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Mathematical models used to inform study design or surveillance systems in infectious diseases: a systematic review

Overview of attention for article published in BMC Infectious Diseases, December 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

Mentioned by

twitter
10 tweeters

Citations

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6 Dimensions

Readers on

mendeley
44 Mendeley
Title
Mathematical models used to inform study design or surveillance systems in infectious diseases: a systematic review
Published in
BMC Infectious Diseases, December 2017
DOI 10.1186/s12879-017-2874-y
Pubmed ID
Authors

Sereina A. Herzog, Stéphanie Blaizot, Niel Hens

Abstract

Mathematical models offer the possibility to investigate the infectious disease dynamics over time and may help in informing design of studies. A systematic review was performed in order to determine to what extent mathematical models have been incorporated into the process of planning studies and hence inform study design for infectious diseases transmitted between humans and/or animals. We searched Ovid Medline and two trial registry platforms (Cochrane, WHO) using search terms related to infection, mathematical model, and study design from the earliest dates to October 2016. Eligible publications and registered trials included mathematical models (compartmental, individual-based, or Markov) which were described and used to inform the design of infectious disease studies. We extracted information about the investigated infection, population, model characteristics, and study design. We identified 28 unique publications but no registered trials. Focusing on compartmental and individual-based models we found 12 observational/surveillance studies and 11 clinical trials. Infections studied were equally animal and human infectious diseases for the observational/surveillance studies, while all but one between humans for clinical trials. The mathematical models were used to inform, amongst other things, the required sample size (n = 16), the statistical power (n = 9), the frequency at which samples should be taken (n = 6), and from whom (n = 6). Despite the fact that mathematical models have been advocated to be used at the planning stage of studies or surveillance systems, they are used scarcely. With only one exception, the publications described theoretical studies, hence, not being utilised in real studies.

Twitter Demographics

The data shown below were collected from the profiles of 10 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 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 44 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 11 25%
Student > Ph. D. Student 8 18%
Researcher 7 16%
Unspecified 6 14%
Student > Doctoral Student 5 11%
Other 7 16%
Readers by discipline Count As %
Unspecified 10 23%
Agricultural and Biological Sciences 8 18%
Medicine and Dentistry 7 16%
Mathematics 4 9%
Environmental Science 3 7%
Other 12 27%

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 26 December 2017.
All research outputs
#2,011,340
of 13,341,965 outputs
Outputs from BMC Infectious Diseases
#648
of 4,970 outputs
Outputs of similar age
#77,765
of 385,723 outputs
Outputs of similar age from BMC Infectious Diseases
#96
of 652 outputs
Altmetric has tracked 13,341,965 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,970 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done well, scoring higher than 86% of its peers.
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 385,723 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 79% of its contemporaries.
We're also able to compare this research output to 652 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.