Access the full text.
Sign up today, get DeepDyve free for 14 days.
Leslie Chandrakantha (2019)
Risk Prediction Model for Dengue Transmission Based on Climate Data: Logistic Regression ApproachStats
M. Islam, C. Quispe, Jesús Herrera-Bravo, Chandan Sarkar, Rohit Sharma, Neha Garg, Larry Fredes, M. Martorell, M. Alshehri, J. Sharifi‐Rad, S. Daştan, D. Calina, R. Alsafi, Saad Alghamdi, G. Batiha, N. Cruz-Martins (2021)
Production, Transmission, Pathogenesis, and Control of Dengue Virus: A Literature-Based Undivided PerspectiveBioMed Research International, 2021
A. Ramadona, Lutfan Lazuardi, Yien Hii, Åsa Holmner, H. Kusnanto, J. Rocklöv (2016)
Prediction of Dengue Outbreaks Based on Disease Surveillance and Meteorological DataPLoS ONE, 11
(2014)
Monitering dengue outbreaks using online data: University of North Texas
L. Jayashree, R. Devi, Nikolaos Papandrianos, E. Papageorgiou (2018)
Application of Fuzzy Cognitive Map for geospatial dengue outbreak risk prediction of tropical regions of Southern IndiaIntell. Decis. Technol., 12
Felestin Nejad, Kasturi Varathan (2019)
Identification of significant climatic risk factors and machine learning models in dengue outbreak predictionBMC Medical Informatics and Decision Making, 21
Y. Lai (2018)
The climatic factors affecting dengue fever outbreaks in southern Taiwan: an application of symbolic data analysisBioMedical Engineering OnLine, 17
Sudipta Roy, S. Bhattacharjee (2021)
Dengue Virus: Epidemiology, Biology and Disease Aetiology.Canadian journal of microbiology
Wiwik Anggraeni, S. Sumpeno, E. Yuniarno, R. Rachmadi, Agustinus Gumelar, M. Purnomo (2020)
Prediction of Dengue Fever Outbreak Based on Climate Factors Using Fuzzy-Logistic Regression2020 International Seminar on Intelligent Technology and Its Applications (ISITIA)
Tanujit Chakraborty, Swarup Chattopadhyay, I. Ghosh (2018)
Forecasting dengue epidemics using a hybrid methodologybioRxiv
Antoine Adde, P. Roucou, M. Mangeas, Vanessa Ardillon, J. Desenclos, D. Rousset, R. Girod, S. Briolant, P. Quénel, C. Flamand (2016)
Predicting Dengue Fever Outbreaks in French Guiana Using Climate IndicatorsPLoS Neglected Tropical Diseases, 10
Jiucheng Xu, Keqiang Xu, Zhichao Li, F. Meng, Taotian Tu, Lei Xu, Qiyong Liu (2020)
Forecast of Dengue Cases in 20 Chinese Cities Based on the Deep Learning MethodInternational Journal of Environmental Research and Public Health, 17
P. Bhatt, S. Sabeena, M. Varma, G. Arunkumar (2020)
Current Understanding of the Pathogenesis of Dengue Virus InfectionCurrent Microbiology, 78
S Bhatt (2013)
504Nature, 496
Y. Choi, C. Tang, L. McIver, M. Hashizume, Vibol Chan, R. Abeyasinghe, Steven Iddings, R. Huy (2016)
Effects of weather factors on dengue fever incidence and implications for interventions in CambodiaBMC Public Health, 16
K. Dharmawardana, J. Lokuge, P. Dassanayake, M. Sirisena, M. Fernando, Amal Perera, Sriganesh Lokanathan (2017)
Predictive model for the dengue incidences in Sri Lanka using mobile network big data2017 IEEE International Conference on Industrial and Information Systems (ICIIS)
William Caicedo-Torres, Diana Montes-Grajales, Wendy Miranda-Castro, Mary Fennix-Agudelo, Nicolas Agudelo-Herrera (2017)
Kernel-Based Machine Learning Models for the Prediction of Dengue and Chikungunya Morbidity in Colombia
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.
VirusDisease – Springer Journals
Published: Jun 1, 2022
Keywords: Dengue; Climate factors; Machine learning; Deep learning; Social network; Epidemiology data
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.