Improving performance of deep learning predictive models for COVID-19 by incorporating environmental parameters
- 8 April 2022
- journal article
- research article
- Published by Elsevier in Gondwana Research
- Vol. 114, 69-77
Abstract
No abstract availableKeywords
This publication has 46 references indexed in Scilit:
- Effects of temperature and humidity on the daily new cases and new deaths of COVID-19 in 166 countriesScience of The Total Environment, 2020
- Prediction for the spread of COVID-19 in India and effectiveness of preventive measuresScience of The Total Environment, 2020
- Factors determining the diffusion of COVID-19 and suggested strategy to prevent future accelerated viral infectivity similar to COVIDScience of The Total Environment, 2020
- Time series forecasting of COVID-19 transmission in Canada using LSTM networksChaos, Solitons & Fractals, 2020
- Forecasting of COVID-19 Cases and Deaths Using ARIMA ModelsmedRxiv, 2020
- Correlation between weather and Covid-19 pandemic in Jakarta, IndonesiaScience of The Total Environment, 2020
- SEIR and Regression Model based COVID-19 outbreak predictions in IndiamedRxiv, 2020
- Application of the ARIMA model on the COVID-2019 epidemic datasetData in Brief, 2020
- Learning to Forget: Continual Prediction with LSTMNeural Computation, 2000
- Long Short-Term MemoryNeural Computation, 1997