To Alleviate the Ebola Virus Epidemic Diffusion
Abstract
The emergence of new drug can stop Ebola and cure patients whose disease is not advanced. It optimizes the eradication of Ebola, or at least its current strain. For the sake of dealing with this problem, there are three models being developed.
Firstly, this paper establishes the model 1 on the basis of the classical model of SIR and diffusion characteristic of Ebola virus. It verifies the reduction of the spread of the virus, the improvement of the patient’s cure rate and the effectiveness of three preventive measures which are significant in the formation of herd immunity. At the same time, we use linear programming to control the cost of drug delivery.
Model 2, namely, the model of SIR with pulse vaccination, provides a pulse vaccination therapy on the basis of model 1. Model 2 considers many factors comprehensively, such as the cycle of inoculation, vaccination rate, the birth rate, death rate and so on. We use differential equation models to get the critical condition of the number of susceptible people, vaccination rate, and the development of predicated estimate with the change of time.
Next, based on the model 2, we establish model 3 which not only considers many factors comprehensively, such as the amount of supply, the location of supply and so on, but also introduce 0-1 variable to combine the general linear programming with another linear programming which is not fixed but multi objectives so that we get the drug delivery network. Meanwhile, this paper obtains the best drug delivery program which has to spend the minimum cost on the condition of effectively controlling the epidemic. Also the result can alleviate serious situation of the Ebola virus epidemic diffusion through the drug delivery network.
This paper puts forward the improvement of the model by using the Self-Organizing Map neural network and cluster analysis to get the urgent degree of different epidemic areas and divide these areas into different priorities. We get a goal programming model based on different priority. Furthermore, we use the drug delivery model and Lingo to get a more reliable drug delivery program on the basis of the objective function of priority.
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Gao, L. Q., & Hethcote, H. W. (1992). Disease transmission models with density-dependent demographics. Math. Bi0l, 30, 717-731.
Shulgin, B., Stone, L., & Agur, Z. (1998). Pulse vaccination strategy in the SIR epidemic Mode. Math.Bi, 60(01),, 1123-1148.
Agur, Z., Shulgin, B., & Stone, L. (2000). Theoretical examination of the pulse vaccination policy in the SIR epidemic model. Mathematics Computation Modeling, 31, 207-215.
Jin, Z. (2001). Ecological and epidemiological model in the impulse action. Xi’an, China: Xi’an Jiaotong University.
Song, M. G. (2005). Emergency alleviate distribution system model building. Taiwan: Taiwan’s National Chiao Tung University.
Ji, Lei, C. H. (2005). Emergency management (pp.100-101). Beijing, China: Higher Education Press.
WHO. (XXXX). | Ebola virus disease. Retrieved from http://www.who.int/mediacentre/factsheets/fs103/en/
How big data to Help Fight Against Ebola. (2014, October 20 23:05). Chinese Statistics Network Published.
Xu, J. J., & Wang, H. Y. (2010). Dynamic vaccine delivery model based on diffusion and clustering analysis of infectious disease law. Journal of Southeast University (English Edition), (01), 132-136.
Gu, Y. (2010). Based on the needs of emergency supplies of infectious diseases prevention and control mechanism. Wuhan University of Technology (Transportation Science & Engineering Edition), (04), 707-711 .
Cheng, Y., Liu, J., Li, Y., Liu, D., Ren, X., Gao, F., Yu, H. J. (2014). Ebola virus disease : pathogenesis etiology , treatment, research progress, and vaccines. Chinese Science Bulletin, 30, 2889-2899.
Ren, J. C., & Duan, G. C. (2014). Epidemiological characteristics of Ebola virus disease. Xinxiang Medical College, 11, 872-876 + 882 .
XXXX. (XXXX). http://baike.baidu.com/link?url=TXD47mWJqS39q3iBk2WS1Ln1T8x4pI45LFoavxAii_75FgzfnGT9OfXOAaCPJwrVwv8LoOkAluXNfWSEh6FMKUSNzChl_BpZJ68yjpN0xdL6OHrC3u8vN09TlS-Ln7AnPJ46W_YQYuKmYc9peo3nu8GpDwwABbA3noaj5BFI2jj9nVwOZ6FJEmMuRgMKfXKX
Hale, T., & Moberg, C. R. (2005). Improving supply chain disaster preparedness: A decision Process for secure site location. International Journal of Physical Distribution & Logistics Management, 35(3/4), 1 95-207
DOI: http://dx.doi.org/10.3968/6871
DOI (PDF): http://dx.doi.org/10.3968/pdf_15
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