Journal
APPLIED SOFT COMPUTING
Volume 80, Issue -, Pages 546-556Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.asoc.2019.04.015
Keywords
HPV transmission dynamics; Computational random network model; Model calibration; Particle swarm optimization; Uncertainty quantification; Agent-based simulation modeling
Categories
Funding
- Spanish Ministerio de Economia y Competitividad [MTM2017-89664-P, TIN2014-54806-R, RTI2018-095180-B-I00]
- EU Structural Funds, Spain
- GLENO project - Fundacion Eugenio Rodriguez Pascual, Spain
- Micro-Stres-MAP-CM [Y2018/NMT-4668]
- GenObIA-CM - Community of Madrid, Spain [S2017/BMD-3773]
Ask authors/readers for more resources
Recently, the transmission dynamics of the Human Papillomavirus (HPV) has been studied. In previous works, we have designed and implemented a computational model (agent-based simulation model) where the contagion of the HPV is described on a network of lifetime sexual partners. The run of a single simulation of this computational model, composed of a network with 500 000 nodes, takes about one hour and a half. In addition to set an adequate model, finding out the model parameters that best fit the proposed model to the available data of prevalence is a crucial goal. Taking into account that the necessary number of simulations to perform the calibration of the model may be very high, the aforementioned goal may become unaffordable. In this paper, we present a procedure to fit the proposed HPV model to the available data and the design of an asynchronous version of the Particle Swarm Optimization (PSO) algorithm adapted to the distributed computing environment. In the process, the number of particles used in PSO should be set carefully looking for a compromise between quality of the solutions and computation time. Another feature of the procedure presented here is that we want to capture the intrinsic uncertainty in the data (data come from a survey) when calibrating the model. To do so, we also propose the design of an algorithm to select the model parameter sets obtained during the calibration that best capture the data uncertainty. (C) 2019 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available