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Surveillance of Dengue Fever Virus: A Review of Epidemiological Models and Early Warning Systems

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  • Vanessa Racloz
  • Rebecca Ramsey
  • Shilu Tong
  • Wenbiao Hu

Abstract

Dengue fever affects over a 100 million people annually hence is one of the world's most important vector-borne diseases. The transmission area of this disease continues to expand due to many direct and indirect factors linked to urban sprawl, increased travel and global warming. Current preventative measures include mosquito control programs, yet due to the complex nature of the disease and the increased importation risk along with the lack of efficient prophylactic measures, successful disease control and elimination is not realistic in the foreseeable future. Epidemiological models attempt to predict future outbreaks using information on the risk factors of the disease. Through a systematic literature review, this paper aims at analyzing the different modeling methods and their outputs in terms of acting as an early warning system. We found that many previous studies have not sufficiently accounted for the spatio-temporal features of the disease in the modeling process. Yet with advances in technology, the ability to incorporate such information as well as the socio-environmental aspect allowed for its use as an early warning system, albeit limited geographically to a local scale. Author Summary: Despite mass vaccination campaigns and large scaled improvements in global surveillance, infectious diseases are a worldwide problem. In recent years, the ability to use models as a tool to help visualize, understand and combat infectious diseases has become more feasible and reliable. In this context, modelling focuses on transmission patterns between the different animal, human or vector components as well as including parameters which affect these pathways such as environmental, climatic or geographic ones. The output of these models can help in decision making processes concerning control purposes, surveillance methods and hopefully also as good predictive tools. Prediction forms part of surveillance systems, and more specifically in early warning systems. It is the timely collection and analysis of data as well as the use of risk-based assessments in order to aid in prompt health interventions such as movement control, vaccination campaigns or the distribution of important information. Early warning systems for vector borne diseases are especially complex due to the involvement of various factors originating from the human, animal and insect sector as well the disease itself. The authors investigate the variety and depth of available models for dengue fever surveillance and their use as early warning tools.

Suggested Citation

  • Vanessa Racloz & Rebecca Ramsey & Shilu Tong & Wenbiao Hu, 2012. "Surveillance of Dengue Fever Virus: A Review of Epidemiological Models and Early Warning Systems," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 6(5), pages 1-9, May.
  • Handle: RePEc:plo:pntd00:0001648
    DOI: 10.1371/journal.pntd.0001648
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    References listed on IDEAS

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    1. Derek A.T. Cummings & Rafael A. Irizarry & Norden E. Huang & Timothy P. Endy & Ananda Nisalak & Kumnuan Ungchusak & Donald S. Burke, 2004. "Travelling waves in the occurrence of dengue haemorrhagic fever in Thailand," Nature, Nature, vol. 427(6972), pages 344-347, January.
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    1. Barbara Häsler & Paula Dominguez-Salas & Kimberly Fornace & Maria Garza & Delia Grace & Jonathan Rushton, 2017. "Where food safety meets nutrition outcomes in livestock and fish value chains: a conceptual approach," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 9(5), pages 1001-1017, October.
    2. Carlos A Bravo-Vega & Juan M Cordovez & Camila Renjifo-Ibáñez & Mauricio Santos-Vega & Mahmood Sasa, 2019. "Estimating snakebite incidence from mathematical models: A test in Costa Rica," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 13(12), pages 1-16, December.
    3. Daniel Adyro Martínez-Bello & Antonio López-Quílez & Alexander Torres-Prieto, 2017. "Bayesian dynamic modeling of time series of dengue disease case counts," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 11(7), pages 1-19, July.
    4. Kraisak Kesorn & Phatsavee Ongruk & Jakkrawarn Chompoosri & Atchara Phumee & Usavadee Thavara & Apiwat Tawatsin & Padet Siriyasatien, 2015. "Morbidity Rate Prediction of Dengue Hemorrhagic Fever (DHF) Using the Support Vector Machine and the Aedes aegypti Infection Rate in Similar Climates and Geographical Areas," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-16, May.
    5. David C Farrow & Logan C Brooks & Sangwon Hyun & Ryan J Tibshirani & Donald S Burke & Roni Rosenfeld, 2017. "A human judgment approach to epidemiological forecasting," PLOS Computational Biology, Public Library of Science, vol. 13(3), pages 1-19, March.
    6. Panja, Madhurima & Chakraborty, Tanujit & Nadim, Sk Shahid & Ghosh, Indrajit & Kumar, Uttam & Liu, Nan, 2023. "An ensemble neural network approach to forecast Dengue outbreak based on climatic condition," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    7. Laith Hussain-Alkhateeb & Tatiana Rivera Ramírez & Axel Kroeger & Ernesto Gozzer & Silvia Runge-Ranzinger, 2021. "Early warning systems (EWSs) for chikungunya, dengue, malaria, yellow fever, and Zika outbreaks: What is the evidence? A scoping review," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 15(9), pages 1-25, September.
    8. Ayu Rahayu & Utari Saraswati & Endah Supriyati & Dian Aruni Kumalawati & Rio Hermantara & Anwar Rovik & Edwin Widyanto Daniwijaya & Iva Fitriana & Sigit Setyawan & Riris Andono Ahmad & Dwi Satria Ward, 2019. "Prevalence and Distribution of Dengue Virus in Aedes aegypti in Yogyakarta City before Deployment of Wolbachia Infected Aedes aegypti," IJERPH, MDPI, vol. 16(10), pages 1-12, May.
    9. Oswaldo Santos Baquero & Lidia Maria Reis Santana & Francisco Chiaravalloti-Neto, 2018. "Dengue forecasting in São Paulo city with generalized additive models, artificial neural networks and seasonal autoregressive integrated moving average models," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-12, April.
    10. Wenting Yang & Jiantong Zhang & Ruolin Ma, 2020. "The Prediction of Infectious Diseases: A Bibliometric Analysis," IJERPH, MDPI, vol. 17(17), pages 1-19, August.

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