Title
Optimizing agent-based transmission models for infectious diseasesOptimizing agent-based transmission models for infectious diseases
Author
Faculty/Department
Faculty of Sciences. Mathematics and Computer Science
Faculty of Medicine and Health Sciences
Research group
Computational Mathematics
Modeling Of Systems and Internet Communication (MOSAIC)
Vaccine & Infectious Disease Institute (VAXINFECTIO)
Publication type
article
Publication
London,
Subject
Mathematics
Chemistry
Biology
Human medicine
Engineering sciences. Technology
Computer. Automation
Source (journal)
BMC bioinformatics. - London
Volume/pages
16(2015), 10 p.
ISSN
1471-2105
1471-2105
Article Reference
183
Carrier
E-only publicatie
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
Background 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. Results 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. Conclusions 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.
Full text (open access)
https://repository.uantwerpen.be/docman/irua/cdcc77/6c3409b5.pdf
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