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GeoHealth - 2022 - Ogunjo - Predicting COVID‐19 Cases From Atmospheric Parameters Using Machine Learning Approach.pdf 505,06KB
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1000 Titel
  • Predicting COVID-19 Cases From Atmospheric Parameters Using Machine Learning Approach
1000 Autor/in
  1. OGUNJO, Samuel |
  2. Fuwape, Ibiyinka |
  3. Rabiu, A Babatunde |
1000 Erscheinungsjahr 2022
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2022-03-19
1000 Erschienen in
1000 Quellenangabe
  • 6(4):e2021GH000509
1000 Copyrightjahr
  • 2022
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1029/2021GH000509 |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • The dynamical nature of COVID-19 cases in different parts of the world requires robust mathematical approaches for prediction and forecasting. In this study, we aim to (a) forecast future COVID-19 cases based on past infections, (b) predict current COVID-19 cases using PM2.5, temperature, and humidity data, using four different machine learning classifiers (Decision Tree, K-nearest neighbor, Support Vector Machine, and Random Forest). Based on RMSE values, k-nearest neighbor and support vector machine algorithms were found to be the best for predicting future incidences of COVID-19 based on past histories. From the RMSE values obtained, temperature was found to be the best predictor for number of COVID-19 cases, followed by relative humidity. Decision tree models was found to perform poorly in the prediction of COVID-19 cases considering particulate matter and atmospheric parameters as predictors. Our results suggests the possibility of predicting virus infection using machine learning. This will guide policy makers in proactive monitoring and control.
1000 Sacherschließung
lokal deep learning
lokal pandemic
gnd 1206347392 COVID-19
lokal machine learning
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://orcid.org/0000-0002-6986-1939|https://orcid.org/0000-0001-7525-9293|https://orcid.org/0000-0002-2734-5389
1000 Hinweis
  • This article also appears in: The COVID-19 pandemic and environmental conditions in Africa
1000 Label
1000 Förderer
  1. Federal Government of Nigeria |
1000 Fördernummer
  1. -
1000 Förderprogramm
  1. -
1000 Dateien
  1. Predicting COVID-19 Cases From Atmospheric Parameters Using Machine Learning Approach
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer Federal Government of Nigeria |
    1000 Förderprogramm -
    1000 Fördernummer -
1000 Objektart article
1000 Beschrieben durch
1000 @id frl:6439787.rdf
1000 Erstellt am 2023-01-25T12:59:05.023+0100
1000 Erstellt von 286
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1000 Bearbeitet von 337
1000 Zuletzt bearbeitet 2023-12-14T12:35:55.230+0100
1000 Objekt bearb. Thu Dec 14 12:35:54 CET 2023
1000 Vgl. frl:6439787
1000 Oai Id
  1. oai:frl.publisso.de:frl:6439787 |
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