Prediction of COVID’19 Outbreak by Using ML-Based Time-Series Forecasting Approach
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-030-66218-9_33
Reference13 articles.
1. Alonso, A. K., Bressan, S., O’Shea, A., Sakellarios, M., laosKoresis, N., Solis, A., Santoni, M., & Leonardo. (2020). COVID-19, aftermath, impacts, and hospitality firms: An international perspective. International Journal of Hospitality Management, 91, 102654. https://doi.org/10.1016/j.ijhm.2020.102654
2. Cardil, A., & de-Miguel, S. (2020). COVID-19 jeopardizes the response to coming natural disasters. Safety Science, 130, 104861. https://doi.org/10.1016/j.ssci.2020.104861.
3. Filimonau, V., Derqui, B., & Matute, J. (2020). The COVID-19 pandemic and organizational commitment of senior hotel managers. International Journal of Hospitality Management, 91, 102659. https://doi.org/10.1016/j.ijhm.2020.102659. Epub 2020 Aug 31. PMID: 32904709; PMCID: PMC7458044.
4. Hawas, M. (2020). Generated time-series prediction data of COVID-19’s daily infections in Brazil by using recurrent neural networks. Data Brief, 32. 106175. https://doi.org/10.1016/j.dib.2020.106175. Epub 2020 Aug 19. PMID: 32839733; PMCID: PMC7437445.
5. Hayashi, T., Yaegashi, N., Konishi, I. (2020). COVID-19 era, preventive effect of no going out against co-infection of the seasonal influenza virus and SARS-CoV-2. Cold Spring Harbor Laboratory Press. 09.27.20202739.
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