Advertisement

Time Series Analysis and Forecasting of Dengue Using Open Data

  • Chiung Ching HoEmail author
  • Choo-Yee Ting
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9429)

Abstract

The modeling of dengue fever cases is an important task to help public health officers to plan and prepare their resources to prevent dengue fever outbreak. In this paper, we present the time-series modeling of accumulated dengue fever cases acquired from the Malaysian Open Data Government Portal. Evaluation of the forecast for future dengue fever outbreak shows promising results, as evidence is presented for the trend and seasonal nature of dengue fever outbreaks in Malaysia.

Keywords

Open Data Dengue fever Time series STL decomposition 

References

  1. 1.
    Gubler, D.J., Clark, G.G.: Dengue/dengue hemorrhagic fever: the emergence of a global health problem. Emerg. Infect. Dis. 1, 55–57 (1995)CrossRefGoogle Scholar
  2. 2.
    Gubler, D.J.: Dengue and dengue hemorrhagic fever. Clin. Microbiol. Rev. 11, 480–496 (1998)Google Scholar
  3. 3.
    Agensi Remote Sensing Malaysia (ARSM): Laman Utama | iDengue. http://idengue.remotesensing.gov.my/idengue/index.php
  4. 4.
    Gomes, V.E.-L.: Question marks as organ-failure joins symptoms of dengue fever | Malaysia | Malay Mail Online. http://www.themalaymailonline.com/malaysia/article/question-marks-as-organ-failure-joins-symptoms-of-dengue-fever
  5. 5.
    Suaya, J.A., Shepard, D.S., Siqueira, J.B., Martelli, C.T., Lum, L.C.S., Tan, L.H., Kongsin, S., Jiamton, S., Garrido, F., Montoya, R., Armien, B., Huy, R., Castillo, L., Caram, M., Sah, B.K., Sughayyar, R., Tyo, K.R., Halstead, S.B.: Cost of dengue cases in eight countries in the Americas and Asia: a prospective study. Am. J. Trop. Med. Hyg. 80, 846–855 (2009)Google Scholar
  6. 6.
    Beatty, M.E., Stone, A., Fitzsimons, D.W., Hanna, J.N., Lam, S.K., Vong, S., Guzman, M.G., Mendez-Galvan, J.F., Halstead, S.B., Letson, G.W., Kuritsky, J., Mahoney, R., Margolis, H.S.: The Asia-Pacific and Americas dengue prevention boards surveillance working group: best practices in dengue surveillance: a report from the Asia-Pacific and Americas dengue prevention boards. PLoS Negl. Trop. Dis. 4, e890 (2010)CrossRefGoogle Scholar
  7. 7.
    Brockwell, P.J., Davis, R.A.: Time Series: Theory and Methods. Springer Science & Business Media, Berlin (2013)Google Scholar
  8. 8.
    Allard, R.: Use of time-series analysis in infectious disease surveillance. Bull. World Health Organ. 76, 327–333 (1998)Google Scholar
  9. 9.
    Tong, S., Hu, W.: Climate variation and incidence of Ross river virus in Cairns, Australia: a time-series analysis. Environ. Health Perspect. 109, 1271–1273 (2001)CrossRefGoogle Scholar
  10. 10.
    Gouveia, N., Fletcher, T.: Respiratory diseases in children and outdoor air pollution in São Paulo, Brazil: a time series analysis. Occup. Environ. Med. 57, 477–483 (2000)CrossRefGoogle Scholar
  11. 11.
    Chadsuthi, S., Modchang, C., Lenbury, Y., Iamsirithaworn, S., Triampo, W.: Modeling seasonal leptospirosis transmission and its association with rainfall and temperature in Thailand using time–series and ARIMAX analyses. Asian Pac. J. Trop. Med. 5, 539–546 (2012)CrossRefGoogle Scholar
  12. 12.
    Luz, P.M., Mendes, B.V.M., Codeço, C.T., Struchiner, C.J., Galvani, A.P.: Time series analysis of dengue incidence in Rio de Janeiro, Brazil. Am. J. Trop. Med. Hyg. 79, 933–939 (2008)Google Scholar
  13. 13.
    Lowe, R., Bailey, T.C., Stephenson, D.B., Graham, R.J., Coelho, C.A.S., Sá Carvalho, M., Barcellos, C.: Spatio-temporal modelling of climate-sensitive disease risk: towards an early warning system for dengue in Brazil. Comput. Geosci. 37, 371–381 (2011)CrossRefGoogle Scholar
  14. 14.
    Cheong, Y.L., Burkart, K., Leitão, P.J., Lakes, T.: Assessing weather effects on dengue disease in Malaysia. Int. J. Environ. Res. Public. Health. 10, 6319–6334 (2013)CrossRefGoogle Scholar
  15. 15.
    Husin, N.A., Salim, N., Ahmad, A.R.: Modeling of dengue outbreak prediction in Malaysia: a comparison of neural network and nonlinear regression model. In: International Symposium on Information Technology, 2008. ITSim 2008, pp. 1–4 (2008)Google Scholar
  16. 16.
    Husin, N.A., Salim, N., Ahmad, A.R.: Simulation of dengue outbreak prediction. Presented at the Postgraduate Annual Research Seminar 2006 (PARS 2006), Postgraduate Studies Department FSKSM, UTM Skudai May (2006)Google Scholar
  17. 17.
    Gersch, W.: Spectral analysis of EEG’s by autoregressive decomposition of time series. Math. Biosci. 7, 205–222 (1970)zbMATHCrossRefGoogle Scholar
  18. 18.
    Madjid, M., Miller, C.C., Zarubaev, V.V., Marinich, I.G., Kiselev, O.I., Lobzin, Y.V., Filippov, A.E., Casscells, S.W.: Influenza epidemics and acute respiratory disease activity are associated with a surge in autopsy-confirmed coronary heart disease death: results from 8 years of autopsies in 34 892 subjects. Eur. Heart J. 28, 1205–1210 (2007)CrossRefGoogle Scholar
  19. 19.
    Zhang, J., Tsui, F.-C., Wagner, M.M., Hogan, W.R.: Detection of outbreaks from time series data using wavelet transform. In: AMIA Annual Symposium Proceedings 2003, pp. 748–752 (2003)Google Scholar
  20. 20.
    Yi, Q., Hoskins, R.E., Hillringhouse, E.A., Sorensen, S.S., Oberle, M.W., Fuller, S.S., Wallace, J.C.: Integrating open-source technologies to build low-cost information systems for improved access to public health data. Int. J. Health Geogr. 7, 29 (2008)CrossRefGoogle Scholar
  21. 21.
    Borukhovich, E.: Open health data. http://openhealthdata.org/
  22. 22.
  23. 23.
    Health 2.0 Developer Challenge: NYS Health Innovation Challenge. http://www.health2con.com/devchallenge/nys-health-innovation-challenge/
  24. 24.
    Theguardian: Power to the people: how open data is improving health service delivery | Global Development Professionals Network | The Guardian. http://www.theguardian.com/global-development-professionals-network/2013/dec/02/open-data-healthcare-accountability-africa
  25. 25.
    MAMPU: Official Malaysia Open Government Portal. http://www.data.gov.my/
  26. 26.
    Moore, A., Seng, S.B., Chong, A.K.: Geostatistical modelling, analysis and mapping of epidemiology of Dengue Fever in Johor State, Malaysia (2005)Google Scholar
  27. 27.
    Rohani, A., Suzilah, I., Malinda, M., Anuar, I., Mohd Mazlan, I., Salmah Maszaitun, M., Topek, O., Tanrang, Y., Ooi, S.C., Rozilawati, H., Lee, H.L.: Aedes larval population dynamics and risk for dengue epidemics in Malaysia. Trop. Biomed. 28, 237–248 (2011)Google Scholar
  28. 28.
    Little, R.J.A., Rubin, D.B.: Statistical Analysis with Missing Data. Wiley, Hoboken (2002)zbMATHCrossRefGoogle Scholar
  29. 29.
    Cleveland, R.B., Cleveland, W.S., McRae, J.E., Terpenning, I.: STL: a seasonal-trend decomposition procedure based on Loess. J. Off. Stat. 6, 3–73 (1990)Google Scholar
  30. 30.
    Brown, R.G., Meyer, R.F.: The fundamental theorem of exponential smoothing. Oper. Res. 9, 673–685 (1961)zbMATHMathSciNetCrossRefGoogle Scholar
  31. 31.
    Hyndman, R.J., Athanasopoulos, G.: Forecasting: principles and practice. OTexts, S.l., Granada (2013)Google Scholar
  32. 32.
    Holt, C.C.: Forecasting seasonals and trends by exponentially weighted moving averages. Int. J. Forecast. 20, 5–10 (2004)CrossRefGoogle Scholar
  33. 33.
    Gardner, E.S.: Exponential smoothing: the state of the art. J. Forecast. 4, 1–28 (1985)CrossRefGoogle Scholar
  34. 34.
    Taylor, J.W.: Exponential smoothing with a damped multiplicative trend. Int. J. Forecast. 19, 715–725 (2003)CrossRefGoogle Scholar
  35. 35.
    Hyndman, R., Koehler, A.B., Ord, J.K., Snyder, R.D.: Forecasting with Exponential Smoothing: the State Space Approach. Springer Science & Business Media, Berlin (2008)CrossRefGoogle Scholar
  36. 36.
    Nau, R.: General seasonal ARIMA models – (0,1,1)×(0,1,1) etc. http://people.duke.edu/~rnau/seasarim.htm
  37. 37.
    Pindyck, R.S., Rubinfeld, D.L.: Econometric Models and Economic Forecasts. Irwin/McGraw-Hill, Boston (1998)Google Scholar
  38. 38.
    Choudhury, Z.M., Banu, S., Islam, A.M.: Forecasting dengue incidence in Dhaka, Bangladesh: a time series analysis. Dengue Bull. 32, 29–37 (2008)Google Scholar
  39. 39.
    Lal, V., Gupta, S., Gupta, O., Bhatnagar, S.: Forecasting incidence of dengue in Rajasthan, using time series analyses. Indian J. Public Health 56, 281 (2012)CrossRefGoogle Scholar
  40. 40.
    Earnest, A., Tan, S.B., Wilder-Smith, A., Machin, D.: Comparing statistical models to predict dengue fever notifications. Comput. Math. Methods Med. 2012, e758674 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Faculty of Computing and InformaticsMultimedia UniversityCyberjayaMalaysia

Personalised recommendations