Calibrating a large network model describing the transmission dynamics of the human papillomavirus using a particle swarm optimization algorithm in a distributed computing environment

Working in large networks applied to epidemiological-type models has led us to design a simple but effective computed distributed environment to perform a large amount of model simulations in a reasonable time in order to study the behavior of these models and to calibrate them. Finding the model pa...

Full description

Saved in:
Bibliographic Details
Published in:The international journal of high performance computing applications 2018-09, Vol.32 (5), p.721-728
Main Authors: Acedo, Luis, Burgos, Clara, Hidalgo, José-Ignacio, Sánchez-Alonso, Victor, Villanueva, Rafael-Jacinto, Villanueva-Oller, Javier
Format: Article
Language:English
Subjects:
Algorithms
Calibration
Computation
Computer networks
Computer simulation
Distributed processing
Epidemiology
Human papillomavirus
Object oriented programming
Optimization algorithms
Particle swarm optimization
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Working in large networks applied to epidemiological-type models has led us to design a simple but effective computed distributed environment to perform a large amount of model simulations in a reasonable time in order to study the behavior of these models and to calibrate them. Finding the model parameters that best fit the available data in the designed distributed computing environment becomes a challenge and it is necessary to implement reliable algorithms for model calibration. In this article, we have adapted the random particle swarm optimization algorithm to our distributed computing environment to be applied to the calibration of a papillomavirus transmission dynamics model on a lifetime sexual partners network. And we have obtained a good fitting saving time and calculations compared with the exhaustive searching strategy we have been using so far.
ISSN:1094-3420
1741-2846
DOI:10.1177/1094342017697862