Review Study on Outbreak Prediction of Covid-19 By using Machine Learning
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Abstract
In December 2019, Wuhan City, China, discovered a new infectious disease, COVID-19. Over 70 million people have been infected and one million people have died as a result of COVID-19. Defeating such a deadly, infectious disease requires accurate models that predict COVID-19 outbreaks. Using prediction models, governments can plan budgets and facilities for fighting diseases, and take control measures to make better decisions and take control measures. For example, they can determine how many medicines and medical equipment to manufacture or import, as well as how many medical personnel are needed to fight the disease. The COVID-19 outbreak has subsequently been predicted in several countries and continents using regression and classification models. A recent study that incorporated statistical and machine learning techniques was reviewed to predict COVID-19 outbreaks in the future. Ground truth datasets are used, their characteristics are investigated, models are developed, predictor variables are identified, statistical and machine learning methods are applied, performance metrics are calculated, and finally comparisons are made. By applying machine learning methods, the survey results indicate that we can make predictions about whether a patient will become infected with COVID-19, how outbreak trends will develop, and which age groups will be affected the most.
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Kumari, R., Kumar, S., Poonia, R. C., Singh, V., Raja, L., Bhatnagar, V., & Agarwal, P. (2021). Analysis and predictions of spread, recovery, and death caused by COVID-19 in India. Big Data Mining and Analytics, 4(2), 65-75. https://doi.org/10.26599/BDMA.2020.9020013
Ardabili, S. F., Mosavi, A., Ghamisi, P., Ferdinand, F., Varkonyi-Koczy, A. R., Reuter, U., ... & Atkinson, P. M. (2020). Covid-19 outbreak prediction with machine learning. Algorithms, 13(10), 249. https://doi.org/10.3390/a13100249
Kumar, A., Rani, P., Kumar, R., Sharma, V., & Purohit, S. R. (2020). Data-driven modelling and prediction of COVID-19 infection in India and correlation analysis of the virus transmission with socio-economic factors. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(5), 1231-1240. https://doi.org/10.1016/j.dsx.2020.07.008
Singh, S., Raj, P., Kumar, R., & Chaujar, R. (2020, July). Prediction and forecast for COVID-19 Outbreak in India based on Enhanced Epidemiological Models. In 2020 Second international conference on inventive research in computing applications (ICIRCA) (pp. 93-97). IEEE. https://doi.org/10.1109/ICIRCA48905.2020.9183126
Yadav, M., Perumal, M., & Srinivas, M. (2020). Analysis on novel coronavirus (COVID-19) using machine learning methods. Chaos, Solitons & Fractals, 139, 110050. https://doi.org/10.1016/j.chaos.2020.110050
Temsah, M. H., Al-Sohime, F., Alamro, N., Al-Eyadhy, A., Al-Hasan, K., Jamal, A., ... & Somily, A. M. (2020). The psychological impact of COVID-19 pandemic on health care workers in a MERS-CoV endemic country. Journal of infection and public health, 13(6), 877-882. https://doi.org/10.1016/j.jiph.2020.05.021
Senapati, A., Nag, A., Mondal, A., & Maji, S. (2021). A novel framework for COVID-19 case prediction through piecewise regression in India. International Journal of Information Technology, 13(1), 41-48. https://doi.org/10.1007/s41870-020-00552-3
Hussain, E., Hasan, M., Rahman, M. A., Lee, I., Tamanna, T., & Parvez, M. Z. (2021). CoroDet: A deep learning based classification for COVID-19 detection using chest X-ray images. Chaos, Solitons & Fractals, 142, 110495. https://doi.org/10.1016/j.chaos.2020.110495
Priyadarshini, I., Mohanty, P., Kumar, R., Son, L. H., Chau, H. T. M., Nhu, V. H., ... & Tien Bui, D. (2020, May). Analysis of outbreak and global impacts of the COVID-19. In Healthcare (Vol. 8, No. 2, p. 148). MDPI. https://doi.org/10.3390/healthcare8020148
Gupta, R., Pandey, G., Chaudhary, P., & Pal, S. K. (2020). Machine learning models for government to predict COVID-19 outbreak. Digital Government: Research and Practice, 1(4), 1-6. https://doi.org/10.1145/3411761
Zhu, N., Zhang, D., Wang, W., Li, X., Yang, B., Song, J., ... & Tan, W. (2020). A novel coronavirus from patients with pneumonia in China, 2019. New England journal of medicine. https://doi.org/10.1056/NEJMoa2001017
Chan, A. K. Y., Tamrakar, M., Jiang, C. M., Lo, E. C. M., Leung, K. C. M., & Chu, C. H. (2021). Common medical and dental problems of older adults: a narrative review. Geriatrics, 6(3), 76. https://doi.org/10.3390/geriatrics6030076
Anderson, R. M., Heesterbeek, H., Klinkenberg, D., & Hollingsworth, T. D. (2020). How will country-based mitigation measures influence the course of the COVID-19 epidemic?. The lancet, 395(10228), 931-934. https://doi.org/10.1016/S0140-6736(20)30567-5
Guhathakurata, S., Saha, S., Kundu, S., Chakraborty, A., & Banerjee, J. S. (2021). South Asian Countries are less fatal concerning COVID-19: a fact-finding procedure integrating machine learning & multiple criteria decision-making (MCDM) technique. Journal of The Institution of Engineers (India): Series B, 1-15. https://doi.org/10.1007/s40031-021-00547-z
Tamhane, R., & Mulge, S. (2020). Prediction of COVID-19 outbreak using machine learning. International Research Journal of Engineering and Technology (IRJET), 7(5).
Khemasuwan, D., Sorensen, J. S., & Colt, H. G. (2020). Artificial intelligence in pulmonary medicine: computer vision, predictive model and COVID-19. European respiratory review, 29(157). https://doi.org/10.1183/16000617.0181-2020
Satrio, C. B. A., Darmawan, W., Nadia, B. U., & Hanafiah, N. (2021). Time series analysis and forecasting of coronavirus disease in Indonesia using ARIMA model and PROPHET. Procedia Computer Science, 179, 524-532. https://doi.org/10.1016/j.procs.2021.01.036
van Hell, N. (2021). Text Data Measured with Error: Empirical Strategies with an Application in S&P 500 Implied Volatility Forecasting (Doctoral dissertation, Queen’s University).
Çetin, U. A., & Fatih, A. B. U. T. (2022). A survey of recent studies on COVID-19 outbreak prediction using statistical and machine learning methods. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 1-1. https://doi.org/10.28948/ngumuh.1025095
Althnian, A., Abou Elwafa, A., Aloboud, N., Alrasheed, H., & Kurdi, H. (2020). Prediction of COVID-19 individual susceptibility using demographic data: A case study on Saudi Arabia. Procedia Computer Science, 177, 379-386. https://doi.org/10.1016/j.procs.2020.10.051
Iwendi, C., Huescas, C. G. Y., Chakraborty, C., & Mohan, S. (2022). COVID-19 health analysis and prediction using machine learning algorithms for Mexico and Brazil patients. Journal of Experimental & Theoretical Artificial Intelligence, 1-21. https://doi.org/10.1080/0952813X.2022.2058097
Song, J., Gao, Y., Yin, P., Li, Y., Li, Y., Zhang, J., ... & Pi, H. (2021). The random forest model has the best accuracy among the four pressure ulcer prediction models using machine learning algorithms. Risk Management and Healthcare Policy, 1175-1187. https://doi.org/10.2147/RMHP.S297838
Ceylan, Z. (2020). Estimation of COVID-19 prevalence in Italy, Spain, and France. Science of The Total Environment, 729, 138817. https://doi.org/10.1016/j.scitotenv.2020.138817
Papastefanopoulos, V., Linardatos, P., & Kotsiantis, S. (2020). COVID-19: a comparison of time series methods to forecast percentage of active cases per population. Applied sciences, 10(11), 3880. https://doi.org/10.3390/app10113880
Mohamadou, Y., Halidou, A., & Kapen, P. T. (2020). A review of mathematical modeling, artificial intelligence and datasets used in the study, prediction and management of COVID-19. Applied Intelligence, 50(11), 3913-3925. https://doi.org/10.1007/s10489-020-01770-9
ArunKumar, K. E., Kalaga, D. V., Kumar, C. M. S., Chilkoor, G., Kawaji, M., & Brenza, T. M. (2021). Forecasting the dynamics of cumulative COVID-19 cases (confirmed, recovered and deaths) for top-16 countries using statistical machine learning models: Auto-Regressive Integrated Moving Average (ARIMA) and Seasonal Auto-Regressive Integrated Moving Average (SARIMA). Applied soft computing, 103, 107161. https://doi.org/10.1016/j.asoc.2021.107161
Fang, L., Wang, D., & Pan, G. (2020). Analysis and estimation of COVID-19 spreading in Russia based on ARIMA model. SN Comprehensive Clinical Medicine, 2, 2521-2527. https://doi.org/10.1007/s42399-020-00555-y
Fayyoumi, E., Idwan, S., & AboShindi, H. (2020). Machine learning and statistical modelling for prediction of novel COVID-19 patients case study: Jordan. International Journal of Advanced Computer Science and Applications, 11(5). https://doi.org/10.14569/IJACSA.2020.0110518
Erdem, E., & Bozkurt, F. (2021). A comparison of various supervised machine learning techniques for prostate cancer prediction. Avrupa Bilim ve Teknoloji Dergisi, (21), 610-620.
Kumar, A., Gupta, P. K., & Srivastava, A. (2020). A review of modern technologies for tackling COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(4), 569-573. https://doi.org/10.1016/j.dsx.2020.05.008
Pourghasemi, H. R., Pouyan, S., Farajzadeh, Z., Sadhasivam, N., Heidari, B., Babaei, S., & Tiefenbacher, J. P. (2020). Assessment of the outbreak risk, mapping and infection behavior of COVID-19: Application of the autoregressive integrated-moving average (ARIMA) and polynomial models. Plos one, 15(7), e0236238. https://doi.org/10.1371/journal.pone.0236238
Chimmula, V. R. (2021). A Data Driven Based Comparison Study of Statistical and Deep Learning Based Time Series Forecasting Methods for Infectious Disease Modeling and Financial Data. The University of Regina (Canada).
Yeung, A. Y., Roewer-Despres, F., Rosella, L., & Rudzicz, F. (2021). Machine learning–based prediction of growth in confirmed COVID-19 infection cases in 114 countries using metrics of nonpharmaceutical interventions and cultural dimensions: model development and validation. Journal of Medical Internet Research, 23(4), e26628. https://doi.org/10.2196/26628
Hassan, A. H. M., Qasem, A. A. M., Abdalla, W. F. M., & Elhassan, O. H. (2021). Visualization & prediction of COVID-19 future outbreak by using machine learning. Int. J. Inf. Technol. Comput. Sci, 13(3), 16-32. https://doi.org/10.5815/ijitcs.2021.03.02
Gebretensae, Y. A., & Asmelash, D. (2021). Trend analysis and forecasting the spread of COVID-19 pandemic in Ethiopia using Box–Jenkins modeling procedure. International journal of general medicine, 1485-1498. https://doi.org/10.2147/IJGM.S306250
Al-Turaiki, I., Almutlaq, F., Alrasheed, H., & Alballa, N. (2021). Empirical evaluation of alternative time-series models for covid-19 forecasting in Saudi Arabia. International Journal of Environmental Research and Public Health, 18(16), 8660. https://doi.org/10.3390/ijerph18168660
Marzouk, M., Elshaboury, N., Abdel-Latif, A., & Azab, S. (2021). Deep learning model for forecasting COVID-19 outbreak in Egypt. Process Safety and Environmental Protection, 153, 363-375. https://doi.org/10.1016/j.psep.2021.07.034
Sánchez-Luna, M., Fernández Colomer, B., de Alba Romero, C., Alarcón Allen, A., Baña Souto, A., Camba Longueira, F., ... & SENEO COVID-19 Registry Study Group. (2021). Neonates born to mothers with COVID-19: data from the Spanish Society of Neonatology Registry. Pediatrics, 147(2). https://doi.org/10.1542/peds.2020-015065
Anki, P., Bustamam, A., & Buyung, R. A. (2021). Looking for the link between the causes of the COVID-19 disease using the multi-model application. Commun. Math. Biol. Neurosci., 2021, Article-ID.
Ballı, S. (2021). Data analysis of Covid-19 pandemic and short-term cumulative case forecasting using machine learning time series methods. Chaos, Solitons & Fractals, 142, 110512. https://doi.org/10.1016/j.chaos.2020.110512
Elsheikh, A. H., Saba, A. I., Abd Elaziz, M., Lu, S., Shanmugan, S., Muthuramalingam, T., ... & Shehabeldeen, T. A. (2021). Deep learning-based forecasting model for COVID-19 outbreak in Saudi Arabia. Process Safety and Environmental Protection, 149, 223-233. https://doi.org/10.1016/j.psep.2020.10.048
Santra, A., & Dutta, A. (2022). A Comprehensive Review of Machine Learning Techniques for Predicting the Outbreak of Covid-19 Cases. International Journal of Intelligent Systems & Applications, 14(3). https://doi.org/10.5815/ijisa.2022.03.04
Luo, J., Zhang, Z., Fu, Y., & Rao, F. (2021). Time series prediction of COVID-19 transmission in America using LSTM and XGBoost algorithms. Results in Physics, 27, 104462. https://doi.org/10.1016/j.rinp.2021.104462
Jojoa, M., Lazaro, E., Garcia-Zapirain, B., Gonzalez, M. J., & Urizar, E. (2021). The impact of COVID 19 on university staff and students from Iberoamerica: Online learning and teaching experience. International Journal of Environmental Research and Public Health, 18(11), 5820. https://doi.org/10.3390/ijerph18115820
Jojoa, M., & Garcia-Zapirain, B. (2020). Forecasting covid 19 confirmed cases using machine learning: the case of america. https://doi.org/10.20944/preprints202009.0228.v1
Moreau, V. H. (2020). Forecast predictions for the COVID-19 pandemic in Brazil by statistical modeling using the Weibull distribution for daily new cases and deaths. Brazilian Journal of Microbiology, 51(3), 1109-1115. https://doi.org/10.1007/s42770-020-00331-z
Gomes, J. C., de Santana, M. A., Masood, A. I., de Lima, C. L., & Dos Santos, W. P. (2023). COVID-19’s influence on cardiac function: a machine learning perspective on ECG analysis. Medical & Biological Engineering & Computing, 1-25. https://doi.org/10.1007/s11517-023-02773-7
Mohammedqasim, H., & Ata, O. (2022). Real-time data of COVID-19 detection with IoT sensor tracking using artificial neural network. Computers and Electrical Engineering, 100, 107971. https://doi.org/10.1016/j.compeleceng.2022.107971
Jojoa, M., Lazaro, E., Garcia-Zapirain, B., Gonzalez, M. J., & Urizar, E. (2021). The impact of COVID 19 on university staff and students from Iberoamerica: Online learning and teaching experience. International Journal of Environmental Research and Public Health, 18(11), 5820. https://doi.org/10.3390/ijerph18115820
Yang, L., & Shami, A. (2020). On hyperparameter optimization of machine learning algorithms: Theory and practice. Neurocomputing, 415, 295-316. https://doi.org/10.1016/j.neucom.2020.07.061
Brat, G. A., Weber, G. M., Gehlenborg, N., Avillach, P., Palmer, N. P., Chiovato, L., ... & Kohane, I. S. (2020). International electronic health record-derived COVID-19 clinical course profiles: the 4CE consortium. NPJ digital medicine, 3(1), 109. https://doi.org/10.1038/s41746-020-00308-0
Moreau, V. H. (2020). Forecast predictions for the COVID-19 pandemic in Brazil by statistical modeling using the Weibull distribution for daily new cases and deaths. Brazilian Journal of Microbiology, 51(3), 1109-1115. https://doi.org/10.1007/s42770-020-00331-z
Kafieh, R., Arian, R., Saeedizadeh, N., Amini, Z., Serej, N. D., Minaee, S., ... & Haghjooy Javanmard, S. (2021). COVID-19 in Iran: forecasting pandemic using deep learning. Computational and mathematical methods in medicine, 2021. https://doi.org/10.1155/2021/6927985
Da Silva, R. G., Ribeiro, M. H. D. M., Mariani, V. C., & dos Santos Coelho, L. (2020). Forecasting Brazilian and American COVID-19 cases based on artificial intelligence coupled with climatic exogenous variables. Chaos, Solitons & Fractals, 139, 110027. https://doi.org/10.1016/j.chaos.2020.110027
Gomez-Cravioto, D. A., Diaz-Ramos, R. E., Cantu-Ortiz, F. J., & Ceballos, H. G. (2021). Data analysis and forecasting of the COVID-19 spread: A comparison of recurrent neural networks and time series models. Cognitive Computation, 1-12. https://doi.org/10.1007/s12559-021-09885-y
Ayoobi, N., Sharifrazi, D., Alizadehsani, R., Shoeibi, A., Gorriz, J. M., Moosaei, H., ... & Mosavi, A. (2021). Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods. Results in physics, 27, 104495. https://doi.org/10.1016/j.rinp.2021.104495
Cui, F., Salih, S. Q., Choubin, B., Bhagat, S. K., Samui, P., & Yaseen, Z. M. (2020). Newly explored machine learning model for river flow time series forecasting at Mary River, Australia. Environmental Monitoring and Assessment, 192, 1-15. https://doi.org/10.1007/s10661-020-08724-1
Bala, S. (2021). COVID-19 Outbreak Prediction Analysis using Machine Learning. International Journal for Research in Applied Science and Engineering Technology, 9(1). https://doi.org/10.22214/ijraset.2021.32690
Hassan, A. H. M., Qasem, A. A. M., Abdalla, W. F. M., & Elhassan, O. H. (2021). Visualization & prediction of COVID-19 future outbreak by using machine learning. Int. J. Inf. Technol. Comput. Sci, 13(3), 16-32. https://doi.org/10.5815/ijitcs.2021.03.02
Cruz, R. C., Reis Costa, P., Vinga, S., Krippahl, L., & Lopes, M. B. (2021). A Review of recent machine learning advances for forecasting harmful algal blooms and shellfish contamination. Journal of Marine Science and Engineering, 9(3), 283. https://doi.org/10.3390/jmse9030283