A Bayesian machine learning approach for spatio-temporal prediction of COVID-19 cases

Poshan Niraula; Jorge Mateu; Somnath Chaudhuri

doi:10.1007/s00477-021-02168-w

ScienceGate Book Chapters

JOURNAL ARTICLE

A Bayesian machine learning approach for spatio-temporal prediction of COVID-19 cases

Poshan Niraula Jorge Mateu Somnath Chaudhuri

Year: 2022 Journal: Stochastic Environmental Research and Risk Assessment Vol: 36 (8)Pages: 2265-2283 Publisher: Springer Science+Business Media

DOI: 10.1007/s00477-021-02168-w

Get Full-Text PDF Get Analytical Report

Keywords:

Computer science Artificial intelligence Computational intelligence Machine learning Artificial neural network Bayesian probability sort Bayesian network Coronavirus disease 2019 (COVID-19) Probabilistic logic Infectious disease (medical specialty) Data mining Disease

Metrics

Cited By

2.44

FWCI (Field Weighted Citation Impact)

Refs

0.84

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

COVID-19 epidemiological studies

Physical Sciences → Mathematics → Modeling and Simulation

Data-Driven Disease Surveillance

Health Sciences → Medicine → Epidemiology

Influenza Virus Research Studies

Health Sciences → Medicine → Epidemiology

A Bayesian machine learning approach for spatio-temporal prediction of COVID-19 cases

Metrics

Citation History

Topics

Related Documents

Bayesian spatio-temporal disease mapping of COVID-19 cases in Bangladesh

Supervised Machine Learning Approach for the Prediction of COVID-19 Cases

Spatio-Temporal Prediction with Machine Learning

Spatio-temporal risk prediction of leptospirosis: A machine-learning-based approach

Covid-19 Prediction Analysis Using Machine Learning Approach