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David M Vu et al. PLOS Glob Public Health 2023 3(7) e0001950
A machine learning model to assess potential misdiagnosed dengue hospitalization.
Claudia Yang Santos et al. Heliyon 2023 9(6) e16634
DDPM: A Dengue Disease Prediction and Diagnosis Model Using Sentiment Analysis and Machine Learning Algorithms.
Gaurav Gupta et al. Diagnostics (Basel, Switzerland) 2023 13(6)
Detecting dengue fever in children using online Rasch analysis to develop algorithms for parents: An APP development and usability study.
Ting-Yun Hu et al. Medicine 2023 102(13) e33296
Learning meaningful latent space representations for patient risk stratification: Model development and validation for dengue and other acute febrile illness.
Bernard Hernandez et al. Frontiers in digital health 2023 51057467
WebGIS-Based Real-Time Surveillance and Response System for Vector-Borne Infectious Diseases.
Momna Javaid et al. International journal of environmental research and public health 2023 20(4)
A systematic review of dengue outbreak prediction models: Current scenario and future directions.
Xing Yu Leung et al. PLoS neglected tropical diseases 2023 17(2) e0010631
Machine Learning-Based Detection of Dengue from Blood Smear Images Utilizing Platelet and Lymphocyte Characteristics.
Mayrose Hilda et al. Diagnostics (Basel, Switzerland) 2023 13(2)
COVID spurs boom in genome sequencing for infectious diseases
S Mallapaty, Nature, December 15, 2022
Dengue Prediction in Latin America Using Machine Learning and the One Health Perspective: A Literature Review.
Cabrera Maritza et al. Tropical medicine and infectious disease 2022 7(10)
Predicting infectious disease for biopreparedness and response: A systematic review of machine learning and deep learning approaches.
Keshavamurthy Ravikiran et al. One health (Amsterdam, Netherlands) 2022 15100439
Role of artificial intelligence-internet of things (AI-IoT) based emerging technologies in the public health response to infectious diseases in Bangladesh.
Rahman Md Siddikur et al. Parasite epidemiology and control 2022 18e00266
A clinical decision-support system for dengue based on fuzzy cognitive maps.
Hoyos William et al. Health care management science 2022
Deep learning models for forecasting dengue fever based on climate data in Vietnam.
Hau Nguyen Van et al. PLoS neglected tropical diseases 2022 16(6) e0010509
Machine Learning Based Forecast of Dengue Fever in Brazilian Cities using Epidemiological and Meteorological Variables.
Roster Kirstin et al. American journal of epidemiology 2022
Data-driven computational intelligence applied to dengue outbreak forecasting: a case study at the scale of the city of Natal, RN-Brazil.
Sanchez-Gendriz Ignacio et al. Scientific reports 2022 12(1) 6550
Infectious diseases prevention and control using an integrated health big data system in China.
Zhou Xudong et al. BMC infectious diseases 2022 22(1) 344
The Diagnosis of Dengue in Patients Presenting With Acute Febrile Illness Using Supervised Machine Learning and Impact of Seasonality.
Ming Damien K et al. Frontiers in digital health 2022 4849641
An 8-gene machine learning model improves clinical prediction of severe dengue progression
YE Liu et al, Genome Medicine, March 28, 2022
Influenza, dengue and common cold detection using LSTM with fully connected neural network and keywords selection.
Nadda Wanchaloem et al. BioData mining 2022 15(1) 5
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Disclaimer: Articles listed in the Public Health Knowledge Base are selected by Public Health Genomics Branch to provide current awareness of the literature and news. Inclusion in the update does not necessarily represent the views of the Centers for Disease Control and Prevention nor does it imply endorsement of the article's methods or findings. CDC and DHHS assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by CDC or DHHS. Opinion, findings and conclusions expressed by the original authors of items included in the update, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of CDC or DHHS. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by CDC or DHHS.