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Computational and Mathematical Methods in Medicine
Volume 2012, Article ID 758674, 6 pages
http://dx.doi.org/10.1155/2012/758674
Research Article

Comparing Statistical Models to Predict Dengue Fever Notifications

1Centre for Quantitative Medicine, Office of Clinical Sciences, Duke-NUS Graduate Medical School Singapore, Singapore 169857
2Tan Tock Seng Hospital, Singapore 308433
3Institute of Public Health, University of Heidelberg, Germany
4National University of Singapore, Singapore 119077
5University of Leicester, UK
6University of Sheffield, UK

Received 16 September 2011; Revised 7 December 2011; Accepted 8 December 2011

Academic Editor: Chris Bauch

Copyright © 2012 Arul Earnest et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [18 citations]

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  • Hai-Qin Yang, Dai-Yu Hu, Ying Liu, Run-Hua Wang, and Jing Yi, “Application of ARIMA model in forecasting monthly incidence of smear-positive tuberculosis,” Academic Journal of Second Military Medical University, vol. 34, no. 9, pp. 980–984, 2013. View at Publisher · View at Google Scholar
  • Fenyang Tang, Yuejia Cheng, Changjun Bao, Jianli Hu, Wendong Liu, Qi Liang, Ying Wu, Jessie Norris, Zhihang Peng, Rongbin Yu, Hongbing Shen, and Feng Chen, “Spatio-Temporal Trends and Risk Factors for Shigella from 2001 to 2011 in Jiangsu Province, People's Republic of China,” Plos One, vol. 9, no. 1, 2014. View at Publisher · View at Google Scholar
  • Tian-Yong Wu, Qing Zeng, Qin Li, Shiwei Liu, Han Zhao, and Meng Yu, “ARIMA model of data of hepatitis C report of China from 2004 to 2012 and trend prediction,” Journal of Shanghai Jiaotong University (Medical Science), vol. 34, no. 5, pp. 705–709, 2014. View at Publisher · View at Google Scholar
  • Kraisak Kesorn, Phatsavee Ongruk, Jakkrawarn Chompoosri, Atchara Phumee, Usavadee Thavara, Apiwat Tawatsin, and Padet Siriyasatien, “Morbidity Rate Prediction of Dengue Hemorrhagic Fever (DHF) Using the Support Vector Machine and the Aedes aegypti Infection Rate in Similar Climates ,” Plos One, vol. 10, no. 5, 2015. View at Publisher · View at Google Scholar
  • Yilan Lin, Min Chen, Guowei Chen, Xiaoqing Wu, and Tianquan Lin, “Application of an autoregressive integrated moving average model for predicting injury mortality in Xiamen, China,” BMJ Open, vol. 5, no. 12, pp. e008491, 2015. View at Publisher · View at Google Scholar
  • Choo-Yee Ting, and Chiung Ching Ho, “Time series analysis and forecasting of dengue using open data,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9429, pp. 51–63, 2015. View at Publisher · View at Google Scholar
  • Tao Wang, Yunping Zhou, Ling Wang, Zhenshui Huang, Feng Cui, and Shenyong Zhai, “Using autoregressive integrated moving average model to predict the incidence of hemorrhagic fever with renal syndrome in Zibo, China, 2004–2014,” Japanese Journal of Infectious Diseases, 2015. View at Publisher · View at Google Scholar
  • Andrew Lo, Waiming Kong, Xu Liu, Suet-Yheng Kok, Grace Yap, Kim-Sung Lee, Yuan Shi, Christopher Kuan Yew Chin, Lee Ching Ng, Alex R. Cook, Jayanthi Rajarethinam, Shaohong Liang, Chee-Seng Chong, and Sharon S. Y. Tan, “Three-month real-time dengue forecast models: An early warning system for outbreak alerts and policy decision support in Singapore,” Environmental Health Perspectives, vol. 124, no. 9, pp. 1369–1375, 2016. View at Publisher · View at Google Scholar
  • Michael A. Johansson, Nicholas G. Reich, Aditi Hota, John S. Brownstein, and Mauricio Santillana, “Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico,” Scientific Reports, vol. 6, no. 1, 2016. View at Publisher · View at Google Scholar
  • Padet Siriyasatien, Atchara Phumee, Phatsavee Ongruk, Katechan Jampachaisri, and Kraisak Kesorn, “Analysis of significant factors for dengue fever incidence prediction,” BMC Bioinformatics, vol. 17, no. 1, 2016. View at Publisher · View at Google Scholar
  • Daniel Adyro Martínez-Bello, Antonio López-Quílez, and Alexander Torres-Prieto, “Bayesian dynamic modeling of time series of dengue disease case counts,” PLoS Neglected Tropical Diseases, vol. 11, no. 7, 2017. View at Publisher · View at Google Scholar
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  • Yirong Chen, Janet Hui Yi Ong, Jayanthi Rajarethinam, Grace Yap, Lee Ching Ng, and Alex R. Cook, “Neighbourhood level real-time forecasting of dengue cases in tropical urban Singapore,” BMC Medicine, vol. 16, no. 1, 2018. View at Publisher · View at Google Scholar
  • Ji-Min Sun, Liang Lu, Ke-Ke Liu, Jun Yang, Hai-Xia Wu, and Qi-Yong Liu, “Forecast of severe fever with thrombocytopenia syndrome incidence with meteorological factors,” Science of The Total Environment, vol. 626, pp. 1188–1192, 2018. View at Publisher · View at Google Scholar
  • Ionara Santos Siqueira, Joaquim Carlos Barbosa Queiroz, Mario Miguel Amin, and Renata Kelen Cardoso Câmara, “A Relação da Incidência de Casos de Dengue com a Precipitação na Área Urbana de Belém-PA, 2007 a 2011, Através de Modelos Multivariados de Séries Temporais,” Revista Brasileira de Meteorologia, vol. 33, no. 2, pp. 380–389, 2018. View at Publisher · View at Google Scholar
  • D Aldila, N Situngkir, and K Nareswari, “Understanding resistant effect of mosquito on fumigation strategy in dengue control program,” Journal of Physics: Conference Series, vol. 948, pp. 012066, 2018. View at Publisher · View at Google Scholar
  • L.S. Jayashree, Lakshmi R. Devi, Nikolaos Papandrianos, and Elpiniki I. Papageorgiou, “Application of Fuzzy Cognitive Map for geospatial dengue outbreak risk prediction of tropical regions of Southern India,” Intelligent Decision Technologies, pp. 1–20, 2018. View at Publisher · View at Google Scholar
  • Jimin Sun, Jun Yang, Keke Liu, Qiyong Liu, Liang Lu, and Haixia Wu, “Spatiotemporal patterns of severe fever with thrombocytopenia syndrome in China, 2011–2016,” Ticks and Tick-borne Diseases, 2018. View at Publisher · View at Google Scholar