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Optimizing agent-based transmission models for infectious diseases

Overview of attention for article published in BMC Bioinformatics, June 2015
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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 (79th percentile)

Mentioned by

twitter
10 tweeters

Citations

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10 Dimensions

Readers on

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49 Mendeley
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Title
Optimizing agent-based transmission models for infectious diseases
Published in
BMC Bioinformatics, June 2015
DOI 10.1186/s12859-015-0612-2
Pubmed ID
Authors

Lander Willem, Sean Stijven, Engelbert Tijskens, Philippe Beutels, Niel Hens, Jan Broeckhove

Abstract

Infectious disease modeling and computational power have evolved such that large-scale agent-based models (ABMs) have become feasible. However, the increasing hardware complexity requires adapted software designs to achieve the full potential of current high-performance workstations. We have found large performance differences with a discrete-time ABM for close-contact disease transmission due to data locality. Sorting the population according to the social contact clusters reduced simulation time by a factor of two. Data locality and model performance can also be improved by storing person attributes separately instead of using person objects. Next, decreasing the number of operations by sorting people by health status before processing disease transmission has also a large impact on model performance. Depending of the clinical attack rate, target population and computer hardware, the introduction of the sort phase decreased the run time from 26 % up to more than 70 %. We have investigated the application of parallel programming techniques and found that the speedup is significant but it drops quickly with the number of cores. We observed that the effect of scheduling and workload chunk size is model specific and can make a large difference. Investment in performance optimization of ABM simulator code can lead to significant run time reductions. The key steps are straightforward: the data structure for the population and sorting people on health status before effecting disease propagation. We believe these conclusions to be valid for a wide range of infectious disease ABMs. We recommend that future studies evaluate the impact of data management, algorithmic procedures and parallelization on model performance.

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 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
Belgium 1 2%
Australia 1 2%
Unknown 46 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 20%
Student > Ph. D. Student 9 18%
Student > Master 7 14%
Professor 4 8%
Professor > Associate Professor 4 8%
Other 9 18%
Unknown 6 12%
Readers by discipline Count As %
Computer Science 8 16%
Medicine and Dentistry 7 14%
Mathematics 6 12%
Agricultural and Biological Sciences 5 10%
Engineering 2 4%
Other 13 27%
Unknown 8 16%

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 31 July 2020.
All research outputs
#3,038,594
of 16,110,865 outputs
Outputs from BMC Bioinformatics
#1,291
of 5,837 outputs
Outputs of similar age
#49,560
of 238,086 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 1 outputs
Altmetric has tracked 16,110,865 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,837 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one has done well, scoring higher than 77% 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 238,086 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 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