This paper is in the following e-collection/theme issue:

JMIR Theme Issue: COVID-19 Special Issue (2452) Machine Learning (1465) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1517) Infectious Diseases (non-STD/STI) (1575) Outbreak and Pandemic Preparedness and Management (1727) Artificial Intelligence (1371) Innovative Methods in Public Health and Surveillance (332) Surveillance Systems (462)

Published on in Vol 23, No 6 (2021): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/24285, first published .
Real-time Prediction of the Daily Incidence of COVID-19 in 215 Countries and Territories Using Machine Learning: Model Development and Validation

Real-time Prediction of the Daily Incidence of COVID-19 in 215 Countries and Territories Using Machine Learning: Model Development and Validation

Real-time Prediction of the Daily Incidence of COVID-19 in 215 Countries and Territories Using Machine Learning: Model Development and Validation

Authors of this article:

Yuanyuan Peng1 Author Orcid Image ;   Xinjian Chen2 Author Orcid Image ;   Yibiao Rong3 Author Orcid Image ;   Chi Pui Pang2, 4 Author Orcid Image ;   Xinjian Chen1 Author Orcid Image ;   Haoyu Chen2 Author Orcid Image
Article Authors Cited by (15) Tweetations (3) Metrics

Journals

  1. Ma M. COVID-19 concerns in cyberspace predict human reduced dispersal in the real world: Meta-regression analysis of time series relationships across American states and 115 countries/territories. Computers in Human Behavior 2022;127:107059 View
  2. Segall R, Sankarasubbu V. Survey of Recent Applications of Artificial Intelligence for Detection and Analysis of COVID-19 and Other Infectious Diseases. International Journal of Artificial Intelligence and Machine Learning 2022;12(2):1 View
  3. Yu T, Zhang Z, Liu M, Zhao Z, Shi Z, Zhang S. Prediction of the wave–current forces acting on a composite bucket foundation using machine learning method. Ocean Engineering 2022;250:111068 View
  4. Pakhchanian H, Raiker R, Kardeş S, Bilal M, Alam K, Khan A, Hutson W, Thakkar S, Singh S. Impact of COVID-19 on interest in hepato-pancreato-biliary diseases. Environmental Science and Pollution Research 2022;29(4):5771 View
  5. Jin W, Dong S, Yu C, Luo Q. A data-driven hybrid ensemble AI model for COVID-19 infection forecast using multiple neural networks and reinforced learning. Computers in Biology and Medicine 2022;146:105560 View
  6. Saegner T, Austys D. Forecasting and Surveillance of COVID-19 Spread Using Google Trends: Literature Review. International Journal of Environmental Research and Public Health 2022;19(19):12394 View
  7. Athanasiou M, Fragkozidis G, Zarkogianni K, Nikita K. Long Short-term Memory–Based Prediction of the Spread of Influenza-Like Illness Leveraging Surveillance, Weather, and Twitter Data: Model Development and Validation. Journal of Medical Internet Research 2023;25:e42519 View
  8. Kellner D, Lowin M, Hinz O. Improved healthcare disaster decision-making utilizing information extraction from complementary social media data during the COVID-19 pandemic. Decision Support Systems 2023;172:113983 View
  9. Qasem S. A novel honey badger algorithm with multilayer perceptron for predicting COVID-19 time series data. The Journal of Supercomputing 2024;80(3):3943 View
  10. Clark E, Neumann S, Hopkins S, Kostopoulos A, Hagerman L, Dobbins M. Changes to Public Health Surveillance Methods Due to the COVID-19 Pandemic: Scoping Review. JMIR Public Health and Surveillance 2024;10:e49185 View
  11. Ariza-Colpas P, Piñeres-Melo M, Urina-Triana M, Barceló-Martinez E, Barceló-Castellanos C, Roman F. Machine Learning Applied to the Analysis of Prolonged COVID Symptoms: An Analytical Review. Informatics 2024;11(3):48 View
  12. Wang J, Yang Z, Chen C, Yao G, Wan X, Bao S, Ding J, Wang L, Jiang H. MPEK: a multitask deep learning framework based on pretrained language models for enzymatic reaction kinetic parameters prediction. Briefings in Bioinformatics 2024;25(5) View
  13. Arai T, Tsubaki H, Wakano A, Shimizu Y. Association Between School-Related Google Trends Search Volume and Suicides Among Children and Adolescents in Japan During 2016-2020: Retrospective Observational Study With a Time-Series Analysis. Journal of Medical Internet Research 2024;26:e51710 View

Books/Policy Documents

  1. Segall R. Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning. View