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Dengue outbreak and severity prediction: current methods and the future scope

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Abstract

Dengue virus (DENV) is the causative agent of dengue fever and severe dengue. Every year, millions of people are infected with this virus. There is no vaccine available for this disease. Dengue virus is present in four serologically varying strains, DENV 1, 2, 3, and 4, and each of these serotypes is further classified into various genotypes based on the geographic distribution and genetic variance. Mosquitoes play the role of vectors for this disease. Tropical countries and some temperate parts of the world witness outbreaks of dengue mainly during the monsoon (rainy) seasons. Several algorithms have been developed to predict the occurrence and prognosis of dengue disease. These algorithms are mainly based on epidemiological data, climate factors, and online search patterns in the infected area. Most of these algorithms are based on either machine learning or deep learning techniques. We summarize the different software tools available for predicting the outbreaks of dengue based on the aforementioned factors, briefly outline the methodology used in these algorithms, and provide a comprehensive list of programs available for the same in this article.

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Author notes

  1. Bibhudutta Mishra

    Present address: Department of Zoology, Centurion University of Technology and Management, Bhubaneswar campus, Jatni, Khurda, Odisha, 752050, India

Authors and Affiliations

  1. Department of Biological Sciences, BITS Pilani K K Birla Goa Campus, Zuarinagar, Goa, 403726, India

    Madhulika Balakumar, Harshitha Reddy Vontela, Vallari Vivek Shinde, Vaibhav Kulshrestha, Bibhudutta Mishra & Raviprasad Aduri

Authors
  1. Madhulika Balakumar
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  2. Harshitha Reddy Vontela
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  3. Vallari Vivek Shinde
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  4. Vaibhav Kulshrestha
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  5. Bibhudutta Mishra
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  6. Raviprasad Aduri
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Correspondence to Raviprasad Aduri.

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Balakumar, M., Vontela, H.R., Shinde, V.V. et al. Dengue outbreak and severity prediction: current methods and the future scope. VirusDis. 33, 125–131 (2022). https://doi.org/10.1007/s13337-022-00767-x

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