A Parallel Sliding Region Algorithm to Make Agent-Based Modeling Possible for a Large-Scale Simulation: Modeling Hepatitis C Epidemics in Canada

• Published in: IEEE journal of biomedical and health informatics Vol. 20; no. 6; pp. 1538 - 1544
• Main Authors: Wong, William W. L., Feng, Zeny Z., Thein, Hla-Hla
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Agent-based model; Agent-based modeling; Agent-based models; Algorithms; Biological system modeling; Canada - epidemiology; Computational efficiency; Computational modeling; Computer applications; Computer Simulation; Computing costs; Computing time; Data models; Diseases; Epidemics; Epidemics - statistics & numerical data; Epidemiology; Hepatitis; Hepatitis C; Hepatitis C - epidemiology; Hepatitis C - transmission; Heterosexuality; Homosexuality; Humans; Infectious diseases; Informatics; Male; Modelling; Models, Biological; Parallel processing; Simulation models; Statistics; Substance Abuse, Intravenous; Canada; Saskatchewan Canada
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2015.2471804

Agent-based models (ABMs) are computer simulation models that define interactions among agents and simulate emergent behaviors that arise from the ensemble of local decisions. ABMs have been increasingly used to examine trends in infectious disease epidemiology. However, the main limitation of ABMs is the high computational cost for a large-scale simulation. To improve the computational efficiency for large-scale ABM simulations, we built a parallelizable sliding region algorithm (SRA) for ABM and compared it to a nonparallelizable ABM. We developed a complex agent network and performed two simulations to model hepatitis C epidemics based on the real demographic data from Saskatchewan, Canada. The first simulation used the SRA that processed on each postal code subregion subsequently. The second simulation processed the entire population simultaneously. It was concluded that the parallelizable SRA showed computational time saving with comparable results in a province-wide simulation. Using the same method, SRA can be generalized for performing a country-wide simulation. Thus, this parallel algorithm enables the possibility of using ABM for large-scale simulation with limited computational resources.