Predicting COVID‐19 Cases From Atmospheric Parameters Using Machine Learning Approach-Reference-Cited by-同舟云学术

Predicting COVID‐19 Cases From Atmospheric Parameters Using Machine Learning Approach

Published:2022-04 Issue:4 Volume:6 Page:
ISSN:2471-1403
Container-title:GeoHealth
language:en
Short-container-title:GeoHealth

Author:

Ogunjo S. T.¹^ORCID,Fuwape I. A.¹²^ORCID,Rabiu A. B.³^ORCID

Affiliation:

1. Department of Physics Federal University of Technology Akure Akure Nigeria

2. Office of the Vice Chancellor Michael and Cecilia Ibru University Ughelli Nigeria

3. Centre for Atmospheric Research National Space and Research Development Agency Anyigba Nigeria

Publisher

American Geophysical Union (AGU)

Subject

Health, Toxicology and Mutagenesis,Management, Monitoring, Policy and Law,Public Health, Environmental and Occupational Health,Pollution,Waste Management and Disposal,Water Science and Technology,Epidemiology,Global and Planetary Change

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2021GH000509

Reference52 articles.

1. Anis A.(2020).The effect of temperature upon transmission of Covid‐19: Australia and Egypt case study.Available at SSRN 3567639.

2. Stability analysis and numerical simulation of SEIR model for pandemic COVID-19 spread in Indonesia

3. Spatial distribution and temporal variability of Harmattan dust haze in sub-Sahel West Africa

4. A new COVID-19 detection method from human genome sequences using CpG island features and KNN classifier

5. Forecasting of COVID-19 using deep layer Recurrent Neural Networks (RNNs) with Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTM) cells

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