References
- Hasan, K. T., Rahman, M. M., Ahmmed, M., Chowdhury, A. A., & Islam, M. K. (2021). 4P model for dynamic prediction of COVID-19: a statistical and machine learning approach. Cognitive Computation, 1-14.
- Alsulami, M. D., Abu-Zinadah, H., & Ibrahim, A. H. (2021). Machine Learning Model and Statistical Methods for COVID-19 Evolution Prediction. Wireless Communications and Mobile Computing, 2021. doi: 10.1155/2021/4840488
- Singh, H., & Bawa, S. (2021). Predicting COVID-19 statistics using machine learning regression model: Li-MuLi-Poly. Multimedia Systems, 1-8.
- Moulaei, K., Shanbehzadeh, M., Mohammadi-Taghiabad, Z., & Kazemi-Arpanahi, H. (2022). Comparing machine learning algorithms for predicting COVID-19 mortality. BMC medical informatics and decision making, 22(1), 1-12. doi: 10.1186/s12911-021-01695-4
- Noy, O., Coster, D., Metzger, M., Atar, I., Shenhar-Tsarfaty, S., Berliner, S., … & Shamir, R. (2022). A machine learning model for predicting deterioration of COVID-19 inpatients. Scientific reports, 12(1), 1-9. doi: 10.1038/s41598-022-05822-7
- Laatifi, M., Douzi, S., Bouklouz, A., Ezzine, H., Jaafari, J., Zaid, Y., … & Naciri, M. (2022). Machine learning approaches in Covid-19 severity risk prediction in Morocco. Journal of Big Data, 9(1), 1-21. doi: 10.1186/s40537-021-00557-0
- Stachel, A., Daniel, K., Ding, D., Francois, F., Phillips, M., & Lighter, J. (2021). Development and validation of a machine learning model to predict mortality risk in patients with COVID-19. BMJ Health & Care Informatics, 28(1).
- T. H. H. Aldhyani, M. Alrasheed, M. H. Al-Adaileh, A. A. Alqarni, M. Y. Alzahrani (2021). Deep learning and holt-trend algorithms for predicting covid-19 pandemic. Computers, Materials & Continua, 67(2), 2141–2160. doi: 10.32604/cmc.2021.014498
- James, J. (2021). Covid-19 Future Predictions using Machine Learning Algorithms. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(11), 6292-6302.
- Gupta, A., Jain, V., & Singh, A. (2021). Stacking Ensemble-Based Intelligent Machine Learning Model for Predicting Post-COVID-19 Complications. New Generation Computing, 1-21.
- Ortigoza, G., & Zapata, U. (2021, October). Covid-19 Projections: A Simple Machine Learning Approach. In 2021 IEEE International Conference on Engineering Veracruz (ICEV) (pp. 1-4). IEEE.
- Watson, G. L., Xiong, D., Zhang, L., Zoller, J. A., Shamshoian, J., Sundin, P., … & Ramirez, C. M. (2021). Pandemic velocity: Forecasting COVID-19 in the US with a machine learning & Bayesian time series compartmental model. PLoS computational biology, 17(3), e1008837. doi: 10.1371/journal.pcbi.1008837
- Huang, W., Ao, S., Han, D., Liu, Y., Liu, S., & Huang, Y. (2021). Data-Driven and Machine-Learning Methods to Project Coronavirus Disease 2019 Pandemic Trend in Eastern Mediterranean. Frontiers in public health, 9.
- Gupta, Y., Raghuwanshi, G., Ahmadini, A. A. H., Sharma, U., Mishra, A. K., Mashwani, W. K., … & Samson Balogun, O. (2021). Impact of Weather Predictions on COVID-19 Infection Rate by Using Deep Learning Models. Complexity, 2021. doi: 10.1155/2021/5520663
- Kim, M., Gu, Z., Yu, S., Wang, G., & Wang, L. (2021). Methods, Challenges and Practical Issues of COVID-19 Projection: A Data Science Perspective. Journal of Data Science, 19(2). doi: 10.6339/JDS.2010.08(2).581
- Mohan, S., Abugabah, A., Kumar Singh, S., kashif Bashir, A., & Sanzogni, L. (2021). An approach to forecast impact of Covid-19 using supervised machine learning model. Software: Practice and Experience.
- Lucas, B., Vahedi, B., & Karimzadeh, M. (2022). A spatiotemporal machine learning approach to forecasting COVID-19 incidence at the county level in the USA. International Journal of Data Science and Analytics, 1-20.
- Battineni, G., Chintalapudi, N., & Amenta, F. (2020). Forecasting of COVID-19 epidemic size in four high hitting nations (USA, Brazil, India and Russia) by Fb-Prophet machine learning model. Applied Computing and Informatics.
- Alali, Y., Harrou, F., & Sun, Y. (2022). A proficient approach to forecast COVID-19 spread via optimized dynamic machine learning models. Scientific Reports, 12(1), 1-20. doi: 10.1038/s41598-021-99269-x
- Ahmad, F., Almuayqil, S. N., Mamoona, H., Shahid, N., Wasim Ahmad, K., & Kashaf, J. (2021). Prediction of COVID-19 cases using machine learning for effective public health management. Computers, Materials, & Continua, 2265-2282. doi: 10.32604/cmc.2021.013067
- Kumar, S., & Veer, K. (2020). Forecasting of Covid-19 cases using machine learning approach. Current Respiratory Medicine Reviews, 16(4), 240-245. doi: 10.2174/1573398X17666210129131009
- Ahmad, H. F., Khaloofi, H., Azhar, Z., Algosaibi, A., & Hussain, J. (2021). An Improved COVID-19 Forecasting by Infectious Disease Modelling Using Machine Learning. Applied Sciences, 11(23), 11426. doi: 10.3390/app112311426
- Zain, Z. M., & Alturki, N. M. (2021). COVID-19 pandemic forecasting using CNN-LSTM: a hybrid approach. Journal of Control Science and Engineering, 2021. doi: 10.1155/2021/8785636
- Kamalam, G. K., Lalitha, K., Priyadarshini, E., Janani, V. C., & Sasidhar, P. M. (2021, November). Forecasting the spread of COVID-19 using supervised machine learning models. In AIP Conference Proceedings (Vol. 2387, No. 1, p. 140017). AIP Publishing LLC.
- Arora, R., Agrawal, A., Arora, R., Poonia, R. C., & Madaan, V. (2021). Prediction and forecasting of COVID-19 outbreak using regression and ARIMA models, Journal of Interdisciplinary Mathematics, 24:1, 227-243, DOI: 10.1080/09720502.2020.1840075
- Agrawal, K., Madaan, V., Roy, A., Kumari, R., & Deore, H. (2021). FOCOMO: Forecasting and monitoring the worldwide spread of COVID-19 using machine learning methods, Journal of Interdisciplinary Mathematics, 24:2, 443-466, DOI: 10.1080/09720502.2021.1885812
- Patil, H., Sharma, S. & Raja, L. (2021). Study of impact of COVID-19 on different age groups using machine learning classifiers, Journal of Interdisciplinary Mathematics, 24:2, 479-487, DOI: 10.1080/09720502.2021.1896585
- Babu, M. A., Ahmmed, M. M., Ferdousi, A., Rahman, M. M., Saiduzzaman, M., Bhatnagar, V., Raja, L., & Poonia, R. C. (2022). The mathematical and machine learning models to forecast the COVID-19 outbreaks in Bangladesh, Journal of Interdisciplinary Mathematics, 25:3, 753-772, DOI: 10.1080/09720502.2021.2015095
- FBProphet Machine Learning Package. https://facebook.github.io/prophet/
- Institute of Epidemiology, Disease Control and Research (IEDCR). https://www.iedcr.gov.bd/
- COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. https://github.com/CSSEGISandData/COVID-19
- Live COVID-19 Vaccination Tracker. https://covidvax.live/location/bgd