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Estimating the prevalence of infectious diseases from under-reported age-dependent compulsorily notification databases

Overview of attention for article published in Theoretical Biology and Medical Modelling, December 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#49 of 272)
  • Good Attention Score compared to outputs of the same age (75th percentile)

Mentioned by

blogs
1 blog
twitter
1 tweeter

Citations

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7 Dimensions

Readers on

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28 Mendeley
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Title
Estimating the prevalence of infectious diseases from under-reported age-dependent compulsorily notification databases
Published in
Theoretical Biology and Medical Modelling, December 2017
DOI 10.1186/s12976-017-0069-2
Pubmed ID
Authors

Marcos Amaku, Marcelo Nascimento Burattini, Eleazar Chaib, Francisco Antonio Bezerra Coutinho, David Greenhalgh, Luis Fernandez Lopez, Eduardo Massad

Abstract

National or local laws, norms or regulations (sometimes and in some countries) require medical providers to report notifiable diseases to public health authorities. Reporting, however, is almost always incomplete. This is due to a variety of reasons, ranging from not recognizing the diseased to failures in the technical or administrative steps leading to the final official register in the disease notification system. The reported fraction varies from 9 to 99% and is strongly associated with the disease being reported. In this paper we propose a method to approximately estimate the full prevalence (and any other variable or parameter related to transmission intensity) of infectious diseases. The model assumes incomplete notification of incidence and allows the estimation of the non-notified number of infections and it is illustrated by the case of hepatitis C in Brazil. The method has the advantage that it can be corrected iteratively by comparing its findings with empirical results. The application of the model for the case of hepatitis C in Brazil resulted in a prevalence of notified cases that varied between 163,902 and 169,382 cases; a prevalence of non-notified cases that varied between 1,433,638 and 1,446,771; and a total prevalence of infections that varied between 1,597,540 and 1,616,153 cases. We conclude that the model proposed can be useful for estimation of the actual magnitude of endemic states of infectious diseases, particularly for those where the number of notified cases is only the tip of the iceberg. In addition, the method can be applied to other situations, such as the well-known underreported incidence of criminality (for example rape), among others.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 28 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 6 21%
Student > Ph. D. Student 3 11%
Professor 3 11%
Student > Bachelor 3 11%
Professor > Associate Professor 2 7%
Other 6 21%
Unknown 5 18%
Readers by discipline Count As %
Medicine and Dentistry 5 18%
Biochemistry, Genetics and Molecular Biology 2 7%
Computer Science 2 7%
Psychology 2 7%
Mathematics 2 7%
Other 7 25%
Unknown 8 29%

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 12 May 2020.
All research outputs
#3,103,849
of 17,704,521 outputs
Outputs from Theoretical Biology and Medical Modelling
#49
of 272 outputs
Outputs of similar age
#69,542
of 287,033 outputs
Outputs of similar age from Theoretical Biology and Medical Modelling
#1
of 1 outputs
Altmetric has tracked 17,704,521 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 272 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.0. This one has done well, scoring higher than 81% 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 287,033 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 75% of its contemporaries.
We're also able to compare this research output to 1 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