Skip to main content
SpringerLink
Log in

Research of Dengue Fever Prediction in San Juan, Puerto Rico Based on a KNN Regression Model

  • Conference paper
  • First Online:
Book cover Intelligent Data Engineering and Automated Learning – IDEAL 2017 (IDEAL 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10585))

Abstract

Existed dengue prediction model associated with temperature data are always based on Poisson regression methods or linear models. However, these models are difficult to be applied to non-stationary climate data, such as rainfall or precipitation. A novel k-nearest neighbor (KNN) regression method was proposed to improve the prediction accuracy of dengue fever regression model in this paper. The dengue cases and the climatic factors (average minimum temperature, average maximum temperature, average temperature, average dew point temperature, temperature difference, relative humidity, absolute humidity, Precipitation) in San Juan, Puerto Rico during the period 1990–2013 were regressed by the KNN algorithm. The performances of KNN regression were studied by compared with correlation analysis and Poisson regression method. Results showed that the KNN model fitted real dengue outbreak better than Poisson regression method while the root mean square error was 6.88.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bhatt, S., Gething, P.W., Brady, O.J., et al.: The global distribution and burden of dengue. Nature 496(7446), 504–507 (2013)

    Article  Google Scholar 

  2. Gubler, D.J.: Resurgent vector-borne diseases as a global health problem. Emerg. Infect. Dis. 4(3), 442–450 (1998)

    Article  Google Scholar 

  3. Beatty, M.E., Stone, A., Fitzsimons, D.W., et al.: Best practices in dengue surveillance: a report from the Asia-Pacific and Americas Dengue Prevention Boards. PLoS Negl. Trop. Dis. 4(11), e890 (1935)

    Article  Google Scholar 

  4. Guy, B., Saville, M., Lang, J.: Development of sanofi pasteur tetravalent dengue vaccine. Hum. Vaccines 46(9), 696–705 (2009)

    Google Scholar 

  5. Hadinegoro, S.R., Arredondo-García, J.L., Capeding, M.R., et al.: Efficacy and long-term safety of a dengue vaccine in regions of endemic disease. N. Engl. J. Med. 373(13), 1195–1206 (2015)

    Article  Google Scholar 

  6. Villar, L., Dayan, G.H., Arredondogarcía, J.L., et al.: Efficacy of a tetravalent dengue vaccine in children in latin America. N. Engl. J. Med. 372(2), 113–123 (2015)

    Article  Google Scholar 

  7. Banu, S., Hu, W., Hurst, C., et al.: Dengue transmission in the Asia-Pacific region: impact of climate change and socio-environmental factors. Trop. Med. Int. Health 16(5), 598–607 (2011). Tm & Ih

    Article  Google Scholar 

  8. Descloux, E., Mangeas, M., Menkes, C.E., et al.: Climate-based models for understanding and forecasting dengue epidemics. PLoS Negl. Trop. Dis. 6(2), e1470 (2012)

    Article  Google Scholar 

  9. Ferreira, M.C.: Geographical distribution of the association between El Niño South Oscillation and dengue fever in the Americas: a continental analysis using geographical information system-based techniques. Geospatial Health 9(1), 141–151 (2014)

    Article  Google Scholar 

  10. Liao, C.M., Huang, T.L., Lin, Y.J., et al.: Regional response of dengue fever epidemics to interannual variation and related climate variability. Stoch. Env. Res. Risk Assess. 29(3), 947–958 (2015)

    Article  Google Scholar 

  11. Gagnon, A.S., Bush, A., Smoyer-Tomic, K.E.: Dengue epidemics and the El Niño southern oscillation. Clim. Res. 19(1), 35–43 (2001)

    Article  Google Scholar 

  12. Banu, S., Hu, W., Guo, Y., et al.: Projecting the impact of climate change on dengue transmission in Dhaka, Bangladesh. Environ. Int. 63(3), 137–142 (2014)

    Article  Google Scholar 

  13. Focks, D., Alexander, N., Villegas, E.: Multicountry study of Aedes aegypti pupal productivity survey methodology: findings and recommendations. Dengue Bull WHO 31, 192–200 (2007)

    Google Scholar 

  14. Arcari, P., Tapper, N., Pfueller, S.: Regional variability in relationships between climate and dengue/DHF in Indonesia. Singap. J. Trop. Geogr. 28, 251–272 (2007)

    Article  Google Scholar 

  15. Bangs, M., Larasati, R., Corwin, A., et al.: 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 (2006)

    Google Scholar 

  16. Burattini, M., Chen, M., Chow, A., et al.: Modelling the control strategies against dengue in Singapore. Epidemiol. Infect. 136, 309–319 (2007)

    Google Scholar 

  17. Chowell, G., Sanchez, F.: Climate-based descriptive models of dengue fever: the 2002 epidemic in Colima. Mexico. J Environ. Health 68, 40–44 (2006)

    Google Scholar 

  18. Keating, J.: An investigation into the cyclical incidence of dengue fever. Soc. Sci. Med. 53, 1587–1597 (2001)

    Article  Google Scholar 

  19. Descloux, E., Mangeas, M., Menkes, C.E., et al.: Climate-based models for understanding and forecasting dengue epidemics. PLoS Negl. Trop. Dis. 6, e1470 (2012)

    Article  Google Scholar 

  20. Gharbi, M., Quenel, P., Gustave, J., et al.: Time series analysis of dengue incidence inGuadeloupe, French West Indies: forecasting models using climate variables as predictors. BMC Infect. Dis. 11, 166 (2011)

    Article  Google Scholar 

  21. Goto, K., Kumarendran, B., Mettananda, S., et al.: Analysis of effects of meteorological factors on dengue incidence in Sri Lanka using time series data. PLoS ONE 8, e63717 (2013)

    Article  Google Scholar 

  22. Chen, S.C., Liao, C.M., Chio, C.P., et al.: Lagged temperature effect with mosquito transmission potential explains dengue variability in southern Taiwan: insights from a statistical analysis. Sci. Total Environ. 408, 4069–4075 (2010)

    Article  Google Scholar 

  23. Antoine, A., Pascal, R., Morgan, M., et al.: Predicting dengue fever outbreaks in French guiana using climate indicators. PLoS Negl. Trop. Dis. 10(4), e0004681 (2016)

    Article  Google Scholar 

  24. Barrera, R., Amador, M., Mackay, A.J.: Population dynamics of aedes aegypti and dengue as influenced by weather and human behavior in San Juan, Puerto Rico. PLoS Negl. Trop. Dis. 5(12), e1378 (2011)

    Article  Google Scholar 

  25. Tipayamongkholgul, M., Fang, C.T., Klinchan, S., et al.: Effects of the El Niño-Southern Oscillation on dengue epidemics in Thailand, 1996–2005. BMC Public Health 9(1), 422 (2009)

    Article  Google Scholar 

  26. Earnest, A., Tan, S.B., Wildersmith, A.: Meteorological factors and El Niño southern oscillation are independently associated with dengue infections. Epidemiol. Infect. 140(7), 1244–1251 (2011)

    Article  Google Scholar 

  27. Jiang, Y., Zhu, G.: Prediction of dengue outbreak based on poisson regression and support vector machine. In: The 7th International Symposium on Computational Intelligence and Industrial Applications (ISCIIA2016), FM-GS1-01. Fuji Technology Press, Fuji (2016)

    Google Scholar 

Download references

Acknowledgement

This study was supported by Guangxi cloud computing and big data Collaborative Innovation Center (No: YD16E18).

Author information

Authors and Affiliations

  1. School of Electronic and Electrical Engineering, Guilin University of Electronic Technology, Guilin, 541004, China

    Ying Jiang & Guohun Zhu

  2. School of ITEE, The University of Queensland, Brisbane, QLD 4072, Australia

    Guohun Zhu

  3. Jiujiang Power Supply Branch, State Grid Electric Power Company, Jiangxi, China

    Ling Lin

Authors
  1. Ying Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guohun Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ling Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ying Jiang .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, United Kingdom

    Hujun Yin

  2. School of Electronic and Electrical Engineering, Nanjing University, Nanjiing, China

    Yang Gao

  3. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Songcan Chen

  4. Guilin University of Electronic Technology, Guilin, China

    Yimin Wen

  5. Guilin University of Electronic Technology, Guilin, China

    Guoyong Cai

  6. Guilin University of Electronic Technology, Guilin, China

    Tianlong Gu

  7. Beijing University of Posts and Telecommunications, Beijing, China

    Junping Du

  8. University of Seville, Seville, Spain

    Antonio J. Tallón-Ballesteros

  9. Southeast University, Nanjing, China

    Minling Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Jiang, Y., Zhu, G., Lin, L. (2017). Research of Dengue Fever Prediction in San Juan, Puerto Rico Based on a KNN Regression Model. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2017. IDEAL 2017. Lecture Notes in Computer Science(), vol 10585. Springer, Cham. https://doi.org/10.1007/978-3-319-68935-7_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-68935-7_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68934-0

  • Online ISBN: 978-3-319-68935-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation