Statistical data analysis of the 1995 Ebola outbreak in the Democratic Republic of Congo

被引：42

作者：

Ndanguza D. ^{[1
]}

Tchuenche J.M. ^{[2
]}

Haario H. ^{[3
]}

机构：

[1] Department of Applied Mathematics, Kigali Institute of Science and Technology, Kigali

[2] Mathematics Department, University of Dar es Salaam, Dar es Salaam

[3] Department of Mathematics and Physics, Lappeenranta University of Technology, 53851 Lappeenranta

来源：

Afrika Matematika | 2013年 / 24卷 / 1期

关键词：

Analysis; Ebola; Estimates; Least squares; Posterior distribution; SEIR;

D O I：

10.1007/s13370-011-0039-5

中图分类号：

学科分类号：

摘要：

Ebola is a lethal viral hemorrhagic fever with the potential to cause major epidemics. We analyse the 1995 outbreak in the Democratic Republic of Congo using two sets of data (onset and death data). Numerical simulations showed that the model fits the observed onset Ebola data at 99.95% and the observed death data at 98.6%. Since Bayesian inference cannot be performed analytically for complex models, Markov Chain Monte Carlo algorithm is then used as the second approach to obtain a solution. Results obtained from both approaches are contrasted and compared. © 2011 African Mathematical Union and Springer-Verlag.

引用

页码：55 / 68

页数：13

共 20 条

[1]

Bausch D.G., Sprecher A.G., Jeffs B., Boumandouki P., Treatment of Marburg and Ebola hemorrhagic fevers: A strategy for testing new drugs and vaccines under outbreak conditions, Elsevier ScienceDirect, 78, pp. 150-161, (2008)

[2]

Bettencourt L.M.A., Ribeiro R.M., Real Time Bayesian Estimation of the Epidemic Potential of Emerging Infectious Diseases, 3, 5, (2008)

[3]

Brauer F., Wu J., van den Driessche P., Mathematical Epidemiology, (2008)

[4]

Carr J., Applications Centre Manifold Theory, (1998)

[5]

Chowell G., Hengartner N.W., Castillo-Chavez C., Fenimore P.W., Hyman J.M., The basic reproductive number of Ebola and the effects of public health measures: the case of Congo and Uganda, J. Theor. Biol., 229, pp. 119-126, (2004)

[6]

Demiris N., Bayesian Inference for Stochastic Epidemic Models using Markov Chain Monte Carlo Methods, (2004)

[7]

Guardiola J., Vecchio A., The basic reproduction number for infections dynamics models and the global stability of stationary points, (2003)

[8]

Haario H., Saksman E., Tamminen J., An adaptive Metropolis algorithm, Bernouilli, 7, pp. 223-242, (2001)

[9]

Khan A.S., Tshioko K., Heymann D.L., LeGuenno B., Nabeth P., Kerstiens D.L., Fleerackers Y., Kilmarx P.H., Rodier G.R., Nkulu O., Rollin P.E., Sanchez A., Zaki S.R., Swanepoel R., Tomori O., Nichol S.T., Peters C.J., Muyembe-Tamfum J.J., Ksiazek T.G., The re-emergence of Ebola hemorrhagic fever, Democratic Republic of the Congo, 1995, J. Infect. Dis., 179, pp. 79-86, (1999)

[10]

Kuhn J.H., Filoviruses: A compendium of 40 years of Epidemiologycal, pp. 60-64, (2008)

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