A Comparative Analysis of the Relative Efficacy of Vector-Control Strategies Against Dengue Fever

Carregando...
Imagem de Miniatura
Citações na Scopus
37
Tipo de produção
article
Data de publicação
2014
Título da Revista
ISSN da Revista
Título do Volume
Editora
SPRINGER
Citação
BULLETIN OF MATHEMATICAL BIOLOGY, v.76, n.3, p.697-717, 2014
Projetos de Pesquisa
Unidades Organizacionais
Fascículo
Resumo
Dengue is considered one of the most important vector-borne infection, affecting almost half of the world population with 50 to 100 million cases every year. In this paper, we present one of the simplest models that can encapsulate all the important variables related to vector control of dengue fever. The model considers the human population, the adult mosquito population and the population of immature stages, which includes eggs, larvae and pupae. The model also considers the vertical transmission of dengue in the mosquitoes and the seasonal variation in the mosquito population. From this basic model describing the dynamics of dengue infection, we deduce thresholds for avoiding the introduction of the disease and for the elimination of the disease. In particular, we deduce a Basic Reproduction Number for dengue that includes parameters related to the immature stages of the mosquito. By neglecting seasonal variation, we calculate the equilibrium values of the model's variables. We also present a sensitivity analysis of the impact of four vector-control strategies on the Basic Reproduction Number, on the Force of Infection and on the human prevalence of dengue. Each of the strategies was studied separately from the others. The analysis presented allows us to conclude that of the available vector control strategies, adulticide application is the most effective, followed by the reduction of the exposure to mosquito bites, locating and destroying breeding places and, finally, larvicides. Current vector-control methods are concentrated on mechanical destruction of mosquitoes' breeding places. Our results suggest that reducing the contact between vector and hosts (biting rates) is as efficient as the logistically difficult but very efficient adult mosquito's control.
Palavras-chave
Dengue, Basic reproduction number, Force of infection, Sensitivity analysis, Vector control
Referências
  1. Adams B, 2010, EPIDEMICS-NETH, V2, P1, DOI 10.1016/j.epidem.2010.01.001
  2. Aguiar M, 2013, ECOL COMPLEX, V16, P31, DOI 10.1016/j.ecocom.2012.09.001
  3. Amaku M., 2013, PHIL T R SOC A
  4. Amaku M, 2009, MEM I OSWALDO CRUZ, V104, P897, DOI 10.1590/S0074-02762009000600013
  5. Amaku M, 2013, COMPUT MATH METHOD M, DOI 10.1155/2013/659038
  6. Anguelov R, 2012, COMPUT MATH APPL, V64, P374, DOI 10.1016/j.camwa.2012.02.068
  7. Bacaer N, 2006, J MATH BIOL, V53, P421, DOI 10.1007/s00285-006-0015-0
  8. Beatty ME, 2008, P 2 INT C DENG DENG
  9. Beatty ME, 2011, AM J TROP MED HYG, V84, P473, DOI 10.4269/ajtmh.2011.10-0521
  10. Brownstin JS, 2003, J INVERTEBR PATHOL, V84, P24, DOI 10.1016/S0022-2011(03)00082-X
  11. Burattini MN, 2008, EPIDEMIOL INFECT, V136, P309, DOI 10.1017/S0950268807008667
  12. Chitnis N, 2008, B MATH BIOL, V70, P1272, DOI 10.1007/s11538-008-9299-0
  13. Cousins R. D., 2004, AM J PHYS, V72, P1068
  14. Coutinho F. A. B., 2004, AM J PHYS, V72, P1068
  15. Coutinho FAB, 2006, B MATH BIOL, V68, P2263, DOI 10.1007/s11538-006-9108-6
  16. Coutinho FAB, 2005, MATH COMPUT SIMULAT, V70, P149, DOI 10.1016/j.matcom.2005.06.003
  17. Dumont Y, 2010, MATH BIOSCI ENG, V7, P313, DOI 10.3934/mbe.2010.7.313
  18. Ellis AM, 2011, AM J TROP MED HYG, V85, P257, DOI 10.4269/ajtmh.2011.10-0516
  19. Erickson RA, 2010, ECOL MODEL, V221, P2899, DOI 10.1016/j.ecolmodel.2010.08.036
  20. Fegan GW, 2007, LANCET, V370, P1035, DOI 10.1016/S0140-6736(07)61477-9
  21. Forattini O. P., 1996, MED CULICIDOLOGY
  22. Garba SM, 2008, MATH BIOSCI, V215, P11, DOI 10.1016/j.mbs.2008.05.002
  23. Gubler Duane J, 2011, Trop Med Health, V39, P3, DOI 10.2149/tmh.2011-S05
  24. Gubler DJ, 2002, ARCH MED RES, V33, P330, DOI 10.1016/S0188-4409(02)00378-8
  25. Guy B, 2011, LANCET, V377, P381, DOI 10.1016/S0140-6736(11)60128-1
  26. Halstead S. B., 1990, Tropical and geographical medicine., P675
  27. Khasnis AA, 2005, ARCH MED RES, V36, P689, DOI 10.1016/j.arcmed.2005.03.041
  28. Kooi BW, 2013, J COMPUT APPL MATH, V252, P148, DOI 10.1016/j.cam.2012.08.008
  29. Lambrechts L, 2010, PLOS NEGLECT TROP D, V4, DOI 10.1371/journal.pntd.0000646
  30. Lopez LF, 2002, CR BIOL, V325, P1073, DOI 10.1016/S1631-0691(02)01534-2
  31. Luz PM, 2011, LANCET, V377, P1673, DOI 10.1016/S0140-6736(11)60246-8
  32. MACDONALD G, 1952, Trop Dis Bull, V49, P813
  33. Massad E, 2011, PHYS LIFE REV, V8, P169, DOI 10.1016/j.plrev.2011.01.001
  34. Massad E, 2009, MALARIA J, V8, DOI 10.1186/1475-2875-8-296
  35. Massad E, 2011, LANCET, V377, P1630, DOI 10.1016/S0140-6736(11)60470-4
  36. Ocampo CB, 2004, AM J TROP MED HYG, V71, P506
  37. Pinho STR, 2010, PHILOS T R SOC A, V368, P5679, DOI 10.1098/rsta.2010.0278
  38. Reiter P., 2001, DENGUE DENGUE HEMORR, P425
  39. Rodrigues H. S., 2012, ARXIV12040544V1
  40. Ross R, 1911, PREVENTION MALARIA
  41. Silverman MP, 2004, AM J PHYS, V72, P1068, DOI 10.1119/1.1738426
  42. Suaya JA, 2009, AM J TROP MED HYG, V80, P846
  43. UNWTO - United Nations World Tourism Organization, 2011, TOUR HIGHL
  44. Wahl LM, 2000, P ROY SOC B-BIOL SCI, V267, P835, DOI 10.1098/rspb.2000.1079
  45. Wang WD, 2008, J DYN DIFFER EQU, V20, P699, DOI 10.1007/s10884-008-9111-8
  46. Wilder-Smith A., 2012, Global Health Action, V5, P17273
  47. Wilder-Smith Annelies, 2010, Curr Infect Dis Rep, V12, P157, DOI 10.1007/s11908-010-0102-7
  48. YASUNO M, 1970, B WORLD HEALTH ORGAN, V43, P319