Author(s)

Si-Yuan Liu, Chao Liu, Yu Liu, Yi-Ming Luo, Gao-Jin Wen, Jian-Ping Fan

Hong Kong University of Science and Technology, Hong Kong, China.
Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
Royal Institute of Technology, Stockholm, Sweden.
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.

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.

EEA, ODE, HIV/AIDS, Spread Estimating, Countermeasure Evaluation, High Performance Computing

Liu, 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.