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Forecast of Dengue Incidence Using Temperature and Rainfall

Author

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  • Yien Ling Hii
  • Huaiping Zhu
  • Nawi Ng
  • Lee Ching Ng
  • Joacim Rocklöv

Abstract

Introduction: An accurate early warning system to predict impending epidemics enhances the effectiveness of preventive measures against dengue fever. The aim of this study was to develop and validate a forecasting model that could predict dengue cases and provide timely early warning in Singapore. Methodology and Principal Findings: We developed a time series Poisson multivariate regression model using weekly mean temperature and cumulative rainfall over the period 2000–2010. Weather data were modeled using piecewise linear spline functions. We analyzed various lag times between dengue and weather variables to identify the optimal dengue forecasting period. Autoregression, seasonality and trend were considered in the model. We validated the model by forecasting dengue cases for week 1 of 2011 up to week 16 of 2012 using weather data alone. Model selection and validation were based on Akaike's Information Criterion, standardized Root Mean Square Error, and residuals diagnoses. A Receiver Operating Characteristics curve was used to analyze the sensitivity of the forecast of epidemics. The optimal period for dengue forecast was 16 weeks. Our model forecasted correctly with errors of 0.3 and 0.32 of the standard deviation of reported cases during the model training and validation periods, respectively. It was sensitive enough to distinguish between outbreak and non-outbreak to a 96% (CI = 93–98%) in 2004–2010 and 98% (CI = 95%–100%) in 2011. The model predicted the outbreak in 2011 accurately with less than 3% possibility of false alarm. Significance: We have developed a weather-based dengue forecasting model that allows warning 16 weeks in advance of dengue epidemics with high sensitivity and specificity. We demonstrate that models using temperature and rainfall could be simple, precise, and low cost tools for dengue forecasting which could be used to enhance decision making on the timing, scale of vector control operations, and utilization of limited resources. Author Summary: Without effective drugs or a vaccine, vector control remains the only method of controlling dengue fever outbreaks in Singapore. Based on our previous findings on the effects of weather on dengue cases and optimal timing for issuing dengue early warning in Singapore, the purpose of this study was to develop a dengue forecasting model that would provide early warning of a dengue outbreak several months in advance to allow sufficient time for effective control to be implemented. We constructed a statistical model using weekly mean temperature and rainfall. This involved 1) identifying the optimal lag period for forecasting dengue cases; 2) developing the model that described past dengue distribution patterns; 3) performing sensitivity tests to analyze whether the selected model could detect actual outbreaks. Finally, we used the selected model to forecast dengue cases from 2011–2012 week16 using weather data alone. Our model forecasted for a period of 16 weeks with high sensitivity in distinguishing between an outbreak and a non-outbreak. We conclude that weather can be an important factor for providing early warning of dengue epidemics, long term sustainability of forecast precision is challenging considering the complex dynamics of disease transmission.

Suggested Citation

  • Yien Ling Hii & Huaiping Zhu & Nawi Ng & Lee Ching Ng & Joacim Rocklöv, 2012. "Forecast of Dengue Incidence Using Temperature and Rainfall," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 6(11), pages 1-9, November.
  • Handle: RePEc:plo:pntd00:0001908
    DOI: 10.1371/journal.pntd.0001908
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    Citations

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    Cited by:

    1. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    2. Ting-Wu Chuang & Luis Fernando Chaves & Po-Jiang Chen, 2017. "Effects of local and regional climatic fluctuations on dengue outbreaks in southern Taiwan," PLOS ONE, Public Library of Science, vol. 12(6), pages 1-20, June.
    3. Jue Tao Lim & Borame Sue Dickens & Sun Haoyang & Ng Lee Ching & Alex R Cook, 2020. "Inference on dengue epidemics with Bayesian regime switching models," PLOS Computational Biology, Public Library of Science, vol. 16(5), pages 1-15, May.
    4. Shaowei Sang & Shaohua Gu & Peng Bi & Weizhong Yang & Zhicong Yang & Lei Xu & Jun Yang & Xiaobo Liu & Tong Jiang & Haixia Wu & Cordia Chu & Qiyong Liu, 2015. "Predicting Unprecedented Dengue Outbreak Using Imported Cases and Climatic Factors in Guangzhou, 2014," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 9(5), pages 1-12, May.
    5. Chen, Cathy W.S. & Liu, Feng-Chi & Pingal, Aljo Clair, 2023. "Integer-valued transfer function models for counts that show zero inflation," Statistics & Probability Letters, Elsevier, vol. 193(C).
    6. Teerawad Sriklin & Siriwan Kajornkasirat & Supattra Puttinaovarat, 2021. "Dengue Transmission Mapping with Weather-Based Predictive Model in Three Southernmost Provinces of Thailand," Sustainability, MDPI, vol. 13(12), pages 1-15, June.
    7. Baharuddin Baharuddin & Suhariningsih Suhariningsih & Brodjol Ulama, 2014. "Geographically Weighted Regression Modeling for Analyzing Spatial Heterogeneity on Relationship between Dengue Hemorrhagic Fever Incidence and Rainfall in Surabaya, Indonesia," Modern Applied Science, Canadian Center of Science and Education, vol. 8(3), pages 1-85, June.
    8. Shaowei Sang & Wenwu Yin & Peng Bi & Honglong Zhang & Chenggang Wang & Xiaobo Liu & Bin Chen & Weizhong Yang & Qiyong Liu, 2014. "Predicting Local Dengue Transmission in Guangzhou, China, through the Influence of Imported Cases, Mosquito Density and Climate Variability," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-10, July.

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