Optimizing agent-based transmission models for infectious diseases

BMC Bioinformatics

Methodology Article

Niel Hens¹  Lander Willem²  Philippe Beutels³  Engelbert Tijskens⁴  Jan Broeckhove⁵  Sean Stijven⁶ 

[1] Centre for Health Economics Research & Modeling of Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium;Interuniversity Institute for Biostatistics and statistical Bioinformatics, Hasselt University, Hasselt, Belgium;Centre for Health Economics Research & Modeling of Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium;Modeling of Systems And Internet Communication, Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium;Interuniversity Institute for Biostatistics and statistical Bioinformatics, Hasselt University, Hasselt, Belgium;Centre for Health Economics Research & Modeling of Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium;School of Public Health and Community Medicine, The University of New South Wales, Sydney, Australia;HPC core facility CalcUA, Computational Mathematics, University of Antwerp, Antwerp, Belgium;Modeling of Systems And Internet Communication, Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium;Modeling of Systems And Internet Communication, Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium;Department of Information Technology, Ghent University–iMinds, Ghent, Belgium;

关键词: Mathematical epidemiology;    Agent-based model;    Optimization;    Performance;   

DOI  :  10.1186/s12859-015-0612-2

 received in 2014-11-21, accepted in 2015-05-11,  发布年份 2015

来源: Springer

【 摘 要 】

BackgroundInfectious 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.ResultsWe 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.ConclusionsInvestment 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.

【 授权许可】

CC BY   
© Willem et al.; licensee BioMed Central. 2015