Short-Term Prediction of COVID-19 Cases Using Machine Learning Models
Source abstract
Abstract
The first case in Bangladesh of the novel coronavirus disease (COVID-19) was reported on 8 March 2020, with the number of confirmed cases rapidly rising to over 175,000 by July 2020. In the absence of effective treatment, an essential tool of health policy is the modeling and forecasting of the progress of the pandemic. We, therefore, developed a cloud-based machine learning short-term forecasting model for Bangladesh, in which several regression-based machine learning models were applied to infected case data to estimate the number of COVID-19-infected people over the following seven days. This approach can accurately forecast the number of infected cases daily by training the prior 25 days sample data recorded on our web application. The outcomes of these efforts could aid the development and assessment of prevention strategies and identify factors that most affect the spread of COVID-19 infection in Bangladesh.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: COVID-19 epidemiological studies · COVID-19 diagnosis using AI · Artificial Intelligence in Healthcare
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Management Information Systems, Noakhali Science and Technology University, Noakhali 3814, Bangladesh
Department of Computer Science and Telecommunication Engineering, Noakhali Science and Techology University, Noakhali 3814, Bangladesh
Department of Computer Science, Nottingham Trent University, Clifton, Nottingham NG11 8NS, UK
Medical Technologies Innovation Facility, Nottingham Trent University, Clifton, Nottingham NG11 8NS, UK
Institute of Information Technology, Jahangirnagar University, Savar 1342, Bangladesh
Institute for Physical Activity and Nutrition, Deakin University, Burwood, VIC 3125, Australia
Healthy Ageing Theme, The Garvan Institute of Medical Research, Darlinghurst, Sydney, NSW 2010, Australia
Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia
WHO Collaborating Centre on eHealth, UNSW Digital Health, School of Public Health and Community Medicine, Faculty of Medicine, University of New South Wales, Sydney, NSW 2052, Australia
References (52)
Show source-provided references
1. Investigating the cases of novel coronavirus disease (COVID-19) in China using dynamic statistical techniques10.1016/j.heliyon.2020.e03747 ↗
2. Machine learning approach for confirmation of COVID-19 cases: Positive, negative, death and release10.53986/ibjm.2020.0031 ↗
3. Islam, M.T., Talukder, A.K., Siddiqui, M.N., and Islam, T. (2020). Tackling the COVID-19 pandemic: The Bangladesh perspective. J. Public Health Res., 9.10.20944/preprints202004.0384.v1 ↗
4. Ahamad, M., Aktar, S., Rashed-Al-Mahfuz, M., Azad, A., Uddin, S., Alyami, S.A., Sarker, I.H., Liò, P., Quinn, J.M.W., and Moni, M.A. (2021). Adverse effects of COVID-19 vaccination: Machine learning and statistical approach to identify and classify incidences of morbidity and post-vaccination reactogenicity. medrXiv.10.1101/2021.04.16.21255618 ↗
5. Uddin, S., Imam, T., and Ali Moni, M. (2021). The implementation of public health and economic measures during the first wave of COVID-19 by different countries with respect to time, infection rate and death rate. 2021 Australasian Computer Science Week Multiconference, Association for Computing Machinery.10.1145/3437378.3437384 ↗
6. COVID-19 Future Forecasting Using Supervised Machine Learning Models10.1109/ACCESS.2020.2997311 ↗
7. A machine learning model to identify early stage symptoms of SARS-Cov-2 infected patients10.1016/j.eswa.2020.113661 ↗
8. Satu, M., Howlader, K.C., and Islam, S.M.S. (2020). Machine Learning-Based Approaches for Forecasting COVID-19 Cases in Bangladesh, Social Science Research Network. SSRN Scholarly Paper ID 3614675.10.2139/ssrn.3614675 ↗
9. Aktar, S., Ahamad, M., Rashed-Al-Mahfuz, M., Azad, A., Uddin, S., Kamal, A., Alyami, S.A., Lin, P.I., Islam, S.M.S., and Quinn, J.M. (2020). Predicting Patient COVID-19 Disease Severity by means of Statistical and Machine Learning Analysis of Blood Cell Transcriptome Data. arXiv.
10. A three layered decentralized IoT biometric architecture for city lockdown during COVID-19 outbreak10.1109/ACCESS.2020.3021983 ↗
11. Data-driven dynamic clustering framework for mitigating the adverse economic impact of Covid-19 lockdown practices10.1016/j.scs.2020.102372 ↗
12. Kalajdjieski, J., Zdravevski, E., Corizzo, R., Lameski, P., Kalajdziski, S., Pires, I.M., Garcia, N.M., and Trajkovik, V. (2020). Air Pollution Prediction with Multi-Modal Data and Deep Neural Networks. Remote Sens., 12.10.3390/rs12244142 ↗
13. Liu, P., Wang, J., Sangaiah, A.K., Xie, Y., and Yin, X. (2019). Analysis and Prediction of Water Quality Using LSTM Deep Neural Networks in IoT Environment. Sustainability, 11.10.3390/su11072058 ↗
14. ECHAD: Embedding-Based Change Detection From Multivariate Time Series in Smart Grids10.1109/ACCESS.2020.3019095 ↗
15. Machine Learning Approach to Predicting COVID-19 Disease Severity Based on Clinical Blood Test Data: Statistical Analysis and Model Development10.2196/25884 ↗
16. Multi-aspect renewable energy forecasting10.1016/j.ins.2020.08.003 ↗
17. Satu, M.S., Khan, M.I., Mahmud, M., Uddin, S., Summers, M.A., Quinn, J.M., and Moni, M.A. (2020). TClustVID: A Novel Machine Learning Classification Model to Investigate Topics and Sentiment in COVID-19 Tweets. MedRxiv.10.1101/2020.08.04.20167973 ↗
18. Satu, M.S., Ahammed, K., Abedin, M.Z., Rahman, M.A., Islam, S.M.S., Azad, A., Alyami, S.A., and Moni, M.A. (2021). Convolutional Neural Network Model to Detect COVID-19 Patients Utilizing Chest X-ray Images. medRxiv.10.1101/2020.06.07.20124594 ↗
19. Aradhya, V.N.M., Mahmud, M., Guru, D., Agarwal, B., and Kaiser, M.S. (2021). One-shot Cluster-Based Approach for the Detection of COVID–19 from Chest X–ray Images. Cogn. Comput., 1–8.10.20944/preprints202007.0656.v1 ↗
20. Automated classification of usual interstitial pneumonia using regional volumetric texture analysis in high-resolution CT10.1097/RLI.0000000000000127 ↗
21. Social Group Optimization–Assisted Kapur’s Entropy and Morphological Segmentation for Automated Detection of COVID-19 Infection from Computed Tomography Images10.1007/s12559-020-09751-3 ↗
22. CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection10.1109/ACCESS.2020.2994762 ↗
23. Aktar, S., Talukder, A., Ahamad, M., Kamal, A., Khan, J.R., Protikuzzaman, M., Hossain, N., Quinn, J.M., Summers, M.A., and Liaw, T. (2020). Machine Learning and Meta-Analysis Approach to Identify Patient Comorbidities and Symptoms that Increased Risk of Mortality in COVID-19. arXiv.10.3390/diagnostics11081383 ↗
24. Privacy-Aware Energy-Efficient Framework Using the Internet of Medical Things for COVID-1910.1109/IOTM.0001.2000123 ↗
25. A machine learning forecasting model for COVID-19 pandemic in India10.1007/s00477-020-01827-8 ↗
26. Chakraborty, C., Banerjee, A., Garg, L., and Rodrigues, J.J.P.C. (2020). COVID-19 Analysis by Using Machine and Deep Learning. Internet of Medical Things for Smart Healthcare: Covid-19 Pandemic, Springer. Studies in Big Data.10.1007/978-981-15-8097-0 ↗
27. Ardabili, S.F., Mosavi, A., Ghamisi, P., Ferdinand, F., Varkonyi-Koczy, A.R., Reuter, U., Rabczuk, T., and Atkinson, P.M. (2020). COVID-19 Outbreak Prediction with Machine Learning. Algorithms, 13.10.32942/OSF.IO/XQ8RB ↗
28. Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study10.1016/j.chaos.2020.110121 ↗
29. Machine Learning Models for Government to Predict COVID-19 Outbreak
30. Neural network powered COVID-19 spread forecasting model10.1016/j.chaos.2020.110203 ↗
31. Prediction of the final size for COVID-19 epidemic using machine learning: A case study of Egypt
32. Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM10.1016/j.chaos.2020.110212 ↗
33. Srivastava, V., Srivastava, S., Chaudhary, G., and Al-Turjman, F. (2020). A systematic approach for COVID-19 predictions and parameter estimation. Pers. Ubiquitous Comput.10.1007/s00779-020-01462-8 ↗
34. Kumar, S., Viral, R., Deep, V., Sharma, P., Kumar, M., Mahmud, M., and Stephan, T. (2021). Forecasting major impacts of COVID-19 pandemic on country-driven sectors: Challenges, lessons, and future roadmap. Pers. Ubiquitous Comput.10.1007/s00779-021-01530-7 ↗
35. Real-time forecasts of the COVID-19 epidemic in China from February 5th to February 24th, 2020
36. An interactive web-based dashboard to track COVID-19 in real time10.1016/S1473-3099(20)30120-1 ↗
37. Deka, G.C., Kaiwartya, O., Vashisth, P., and Rathee, P. (2018). Performance Analysis of Time Series Forecasting Using Machine Learning Algorithms for Prediction of Ebola Casualties. International Conference on Application of Computing and Communication Technologies, Springer. Communications in Computer and Information Science.
38. Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil10.1016/j.chaos.2020.109853 ↗
39. Prediction of epidemic trends in COVID-19 with logistic model and machine learning techniques10.1016/j.chaos.2020.110058 ↗
40. Short-term load forecasting in smart meters with sliding window-based ARIMA algorithms10.1007/s40595-018-0119-7 ↗
41. Khan, I.A., Akber, A., and Xu, Y. (2019, January 5–8). Sliding window regression based short-term load forecasting of a multi-area power system. Proceedings of the 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE), Edmonton, AB, Canada.10.1109/CCECE.2019.8861915 ↗
42. Scikit-learn: Machine Learning in Python
43. Khan, M.H.R., and Hossain, A. (2020). COVID-19 Outbreak Situations in Bangladesh: An Empirical Analysis. medRxiv.
44. Punn, N.S., Sonbhadra, S.K., and Agarwal, S. (2020). COVID-19 Epidemic Analysis using Machine Learning and Deep Learning Algorithms. medRxiv.10.1101/2020.04.08.20057679 ↗
45. Ndiaye, B.M., Tendeng, L., and Seck, D. (2020). Analysis of the COVID-19 pandemic by SIR model and machine learning technics for forecasting. arXiv.
46. Satu, M.S., Rahman, M.K., Rony, M.A., Shovon, A.R., Adnan, M.J.A., Howlader, K.C., and Kaiser, M.S. (2021, January 27–28). COVID-19: Update, Forecast and Assistant-An Interactive Web Portal to Provide Real-Time Information and Forecast COVID-19 Cases in Bangladesh. Proceedings of the 2021 International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD), Dhaka, Bangladesh.10.1109/ICICT4SD50815.2021.9396786 ↗
47. Han, J., Pei, J., and Kamber, M. (2011). Data Mining: Concepts and Techniques, Elsevier.
48. Machine learning-based models for early stage detection of autism spectrum disorders10.1109/ACCESS.2019.2952609 ↗
49. Comparing different supervised machine learning algorithms for disease prediction
50. Credit default prediction modeling: An application of support vector machine10.1057/s41283-017-0016-x ↗
51. Multilayer perceptron for robust nonlinear interval regression analysis using genetic algorithms10.1155/2014/970931 ↗
52. Forecasting at scale10.1080/00031305.2017.1380080 ↗