Modelling weekly vector control against Dengue in the Guangdong Province of China

Journal of Theoretical Biology

Volumn 410, Issue , 2016, Pages 65-76

  Tang, Biao ^a,c   Xiao, Yanni ^a   Tang, Sanyi ^b   Wu, Jianhong ^c  

^a XI'AN JIAOTONG UNIVERSITY   (China)

^b SHAANXI NORMAL UNIVERSITY   (China)

^c YORK UNIVERSITY   (Canada)

Author keywords

Control reproduction number; Dengue; Impulsive control strategy; Parameter estimation; Sensitive analysis

Indexed keywords

DENGUE FEVER; DISEASE CONTROL; DISEASE TREATMENT; ESTIMATION METHOD; INFECTIOUS DISEASE; NUMERICAL MODEL; SENSITIVITY ANALYSIS;

ARTICLE; CHINA; CONTROLLED STUDY; DENGUE; EPIDEMIC; INSECTICIDAL ACTIVITY; MATHEMATICAL MODEL; MOSQUITO; PRIORITY JOURNAL; RETROSPECTIVE STUDY; VECTOR CONTROL; VIRUS TRANSMISSION; BIOLOGICAL MODEL; HUMAN; MOSQUITO VECTOR; PEST CONTROL; TIME FACTOR; TRANSMISSION;

CHINA; GUANGDONG;

CHINA; DENGUE; HUMANS; MODELS, BIOLOGICAL; MOSQUITO VECTORS; PEST CONTROL; TIME FACTORS;

EID: 84988473946     PISSN: 00225193     EISSN: 10958541     Source Type: Journal    
DOI: 10.1016/j.jtbi.2016.09.012     Document Type: Article

References (54)

1. Adams, B., Boots, M., How important is vertical transmission in mosquitoes for the persistence of dengue? Insights from a mathematical model. Epidemics 2 (2010), 1–10.
2. Andraud, M., Hens, N., Marais, C., Beutels, P., Dynamic epidemiological models for dengue transmission: a systematic review of structural approaches. PLoS One, 7(11), 2012, e49085.
3. Atkinson, M.P., Su, Z., Alphey, N., et al. Analyzing the control of mosquito-borne diseases by a dominant lethal genetic system. Proc. Natl. Acad. Sci. U.S.A. 104 (2007), 9540–9545.
4. Bainov, D.D., Simeonov, P.S., 1989. Systems with Impulse Effect, Theory and Applications. Mathematics and Applications. Ellis-Horwood, Chichester, UK.
5. Bainov, D.D., Simeonov, P.S., 1993. Impulsive Differential Equations: Periodic Solutions and Applications. Pitman Monographs and Surveys in Pure and Applied Mathematics, vol. 66. Longman, Harlow.
6. Burattini, M.N., Chen, M., Chow, A., et al. Modelling the control strategies against dengue in Singapore. Epidemiol. Infect. 136 (2008), 309–319.
7. Bailey, N.T.J., The Mathematical Theory of Infectious Diseases and Its Applications. 2nd ed., 1975, Griffin, London.
8. Blower, S.M., Dowlatabadi, H., Sensitivity and uncertainty analysis of complex models of disease transmission: an HIV model, as an example. Int. Stat. Rev. 62 (1994), 229–243.
9. Cummings, D.A.T., Iamsirithaworn, S., Lessler, J.T., et al. The impact of the demographic transition on dengue in Thailand: insights from a statistical analysis and mathematical modeling. PLoS Med., 6(9), 2009, e1000139.
10. Coutinho, F.A.B., Burattini, M., Lopez, L., Massad, E., Threshold conditions for a non-autonomous epidemic system describing the population dynamics of dengue. Bull. Math. Biol. 68 (2006), 2263–2282.
11. Chowell, G., Diaz-Dueñas, P., Miller, J.C., et al. Estimation of the reproduction number of dengue fever from spatial epidemic data. Math. Biosci. 208 (2007), 571–589.
12. Coelho, G.E., Burattini, M.N., Teixeira, M.D.G., Coutinho, F.A., Massad, E., Dynamics of the 2006/2007 dengue outbreak in Brazil. Mem. Inst. Oswaldo Cruz 103:6 (2008), 535–539.
13. Degallier, N., Favier, C., Boulanger, J.P., Menkes, C., Imported and autochthonous cases in the dynamics of dengue epidemics in Brazil. Rev. Saude Publica 43:1 (2009), 1–7.
14. Diekmann, O., Heesterbeek, J.A.P., Mathematical Epidemiology of Infectious Diseases: Model Building, Analysis and Interpretation. 2000, John Wiley, Chichester.
15. Esteva, L., Vargas, C., Analysis of a dengue disease transmission model. Math. Biosci. 150 (1998), 131–151.
16. Esteva, L., Vargas, C., Influence of vertical and mechanical transmission on the dynamics of dengue disease. Math. Biosci. 167 (2000), 51–64.
17. Erickson, R.A., Presley, S.M., Allen, L.J.S., Long, K.R., Cox, S.B., A stagestructured, Aedes albopictus population model. Ecol. Model. 221 (2010), 1273–1282.
18. Favier, C., Degallier, N., Rosa-Freitas, M.G., et al. Early determination of the reproductive number for vector-borne diseases: the case of dengue in Brazil. Trop. Med. Int. Health 11:3 (2006), 332–340.
19. Ferguson, N.M., Donnelly, C.A., Anderson, R.M., Transmission dynamics and epidemiology of dengue: insights from age-stratified seroprevalence surveys. Philos. Trans. R. Soc. Lond. B 354 (1999), 757–768.
20. Guangdong Bureau of Health, 2014. http://www.gdwst.gov.cn/a/yiqingxx/index-7.html (accessed 11/08/2016).
21. Geweke, J., Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments. Bernardo, J.M., Berger, J., Dawid, A.P., Smith, A.F.M., (eds.) Bayesian Statistics, vol. 4, 1992, Oxford University Press, Oxford, 169–193.
22. Garba, S.M., Gumel, A.B., Abu Bakar, M.R., Backward bifurcations in dengue transmission dynamics. Math. Biosci. 215 (2008), 11–25.
23. Haario, H., Laine, M., Mira, A., Saksman, E., DRAM: efficient adaptive MCMC. Stat. Comput. 16 (2006), 339–354.
24. Halstead, S.B., Dengue. The Lancet 370 (2007), 1644–1652.
25. Heesterbeek, J., Roberts, M.G., The type-reproduction number T in models for infectious disease control. Math. Biosci. 206 (2007), 3–10.
26. Johansson, M.A., Hombach, J., Cummings, D.A.T., Models of the impact of dengue vaccines: a review of current research and potential approaches. Vaccine 29 (2011), 5860–5868.
27. Kautner, I., Robinson, M.J., Kuhnle, U., Dengue virus infection: epidemiology, pathogenesis, clinical presentation, diagnosis, and prevention. J. Pediatr. 131 (1997), 516–524.
28. Kurane, I., Takasaki, T., Yamada, K., Trends in flavivirus infections in Japan. Emerg. Infect. Dis. 6 (2000), 569–571.
29. Koopman, J.S., Prevots, D.R., Vaca Marin, M.A., et al. Determinants and predictors of dengue infection in Mexico. Am. J. Epidemiol. 133:11 (1991), 1168–1178.
30. 0. Comput. Math. Methods Med. 13 (2011), 224–234.
31. Luz, P.M., Lima-Camara, T.N., Bruno, R.V., et al. Potential impact of a presumed increase in the biting activity of dengue-virus-infected Aedes aegypti (Diptera: Culicidae) females on virus transmission dynamics. Mem. Inst. Oswaldo Cruz. 106 (2011), 755–758.
32. Luz, P.M., Vanni, T., Medlock, J., Paltiel, A.D., Galvani, A.P., Dengue vector control strategies in an urban setting: an economic modelling assessment. The Lancet 377 (2011), 1673–1680.
33. Marques, C.A., Forattini, O.P., Massad, E., The basic reproduction number for dengue fever in Sao Paulo state, Brazil: 1990–1991 epidemic. Trans. R. Soc. Trop. Med. Hyg. 88 (1994), 58–59.
34. Massad, E., Burattini, M.N., Coutinho, F.A., Lopez, L.F., Dengue and the risk of urban yellow fever reintroduction in Sao Paulo State, Brazil. Rev. Saude Publica 37:4 (2003), 477–484.
35. Massad, E., Coutinho, F.A., Burattini, M.N., Lopez, L.F., The risk of yellow fever in a dengue-infested area. Trans. R. Soc. Trop. Med. Hyg. 95:4 (2001), 370–374.
36. Mckay, M.D., Beckman, R.J., Conover, W.J., A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21 (1979), 239–245.
37. Marino, S., Ian, B., Hogue, I.B., Ray, C.J., Kirschner, D.E., A methodology for performing global uncertainty and sensitivity analysis in systems biology. J. Theor. Biol. 254 (2008), 178–196.
38. Messer, W.B., Gubler, D.J., Harris, E., Sivananthan, K., de Silva, A.M., Emergence and global spread of a dengue serotype 3, subtype III virus. Emerg. Infect. Dis. 9 (2003), 800–809.
39. Newton, E.A.C., Reiter, P., A model of the transmission of dengue fever with an evaluation of the impact of ultra-low volume (ULV) insecticide applications on Dengue epidemics. Am. J. Trop. Med. Hyg. 47 (1992), 709–720.
40. Nagao, Y., Koelle, K., Decreases in dengue transmission may act to increase the incidence of dengue hemorrhagic fever. Proc. Natl. Acad. Sci. U.S.A. 105:December (2008), 2238–2243.
41. Pongsumpun, P., Patanarapelert, K., Sriprom, M., Varamit, S., Tang, I.M., Infection risk to travelers going to dengue fever endemic regions. Southeast Asian J. Trop. Med. Public Health 35 (2004), 155–159.
42. Rocco, I.M., Kavakama, B.B., Santos, C.L.S., First isolation of dengue 3 in Brazil from an imported case. Rev. Inst. Med. Trop. Sao Paulo 43 (2001), 55–57.
43. Roberts, M.G., Heesterbeek, J., A new method to estimate the effort required to control an infectious disease. Proc. R. Soc. Lond. B 270 (2003), 1359–1364.
44. Supriatna, A.K., Soewono, E., van Gils, S.A., A two-age-classes dengue transmission model. Math. Biosci. 216 (2008), 114–121.
45. Sierra, B., Perez, A.B., Vogt, K., et al. Secondary heterologous dengue infection risk: disequilibrium between immune regulation and inflammation?. Cell Immunol. 262 (2010), 134–140.
46. Scott, T.W., Amerasinghe, P.H., Morrison, A.C., et al. Longitudinal studies of Aedes aegypti (Diptera: Culicidae) in Thailand and Puerto Rico: blood feeding frequency. J. Med. Entomol. 30 (2000), 94–99.
47. Tewa, J.J., Dimi, J.L., Bowong, S., Lyapunov functions for a dengue disease transmission model. Chaos Solitons Fractals 39 (2009), 936–941.
48. Tang, S.Y., Xiao, Y.N., Lin, Y., Check, R.A., Wu, J.H., Campus quarantine (Fengxiao) for curbing emergent infectious diseases: lessons from mitigating A/H1N1 in Xi'an, China. J. Theor. Biol. 295 (2012), 47–58.
49. van den Driessche, P., Watmough, J., Reproduction numbers and subthreshold endemic equilibria for compartmental models of disease transmission. Math. Biosci. 180 (2002), 29–48.
50. Wei, H.M., Li, X.Z., Martcheva, M., An epidemic model of a vector-borne disease with direct transmission and time delay. J. Math. Anal. Appl. 342 (2008), 895–908.
51. Xiao, J.P., He, J.F., Deng, A.P., et al. Characterizing a large outbreak of dengue fever in Guangdong Province. China. Infect. Dis. Poverty, 5, 2016, 44.
52. Xinhua News, 2014. 〈 http://news.xinhuanet.com/politics/2014-10/09/c_1112752842.htm〉 (accessed 11/08/2016).
53. Xinlang News, 2014. 〈 http://gd.sina.com.cn/news/b/2014-10-16/1058134284.html〉 (accessed 11/08/2016).
54. Yang, H.M., Ferreira, C.P., Assessing the effects of vector control on dengue transmission. Appl. Math. Comput. 198 (2008), 401–413.