EEA algorithm model in estimating spread and evaluating countermeasures on high performance computing
Affiliation(s)
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China. Royal Institute of Technology, Stockholm, Sweden. Hong Kong University of Science and Technology, Hong Kong, China.
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China. Royal Institute of Technology, Stockholm, Sweden. Hong Kong University of Science and Technology, Hong Kong, China.
ABSTRACT
This work started out with the in-depth feasibil-ity study and limitation analysis on the current disease spread estimating and countermea-sures evaluating models, then we identify that the population variability is a crucial impact which has been always ignored or less empha-sized. Taking HIV/AIDS as the application and validation background, we propose a novel al-gorithm model system, EEA model system, a new way to estimate the spread situation, evaluate different countermeasures and analyze the development of ARV-resistant disease strains. The model is a series of solvable ordi-nary differential equation (ODE) models to es-timate the spread of HIV/AIDS infections, which not only require only one year’s data to deduce the situation in any year, but also apply the piecewise constant method to employ multi- year information at the same time. We simulate the effects of therapy and vaccine, then evaluate the difference between them, and offer the smallest proportion of the vaccination in the population to defeat HIV/AIDS, especially the advantage of using the vaccination while the deficiency of using therapy separately. Then we analyze the development of ARV-resistant dis-ease strains by the piecewise constant method. Last but not least, high performance computing (HPC) platform is applied to simulate the situa-tion with variable large scale areas divided by grids, and especially the acceleration rate will come to around 4 to 5.5.
This work started out with the in-depth feasibil-ity study and limitation analysis on the current disease spread estimating and countermea-sures evaluating models, then we identify that the population variability is a crucial impact which has been always ignored or less empha-sized. Taking HIV/AIDS as the application and validation background, we propose a novel al-gorithm model system, EEA model system, a new way to estimate the spread situation, evaluate different countermeasures and analyze the development of ARV-resistant disease strains. The model is a series of solvable ordi-nary differential equation (ODE) models to es-timate the spread of HIV/AIDS infections, which not only require only one year’s data to deduce the situation in any year, but also apply the piecewise constant method to employ multi- year information at the same time. We simulate the effects of therapy and vaccine, then evaluate the difference between them, and offer the smallest proportion of the vaccination in the population to defeat HIV/AIDS, especially the advantage of using the vaccination while the deficiency of using therapy separately. Then we analyze the development of ARV-resistant dis-ease strains by the piecewise constant method. Last but not least, high performance computing (HPC) platform is applied to simulate the situa-tion with variable large scale areas divided by grids, and especially the acceleration rate will come to around 4 to 5.5.
KEYWORDS
EEA, ODE, HIV/AIDS, Spread Estimating, Countermeasure Evaluation, High Performance Computing
EEA, ODE, HIV/AIDS, Spread Estimating, Countermeasure Evaluation, High Performance Computing
Cite this paper
nullLiu, S. , Liu, C. , Liu, Y. , Luo, Y. , Wen, G. and Fan, J. (2009) EEA algorithm model in estimating spread and evaluating countermeasures on high performance computing. Journal of Biomedical Science and Engineering, 2, 41-50. doi: 10.4236/jbise.2009.21008.
nullLiu, S. , Liu, C. , Liu, Y. , Luo, Y. , Wen, G. and Fan, J. (2009) EEA algorithm model in estimating spread and evaluating countermeasures on high performance computing. Journal of Biomedical Science and Engineering, 2, 41-50. doi: 10.4236/jbise.2009.21008.
References
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[1] B. H. Reinhard; K. S. Bernd; G. Peter; K. Bernhard; S. Shlomo; H. Eilke B 1. (1997) ‘Changing incidence of AIDS-defining ill-nesses in the era of antiretroviral combination therapy’, AIDS. 11(14): 1731-1738, November 15, 1997. [2] B. Rapatski, P. Klepac, S. Dueck, M. X. Liu and L. I. Weiss, (2001) ‘Mathematical epidemiology of HIV/AIDS in Cuba dur-ing the period 1986-2000’, Mathematical Biosciences and En-gineering, Vol. 3, No. 3, pp. 545-556. [3] C. Willard JR. MD, MPH; The American Social Health Asso-ciation Panel (1999) ‘Estimates of the Incidence and Prevalence of Sexually Transmitted Diseases in the United States’, Sexually Transmitted Diseases. Vol .26, No.4, Supplement: S2-S7, April. [4] Lewis. (2001) ‘Three stage AIDS incubation period: a worst case scenario using addict-needle interaction as-sumptions’. Mathematical Biosciences, Vol. 169, No.01, pp. 53-87. [5] F. Berman, G. Fox, T. Hey. (2003) Grid Computing: Making the Global Infrastructure a Reality. John Wiley and Sons. P747-771. [6] G. J. Dore, P. K. Correll, Y. Li, J. M. Kaldor, D. A. Cooper, B. J. Brew. (1999) ‘Changes to AIDS dementia complex in the era of highly active antiretroviral therapy’. PubMed, Jul 9, 13(10), 1249-53. [7] D. J. Gregory, C. K. Patricia, Y. M. Li,; J. M. Kaldor,.; C. A. David, B. J. Bruce (1999) ‘Epidemiology and Social’, AIDS, Vol.13, No.10, pp.1249-1253, July 9. [8] H. L. Wu. (2000) ‘Modeling the HIV epidemic: a state-space approach’. Mathematical and Computer Modeling, Vol. 32, No. 1-2, pp.197-215. [9] Ministry of Health of the People’s Republic of China (2003) ‘Report on the Chinese AIDS Epidemic (Chinese version, De-cember 2003)’, December 1. [10] UNAIDS/WHO. (2003) ‘Overview of making estimates of HIV/AIDS and its impact in countries with low-level or concen-trated epidemics: The Workbook Method’. UNAIDS/WHO, June. [11] UNAIDS. (2004) ‘Report on the global AIDS epidemic (English original, June 2004)’. [12] UNGASS. (2003) ‘Program monitoring data from UNGASS (United Nations General Assembly Special Session) country reports 2003’. [13] UNAIDS. (2000) ‘UNAIDS Epidemiological Fact Sheets by Country’, June. [14] United Nations. (2005) ‘Population Division, Department of Economic and Social Affairs (2005) World Population Prospects: The 2004 Revision’. [15] I. Foster, C. Kesselman. (1999) The Grid Blueprint for a New Computing Infrastructure [M]. Morgan Kaufmann Publishers, Inc., 6-8. [16] I Foster, C Kesselman, S Tueeke. (2001) The anatomy of the grid: Enabling scalable virtual organizations. International Journal of Supercomputer Applications, 15(3): 200-222. [17] Z. W. Xu, W. Li, L. Zha, et al. (2004) Vega: A Computer Sys-tems Approach to Grid Computing. Journal of Grid Computing. Vol. 2(2): 109-120. [18] Y. L. Gong, F. P. Dong, W. Li, Z. W. Xu. (2003) VEGA Infra-structure for Resource Discovery in Grids. Journal of Computer Science & Technology. Vol. 18, No.4, 413-422. [19] W Allcock, J Bresnahan, J Bester. et a1. (2002) Grid FTP Protocol Specification [Z]. GGF Grid FTP Working Group Document. [20] J Bester, I Foster, C Kessdman et a1. (1999) GASS：A data movement and access service for wide a computing systems [A]. Sixth Workshop on I/O in Parallel and Distributed System[C]. [21] S. Y. Liu, C. Liu, C. Z. Zhao and Yu Liu. (2007) Mathematical Models and Optimization Discussions on EA System on AIDS/ HIV Spread Estimating and Countermeasures Evaluating. IEEE 7th International Symposium on Bioinformatics and Bioengi-neering. [22] D. Patterson, A. Brown el a1. (2002) Recover oriented comput-ing (ROC): Motivation, definition, techniques, and ease studies. UC Berkeley, Tech Rep: UCB/CSD-02-1175. [23] L. Chen, C.L. Wang, F. C. M. Lau, and R. K. K. Ma, (2002) “A Grid Middleware for Distributed Java Computing with MPI Binding and Process Migration Supports,” International Work-shop on Grid and Cooperative Computing (GCC-2002), De-cember 26-28, Hainan, China, pp. 640-652. [24] M. J. M. Ma, C. L. Wang, F. C. M. Lau. (2000) “JESSICA: Java-Enabled Single-System-Image Computing Architecture,” Journal of Parallel and Distributed Computing, Vol. 60, No. 10, pp. 1194-1222. [25] H. Stockinger, F. Donno, E. Laure, S. Muzaffar, P. Kunszt, G. Andronico, P. Millar. (2003) Grid Data Management in Action: Experience in Running and Supporting Data Management Ser-vices in the EU Data Grid Project. Computer Science. [26] K. Gor, D. Ra, S. Ali, L. Alves, N. Arurkar, I. Gupta, A. Chak-rabarti, A. Sharma, S. Sengupta. (2005) Scalable enterprise level workflow and infrastructure management in a grid computing environment. Cluster Computing and the Grid. [27] K. Yang, A. Galis, C. Todd, (2002) Policy-based active Grid management architecture. Networks. ICON 2002. [28] A. Boukerche, A. Roy. (2002) Dynamic Grid-Based Approach to Data Distribution Management. Journal of Parallel and Dis-tributed Computing．Volume 62, Issue 3, Pages 366-392. [29] A. -M. Vandamme, K. Van Laethem, and E. De Clercq, (1999) “Managing Resistance to Anti-HIV Drugs: An Important Con-sideration for Effective Disease Management,” Drugs, Vol. 57, >No. 3, 337-361. [30] H. Walter et al., (1999) “Rapid, Phenotypic HIV-1 Drug Sensi-tivity Assay for Protease and Reverse Transcriptase Inhibitors,” J. Clinical Virology, Vol. 13, Nos. 1-2, 71-80. [31] D. Wang, S. Bloor, and B. A. Larder, (2000) “The Application of Neural Networks in Predicting Phenotypic Resistance from Genotypes for HIV-1 Protease Inhibitors,” Antiviral Therapy, Vol. 5, supplement 3, 51-52. [32] J. R. Quinlan, (1993) C4.5: Programs for Machine Learning, Morgan Kaufmann, San Francisco. [33] C. J. C. Burges, (1998) “A Tutorial on Support Vector Machines for Pattern Recognition,” Data Mining and Knowledge Discov-ery, Vol. 2, No. 2, 121-167. [34] T. Joachims, (1999) "Making Large-Scale Support Vector Ma-chine Learning Practical,?Advances in Kernel Methods: Sup-port Vector Learning, B. Scholkopf, C. Burges, and A. Smola, eds., MIT Press, Cambridge, Mass., 169-184. [35] J. Selbig, T. Mevissen, and T. Lengauer, (1999) “Decision Tree-Based Formation of Consensus Protein Secondary Structure Prediction,” Bioinformatics, Vol. 15, No. 12, 1039-1046. [36] X. Shen, W. K. Liao, A. Choudhary, G. Memik, M. Kandemir, (2003) “A high-performance application data environment for large-scale scientific computations,” IEEE Transactions on Par-allel and Distributed Systems, Volume: 14 Issue: 12 Date: Page(s): 1262-1274.