期刊论文详细信息
Infectious Diseases of Poverty
Estimating the basic reproduction number for single-strain dengue fever epidemics
Mudassar Imran2  Muhammad Hassan1  Adnan Khan2 
[1] Department of Mathematics, Swiss Federal Institute of Technology (ETH), Zurich, Rämistrasse 101, 8092 Zurich, Switzerland;Department of Mathematics, Lahore University of Management Sciences, DHA, Lahore, Pakistan
关键词: Markov chain Monte Carlo;    Stochastic model;    Statistical inference;    Dengue fever;    Epidemiology;   
Others  :  801087
DOI  :  10.1186/2049-9957-3-12
 received in 2013-09-26, accepted in 2014-03-06,  发布年份 2014
PDF
【 摘 要 】

Background

Dengue, an infectious tropical disease, has recently emerged as one of the most important mosquito-borne viral diseases in the world. We perform a retrospective analysis of the 2011 dengue fever epidemic in Pakistan in order to assess the transmissibility of the disease. We obtain estimates of the basic reproduction number R0 from epidemic data using different methodologies applied to different epidemic models in order to evaluate the robustness of our estimate.

Results

We first estimate model parameters by fitting a deterministic ODE vector-host model for the transmission dynamics of single-strain dengue to the epidemic data, using both a basic ordinary least squares (OLS) as well as a generalized least squares (GLS) scheme. Moreover, we perform the same analysis for a direct-transmission ODE model, thereby allowing us to compare our results across different models. In addition, we formulate a direct-transmission stochastic model for the transmission dynamics of dengue and obtain parameter estimates for the stochastic model using Markov chain Monte Carlo (MCMC) methods. In each of the cases we have considered, the estimate for the basic reproduction number R0 is initially greater than unity leading to an epidemic outbreak. However, control measures implemented several weeks after the initial outbreak successfully reduce R0 to less than unity, thus resulting in disease elimination. Furthermore, it is observed that there is strong agreement in our estimates for the pre-control value of R0, both across different methodologies as well across different models. However, there are also significant differences between our estimates for the post-control value of the basic reproduction number across the two different models.

Conclusion

In conclusion, we have obtained robust estimates for the value of the basic reproduction number R0 associated with the 2011 dengue fever epidemic before the implementation of public health control measures. Furthermore, we have shown that there is close agreement between our estimates for the post-control value of R0 across the different methodologies. Nevertheless, there are also significant differences between the estimates for the post-control value of R0 across the two different models.

【 授权许可】

   
2014 Khan et al.; licensee BioMed Central Ltd.

【 预 览 】
附件列表
Files Size Format View
20140708003113688.pdf 841KB PDF download
Figure 12. 41KB Image download
Figure 11. 27KB Image download
Figure 10. 32KB Image download
Figure 9. 24KB Image download
Figure 8. 18KB Image download
Figure 7. 20KB Image download
Figure 6. 25KB Image download
Figure 5. 24KB Image download
Figure 4. 36KB Image download
Figure 2. 25KB Image download
Figure 1. 27KB Image download
【 图 表 】

Figure 1.

Figure 2.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

Figure 8.

Figure 9.

Figure 10.

Figure 11.

Figure 12.

【 参考文献 】
  • [1]Ranjit S, Kissoon N: Dengue hemorrhagic fever and shock syndromes. Pediatr Crit Care Med 2011, 12:90-100.
  • [2]World Health Organization: dengue and severe dengue fact sheet 2012. [http://www.who.int/mediacentre/factsheets/fs117/en/ webcite]
  • [3]Gubler DJ: Dengue and dengue hemorrhagic fever. Clin Microbiol Rev 1998, 11(3):480-496.
  • [4]Halstead S, Nimmannitya S, Cohen S: Observations related to pathogenesis of dengue hemorrhagic fever. IV. Relation of disease severity to antibody response and virus recovered. Yale J Biol Med 1970, 42(5):311-322.
  • [5]Kautner I, Robinson MJ, Kuhnle U: Dengue virus infection: epidemiology, pathogenesis, clinical presentation, diagnosis, and prevention. J Pediatr 1997, 131(4):516-524.
  • [6]Shekhar C: Deadly dengue: new vaccines promise to tackle this escalating global menace. Chem Biol 2007, 14(8):871-872.
  • [7]Holmes EC, Twiddy SS: The origin, emergence and evolutionary genetics of dengue virus. Infect Genet Evol 2003, 3:19-28.
  • [8]Whitehorn J, Farrar J: Dengue. Br Med Bull 2010, 95:161-173.
  • [9]Gubler D, Kuno G: Dengue and Dengue Hemorrhagic Fever. London: CAB INTERNATIONAL; 1997.
  • [10]Kawaguchi I, Sasaki A, Boots M: Why are dengue virus serotypes so distantly related? Enhancement and limiting serotype similarity between dengue virus strains. Proc R Soc Lond B Biol Sci 2003, 270(1530):2241-2247.
  • [11]Garba SM, Gumel AB: Abu Bakar MR: Backward bifurcations in dengue transmission dynamics. Math Biosci 2008, 215:11-25.
  • [12]Garba S, Gumel A: Effect of cross-immunity on the transmission dynamics of two strains of dengue. Int J Comput Math 2010, 87(10):2361-2384.
  • [13]Wearing HJ, Rohani P: Ecological and immunological determinants of dengue epidemics. Proc Natl Acad Sci 2006, 103(31):11802-11807.
  • [14]Esteva L, Vargas C: Coexistence of different serotypes of dengue virus. J Math Biol 2003, 46:31-47.
  • [15]Ferguson N, Anderson R, Gupta S: The effect of antibody-dependent enhancement on the transmission dynamics and persistence of multiple-strain pathogens. Proc Natl Acad Sci 1999, 96(2):790-794.
  • [16]Esteva L, Vargas C: A model for dengue disease with variable human population. J Math Biol 1999, 38(3):220-240.
  • [17]Esteva L, Vargas C: Analysis of a dengue disease transmission model. Math Biosci 1998, 150(2):131-151.
  • [18]Chowell G, Diaz-Dueñas P, Miller J, Alcazar-Velazco A, Hyman J, Fenimore P, Castillo-Chavez C: Estimation of the reproduction number of dengue fever from spatial epidemic data. Math Biosci 2007, 208(2):571-589.
  • [19]Allen LJ: An introduction to stochastic epidemicmodels. In Mathematical Epidemiology, Volume 1945 of Lecture Notes in Mathematics. Edited by Brauer F, Driessche P, Wu J. Springer Berlin Heidelberg, 14197 Berlin Germany; 2008:81-130.
  • [20]Keeling MJ, Ross JV: On methods for studying stochastic disease dynamics. J R Soc Interface 2008, 5(19):171-181.
  • [21]Bailey NT: A simple stochastic epidemic. Biometrika 1950, 37:193-202.
  • [22]Allen LJ, Flores DA, Ratnayake RK, Herbold JR: Discrete-time deterministic and stochastic models for the spread of rabies. Appl Math Comput 2002, 132(2):271-292.
  • [23]Weiss GH, Dishon M: On the asymptotic behavior of the stochastic and deterministic models of an epidemic. Math Biosci 1971, 11(3):261-265.
  • [24]Tuite AR, Tien J, Eisenberg M, Earn DJ, Ma J, Fisman DN: Cholera epidemic in Haiti, 2010: using a transmission model to explain spatial spread of disease and identify optimal control interventions. Ann Intern Med 2011, 154(9):593-601.
  • [25]Allen L, Driessche P: Stochastic epidemic models with a backward bifurcation. Math Biosci Eng 2006, 3(3):445.
  • [26]de Souza DR, Tomé T, Pinho ST, Barreto FR, de Oliveira MJ: Stochastic dynamics of dengue epidemics. Phys Rev E 2013, 87:012709.
  • [27]Spencer S: Stochastic epidemic models for emerging diseases. PhD thesis. University of Nottingham; 2008.
  • [28]Allen LJ: An Introduction to Stochastic Processes with Applications to Biology. New Jersey: Pearson Education; 2003.
  • [29]Allen LJ, Burgin AM: Comparison of deterministic and stochastic SIS and SIR models in discrete time. Math Biosci 2000, 163:1-33.
  • [30]Cintrón-Arias A, Castillo-Chávez C, Bettencourt LM, Lloyd AL, Banks H: The estimation of the effective reproductive number from disease outbreak data. Math Biosci Eng 2009, 6(2):261-282.
  • [31]Van den Driessche P, Watmough J: Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission. Math Biosci 2002, 180:29-48.
  • [32]Chowell G, Hengartner N, Castillo-Chavez C, Fenimore F, Hyman J: The basic reproductive number of Ebola and effects of public health measures: the cases of Congo and Uganda. J Theor Biol 2004, 229:119-126.
  • [33]Lekone PE, Finkenstädt BF: Statistical inference in a stochastic epidemic SEIR model with control intervention: Ebola as a case study. Biometrics 2006, 62(4):1170-1177.
  • [34]O’Neill P, Roberts GO: Bayesian inference for partially observed stochastic epidemics. J R Statisitcal Soc A 1999, 162:121-129.
  • [35]Sanchez MA, Blower SM: Uncertainty and sensitivity analysis of the basic reproductive rate: tuberculosis as an example. Am J Epidemiol 1997, 145(12):1127-1137.
  • [36]Suaya JA, Shepard DS, Beatty ME: Dengue: burden of disease and costs of illness. In TDR. Report of the Scientific Working Group Meeting on Dengue. Geneva Switzerland: World Health Organization; 2006:35-49.
  • [37]Beatty ME, Beutels P, Meltzer MI, Shepard DS, Hombach J, Hutubessy R, Dessis D, Coudeville L, Dervaux B, Wichmann O, Margolis HS, Kuritsky JN: Health economics of dengue: a systematic literature review and expert panel’s assessment. Am J Trop Med Hyg 2011, 84(3):473-488.
  • [38]Banks HT, Davidian M, Jr Samuels JR, Sutton KL: An Inverse Problem Statistical Methodology Summary. 3994 AK Houten Netherland; 2009.
  • [39]Jacquez JA, O’Neill P: Reproduction numbers and thresholds in stochastic epidemic models I. Homogeneous populations. Math Biosci 1991, 107(2):161-186.
  文献评价指标  
  下载次数:70次 浏览次数:35次