1921
Volume 79, Issue 6
  • ISSN: 0002-9637
  • E-ISSN: 1476-1645

Abstract

We use the Box-Jenkins approach to fit an autoregressive integrated moving average (ARIMA) model to dengue incidence in Rio de Janeiro, Brazil, from 1997 to 2004. We find that the number of dengue cases in a month can be estimated by the number of dengue cases occurring one, two, and twelve months prior. We use our fitted model to predict dengue incidence for the year 2005 when two alternative approaches are used: 12-steps ahead versus 1-step ahead. Our calculations show that the 1-step ahead approach for predicting dengue incidence provides significantly more accurate predictions ( value = 0.002, Wilcoxon signed-ranks test) than the 12-steps ahead approach. We also explore the predictive power of alternative ARIMA models incorporating climate variables as external regressors. Our findings indicate that ARIMA models are useful tools for monitoring dengue incidence in Rio de Janeiro. Furthermore, these models can be applied to surveillance data for predicting trends in dengue incidence.

Loading

Article metrics loading...

The graphs shown below represent data from March 2017
/content/journals/10.4269/ajtmh.2008.79.933
2008-12-01
2019-11-13
Loading full text...

Full text loading...

/deliver/fulltext/14761645/79/6/0790933.html?itemId=/content/journals/10.4269/ajtmh.2008.79.933&mimeType=html&fmt=ahah

References

  1. Gubler DJ, 1998. Dengue and dengue hemorrhagic fever. Clin Microbiol Rev 11 : 480–496. [Google Scholar]
  2. Halstead SB, 2002. Dengue. Curr Opin Infect Dis 15 : 471–476. [Google Scholar]
  3. Guzman MG, Kouri G, 2002. Dengue: an update. Lancet Infect Dis 2 : 33–42. [Google Scholar]
  4. Wilson ME, Chen LH, 2002. Dengue in the Americas. Dengue Bulletin 26 : 44–61. [Google Scholar]
  5. Torres JR, Castro J, 2007. The health and economic impact of dengue in Latin America. Cad Saude Publica 23 (Suppl 1): S23–S31. [Google Scholar]
  6. Nogueira RM, Araujo JMG, Schatzmayr HG, 2007. Dengue viruses in Brazil, 1986–2006. Rev Panam Salud Publica 22 : 358–363. [Google Scholar]
  7. Camara FP, Theophilo RL, Dos Santos GT, Pereira SR, Camara DC, de Matos RR, 2007. Regional and dynamics characteristics of dengue in Brazil: a retrospective study. Rev Soc Bras Med Trop 40 : 192–196. [Google Scholar]
  8. Nogueira RM, Miagostovich MP, Schatzmayr HG, dos Santos FB, de Araujo ES, de Filippis AM, de Souza RV, Zagne SM, Nicolai C, Baran M, Teixeira Filho G, 1999. Dengue in the state of Rio de Janeiro, Brazil, 1986–1998. Mem Inst Oswaldo Cruz 94 : 297–304. [Google Scholar]
  9. Nogueira RM, Schatzmayr HG, de Filippis AM, dos Santos FB, da Cunha RV, Coelho JO, de Souza LJ, Guimaraes FR, de Araujo ES, De Simone TS, Baran M, Teixeira G Jr, Miagostovich MP, 2005. Dengue virus type 3, Brazil, 2002. Emerg Infect Dis 11 : 1376–1381. [Google Scholar]
  10. Siqueira JB Jr, Martelli CM, Coelho GE, Simplicio AC, Hatch DL, 2005. Dengue and dengue hemorrhagic fever, Brazil, 1981–2002. Emerg Infect Dis 11 : 48–53. [Google Scholar]
  11. Honorio NA, Lourenco-de-Oliveira R, 2001. Frequency of Aedes aegypti and Aedes albopictus larvae and pupae in traps, Brazil. Rev Saude Publica 35 : 385–391. [Google Scholar]
  12. Gubler DJ, Kuno G, 1997. Dengue and Dengue Hemorrhagic Fever. New York: CAB International.
  13. WHO, 2004. Using Climate to Predict Infectious Disease Outbreaks: A Review. Geneva: World Health Organization, 55.
  14. Arcari P, Tapper N, Pfueller S, 2007. Regional variability in relationships between climate and dengue/DHF in Indonesia. Singap J Trop Geogr 28 : 251–272. [Google Scholar]
  15. Barrera R, Delgado N, Jimenez M, Valero S, 2002. Eco-epidemiological factors associated with hyperendemic dengue haemorrhagic fever in Maracay city, Venezuela. Dengue Bull 26 : 84–92. [Google Scholar]
  16. Burattini MN, Chen M, Chow A, Coutinho FA, Goh KT, Lopez LF, Ma S, Massad E, 2007. Modelling the control strategies against dengue in Singapore. Epidemiol Infect 136 : 1–11. [Google Scholar]
  17. Chadee DD, Shivnauth B, Rawlins SC, Chen AA, 2007. Climate, mosquito indices and the epidemiology of dengue fever in Trinidad (2002–2004). Ann Trop Med Parasitol 101 : 69–77. [Google Scholar]
  18. Chowell G, Sanchez F, 2006. Climate-based descriptive models of dengue fever: the 2002 epidemic in Colima, Mexico. J Environ Health 68 : 40–44. [Google Scholar]
  19. Corwin AL, Larasati RP, Bangs MJ, Wuryadi S, Arjoso S, Sukri N, Listyaningsih E, Hartati S, Namursa R, Anwar Z, Chandra S, Loho B, Ahmad H, Campbell JR, Porter KR, 2001. Epidemic dengue transmission in southern Sumatra, Indonesia. Trans R Soc Trop Med Hyg 95 : 257–265. [Google Scholar]
  20. Depradine C, Lovell E, 2004. Climatological variables and the incidence of dengue fever in Barbados. Int J Environ Health Res 14 : 429–441. [Google Scholar]
  21. Focks DA, Barrera R, 2006. Dengue Transmission Dynamics: Assessment and Implications for Control. Report on the Scientific Working Group on Dengue, 2006. Geneva: World Health Organization, 92–109.
  22. Keating J, 2001. An investigation into the cyclical incidence of dengue fever. Soc Sci Med 53 : 1587–1597. [Google Scholar]
  23. Nakhapakorn K, Tripathi NK, 2005. An information value based analysis of physical and climatic factors affecting dengue fever and dengue haemorrhagic fever incidence. Int J Health Geogr 4 : 13. [Google Scholar]
  24. Wu PC, Guo HR, Lung SC, Lin CY, Su HJ, 2007. Weather as an effective predictor for occurrence of dengue fever in Taiwan. Acta Trop 103 : 50–57. [Google Scholar]
  25. Bangs MJ, Larasati RP, Corwin AL, Wuryadi S, 2006. Climatic factors associated with epidemic dengue in Palembang, Indonesia: implications of short-term meteorological events on virus transmission. Southeast Asian J Trop Med Public Health 37 : 1103–1116. [Google Scholar]
  26. Scott TW, Morrison AC, 2003. Aedes aegypti and the risk of dengue-virus transmission. Takken W, Scott TW, eds. Ecological Aspects for Application of Genetically Modified Mmosquitoes. Dordretch, The Netherlands: FRONTIS, 187–206.
  27. Helfenstein U, 1991. The use of transfer function models, intervention analysis and related time series methods in epidemiology. Int J Epidemiol 20 : 808–815. [Google Scholar]
  28. Choi K, Thacker SB, 1981. An evaluation of influenza mortality surveillance, 1962–1979. 1. Time-series forecasts of expected pneumonia and influenza deaths. Am J Epidemiol 113 : 215–226. [Google Scholar]
  29. Farmer RD, Emami J, 1990. Models for forecasting hospital bed requirements in the acute sector. J Epidemiol Community Health 44 : 307–312. [Google Scholar]
  30. Milner PC, 1997. Ten-year follow-up of ARIMA forecasts of attendances at accident and emergency departments in the Trent region. Stat Med 16 : 2117–2125. [Google Scholar]
  31. Nobre FF, Monteiro AB, Telles PR, Williamson GD, 2001. Dynamic linear model and SARIMA: a comparison of their forecasting performance in epidemiology. Stat Med 20 : 3051–3069. [Google Scholar]
  32. Allard R, 1998. Use of time-series analysis in infectious disease surveillance. Bull World Health Organ 76 : 327–333. [Google Scholar]
  33. Bowie C, Prothero D, 1981. Finding causes of seasonal diseases using time series analysis. Int J Epidemiol 10 : 87–92. [Google Scholar]
  34. Helfenstein U, 1986. Box-Jenkins modelling of some viral infectious diseases. Stat Med 5 : 37–47. [Google Scholar]
  35. Tong S, Hu W, 2001. Climate variation and incidence of Ross River virus in Cairns, Australia: a time-series analysis. Environ Health Perspect 109 : 1271–1273. [Google Scholar]
  36. Trottier H, Philippe P, Roy R, 2006. Stochastic modeling of empirical time series of childhood infectious diseases data before and after mass vaccination. Emerg Themes Epidemiol 3 : 9. [Google Scholar]
  37. Reis BY, Mandl KD, 2003. Time series modeling for syndromic surveillance. BMC Med Inform Decis Mak 3 : 2. [Google Scholar]
  38. Earnest A, Chen MI, Ng D, Sin LY, 2005. Using autoregressive integrated moving average (ARIMA) models to predict and monitor the number of beds occupied during a SARS outbreak in a tertiary hospital in Singapore. BMC Health Serv Res 5 : 36. [Google Scholar]
  39. Box GEP, Jenkins GM, 1976. Time Series Analysis: Forecasting and Control. San Francisco: Holden-Day.
  40. Heiberger RM, Holland B, 2004. Statistical Analysis and Data Display: An Intermediate Course with Examples in S-plus, R, and SAS. New York: Springer.
  41. ADPRJ, 2007. Armazen dos dados da Prefeitura do Rio de Janeiro (City-hall of Rio de Janeiro). Available at: http://www.armazemdedados.rio.rj.gov.br/. Accessed July 20, 2007.
  42. SMSRJ, 2007. Secretaria Municipal de Saúde do Rio de Janeiro (Health Department of the city of Rio de Janeiro). Available at: http://www.saude.rio.rj.gov.br/. Accessed June 15, 2007.
  43. Heiberger RM, Teles P, 2002. Displays for direct comparison of ARIMA models. Am Stat 56 : 131–138. [Google Scholar]
  44. Ljung GM, Box GEP, 1978. Measure of lack of fit in time-series models. Biometrika 65 : 297–303. [Google Scholar]
  45. R Development Core Team, 2007. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.
  46. Hyndman RJ, 2008. Forecast: Forecasting Functions for Time Series. R package version 1.11. Available at: http://www.robhyndman.info/Rlibrary/forecast/.
  47. Hyndman RJ, Khandakar Y, 2008. Automatic time series forecasting: the forecast package for R. J Statis Software.
  48. Scott TW, Amerasinghe PH, Morrison AC, Lorenz LH, Clark GG, Strickman D, Kittayapong P, Edman JD, 2000. Longitudinal studies of Aedes aegypti (Diptera: Culicidae) in Thailand and Puerto Rico: blood feeding frequency. J Med Entomol 37 : 89–101. [Google Scholar]
  49. Scott TW, Morrison AC, Lorenz LH, Clark GG, Strickman D, Kittayapong P, Zhou H, Edman JD, 2000. Longitudinal studies of Aedes aegypti (Diptera: Culicidae) in Thailand and Puerto Rico: population dynamics. J Med Entomol 37 : 77–88. [Google Scholar]
  50. Watts DM, Burke DS, Harrison BA, Whitmire RE, Nisalak A, 1987. Effect of temperature on the vector efficiency of Aedes aegypti for dengue 2 virus. Am J Trop Med Hyg 36 : 143–152. [Google Scholar]
  51. Focks DA, Brenner RJ, Hayes J, Daniels E, 2000. Transmission thresholds for dengue in terms of Aedes aegypti pupae per person with discussion of their utility in source reduction efforts. Am J Trop Med Hyg 62 : 11–18. [Google Scholar]
  52. da Cunha RV, Dias M, Nogueira RM, Chagas N, Miagostovich MP, Schatzmayr HG, 1995. Secondary dengue infection in schoolchildren in a dengue endemic area in the state of Rio de Janeiro, Brazil. Rev Inst Med Trop Sao Paulo 37 : 517–521. [Google Scholar]
  53. Figueiredo LT, Cavalcante SM, Simoes MC, 1990. Dengue serologic survey of schoolchildren in Rio de Janeiro, Brazil, in 1986 and 1987. Bull Pan Am Health Organ 24 : 217–225. [Google Scholar]
  54. Honorio NA, Silva Wda C, Leite PJ, Goncalves JM, Lounibos LP, Lourenco-de-Oliveira R, 2003. Dispersal of Aedes aegypti and Aedes albopictus (Diptera: Culicidae) in an urban endemic dengue area in the State of Rio de Janeiro, Brazil. Mem Inst Oswaldo Cruz 98 : 191–198. [Google Scholar]
  55. de Lima-Camara TN, Honorio NA, Lourenco-de-Oliveira R, 2006. Frequency and spatial distribution of Aedes aegypti and Aedes albopictus (Diptera: Culicidae) in Rio de Janeiro, Brazil. Cad Saude Publica 22 : 2079–2084. [Google Scholar]
  56. Maciel-De-Freitas R, Codeco CT, Lourenco-De-Oliveira R, 2007. Daily survival rates and dispersal of Aedes aegypti females in Rio de Janeiro, Brazil. Am J Trop Med Hyg 76 : 659–665. [Google Scholar]
  57. Maciel-de-Freitas R, Marques WA, Peres RC, Cunha SP, de Oliveira RL, 2007. Variation in Aedes aegypti (Diptera: Culicidae) container productivity in a slum and a suburban district of Rio de Janeiro during dry and wet seasons. Mem Inst Oswaldo Cruz 102 : 489–496. [Google Scholar]
  58. Gubler DJ, 2002. Epidemic dengue/dengue hemorrhagic fever as a public health, social and economic problem in the 21st century. Trends Microbiol 10 : 100–103. [Google Scholar]
  59. Teixeira MG, Costa MCN, Guerra Z, Barreto ML, 2002. Dengue in Brazil: situation-2001 and trends. Dengue Bull 26 : 70–76. [Google Scholar]
  60. de Souza IC, Vianna RP, de Moraes RM, 2007. Modeling of dengue incidence in Paraiba State, Brazil, using distributed lag models. Cad Saude Publica 23 : 2623–2630. [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.4269/ajtmh.2008.79.933
Loading
/content/journals/10.4269/ajtmh.2008.79.933
Loading

Data & Media loading...

  • Received : 26 Oct 2007
  • Accepted : 04 Aug 2008

Most Cited This Month

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error