science 2 6%
Other 7 23%
Unknown 6 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 28 March 2020.
All research outputs
#3,777,042
of 15,916,297 outputs
Outputs from BMC Medicine
#1,661
of 2,485 outputs
Outputs of similar age
#74,395
of 281,642 outputs
Outputs of similar age from BMC Medicine
#1
of 2 outputs
Altmetric has tracked 15,916,297 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,485 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 36.9. This one is in the 33rd percentile – i.e., 33% 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 281,642 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 73% of its contemporaries.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them