Yiming Fei
ABSTRACT
The world's first COVID-19 infection was confirmed in 2019, and the virus has since spread rapidly around the world, with more than 200 million people infected worldwide and almost 4 million fatalities by August 2021. Therefore, it is important to study an accurate prediction model for COVID-19 infection, this is critical in order to maximize available resources and halt or reduce this illness. In this paper, we will analyze the use of machine learning algorithms in predicting COVID-19 based on existing research and compare the performance of different machine learning algorithms to conclude a more precise prediction model for COVID-19.
- [n.d.]. 2019 Coronavirus Disease (COVID-19) Topic Questions and Answers. [World Health Organization]. http://www.who.int/zh/emergencies/diseases/novel-coronavirus-2019/question-and-answers-hub/q-a-detail/coronavirus-disease-covid-19 [2020-08-26].Google Scholar
- [n.d.]. 2019 How is coronavirus disease (COVID-19) transmitted? [World Health Organization]. https://www.who.int/zh/news-room/q-a-detail/coronavirus-disease-covid-19-how-is-it-transmitted [2020-08-26].Google Scholar
- [n.d.]. Global Confirmed. [Johns Hopkins University]. https://coronavirus.jhu.edu/.Google Scholar
- [n.d.]. Post-COVID Conditions. [U.S. Centers for Disease Control and Prevention]. https://www.cdc.gov/coronavirus/2019-ncov/long-term-effects.html [2021-06-27].Google Scholar
- Ratnadip Adhikari and Ramesh K Agrawal. 2013. An introductory study on time series modeling and forecasting. arXiv preprint arXiv:1302.6613 (2013).Google Scholar
- Tania Akter, Md Shahriare Satu, Md Imran Khan, Mohammad Hanif Ali, Shahadat Uddin, Pietro Lio, Julian MW Quinn, and Mohammad Ali Moni. 2019. Machine learning-based models for early stage detection of autism spectrum disorders. IEEE Access 7 (2019), 166509--166527.Google ScholarCross Ref
- Dima Alberg and Mark Last. 2018. Short-term load forecasting in smart meters with sliding window-based ARIMA algorithms. Vietnam Journal of Computer Science 5, 3 (2018), 241--249.Google ScholarDigital Library
- Lamiaa A Amar, Ashraf A Taha, and Marwa Y Mohamed. 2020. Prediction of the final size for COVID-19 epidemic using machine learning: a case study of Egypt. Infectious Disease Modelling 5 (2020), 622--634.Google ScholarCross Ref
- Sama Amid and T Mesri Gundoshmian. 2016. Prediction of output energy based on different energy inputs on broiler production using application of adaptive neural-fuzzy inference system. Agri. Sci. Dev 5 (2016), 14--21.Google Scholar
- Sama Amid and Tarahom Mesri Gundoshmian. 2017. Prediction of output energies for broiler production using linear regression, ANN (MLP, RBF), and ANFIS models. Environmental Progress & Sustainable Energy 36, 2 (2017), 577--585.Google ScholarCross Ref
- Sina Ardabili, Amir Mosavi, Asghar Mahmoudi, Tarahom Mesri Gundoshmian, Saeed Nosratabadi, and Annamária R Várkonyi-Kóczy. 2019. Modelling temper- ature variation of mushroom growing hall using artificial neural networks. In International Conference on Global Research and Education. Springer, 33--45.Google Scholar
- Sina Faizollahzadeh Ardabili, Asghar Mahmoudi, and Tarahom Mesri Gundosh- mian. 2016. Modeling and simulation controlling system of HVAC using fuzzy and predictive (radial basis function, RBF) controllers. Journal of Building Engineering 6 (2016), 301--308.Google ScholarCross Ref
- Sina F Ardabili, Amir Mosavi, Pedram Ghamisi, Filip Ferdinand, Annamaria R Varkonyi-Koczy, Uwe Reuter, Timon Rabczuk, and Peter M Atkinson. 2020. Covid- 19 outbreak prediction with machine learning. Algorithms 13, 10 (2020), 249.Google ScholarCross Ref
- Domenico Benvenuto, Marta Giovanetti, Lazzaro Vassallo, Silvia Angeletti, and Massimo Ciccozzi. 2020. Application of the ARIMA model on the COVID-2019 epidemic dataset. Data in brief 29 (2020), 105340.Google Scholar
- Erasmo Cadenas, Oscar A Jaramillo, and Wilfrido Rivera. 2010. Analysis and fore- casting of wind velocity in chetumal, quintana roo, using the single exponential smoothing method. Renewable Energy 35, 5 (2010), 925--930.Google ScholarCross Ref
- Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014).Google Scholar
- Javier Contreras, Rosario Espinola, Francisco J Nogales, and Antonio J Conejo. 2003. ARIMA models to predict next-day electricity prices. IEEE transactions on power systems 18, 3 (2003), 1014--1020.Google ScholarCross Ref
- Xiao Fang Du, Stephen CH Leung, Jin Long Zhang, and Kin Keung Lai. 2013. Demand forecasting of perishable farm products using support vector machine. International journal of systems Science 44, 3 (2013), 556--567.Google Scholar
- Alex Graves and Jürgen Schmidhuber. 2005. Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural networks 18, 5--6 (2005), 602--610.Google Scholar
- Rajan Gupta, Gaurav Pandey, Poonam Chaudhary, and Saibal K Pal. 2020. Machine learning models for government to predict COVID-19 outbreak. Digital Government: Research and Practice 1, 4 (2020), 1--6.Google ScholarDigital Library
- Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735--1780.Google ScholarDigital Library
- Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735--1780.Google ScholarDigital Library
- M Anwar Hossain, Bamidele V Ayodele, Chin Kui Cheng, and Maksudur R Khan. 2016. Artificial neural network modeling of hydrogen-rich syngas production from methane dry reforming over novel Ni/CaFe2O4 catalysts. International Journal of Hydrogen Energy 41, 26 (2016), 11119--11130.Google ScholarCross Ref
- Yi-Chung Hu. 2014. Multilayer perceptron for robust nonlinear interval regression analysis using genetic algorithms. The Scientific World Journal 2014 (2014).Google Scholar
- David S Hui, Esam I Azhar, Tariq A Madani, Francine Ntoumi, Richard Kock, Osman Dar, Giuseppe Ippolito, Timothy D Mchugh, Ziad A Memish, Christian Drosten, et al. 2020. The continuing 2019-nCoV epidemic threat of novel coron-aviruses to global health---The latest 2019 novel coronavirus outbreak in Wuhan, China. International journal of infectious diseases 91 (2020), 264--266.Google Scholar
- J-SR Jang. 1993. ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics 23, 3 (1993), 665--685.Google Scholar
- Irfan Ahmad Khan, Adnan Akber, and Yinliang Xu. 2019. Sliding window regression based short-term load forecasting of a multi-area power system. In 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE). IEEE, 1--5.Google ScholarCross Ref
- A Khosravi, RNN Koury, L Machado, and JJG Pabon. 2018. Prediction of wind speed and wind direction using artificial neural network, support vector regres- sion and adaptive neuro-fuzzy inference system. Sustainable Energy Technologies and Assessments 25 (2018), 146--160.Google ScholarCross Ref
- Diederik P Kingma. 2013. Fast gradient-based inference with continuous latent variable models in auxiliary form. arXiv preprint arXiv:1306.0733 (2013).Google Scholar
- Daniel D Lee, P Pham, Y Largman, and A Ng. 2009. Advances in neural information processing systems 22. Technical Report. Tech. Rep., Tech. Rep.Google Scholar
- Daniel P Oran and Eric J Topol. 2020. Prevalence of asymptomatic SARS-CoV-2 infection: a narrative review. Annals of internal medicine 173, 5 (2020), 362--367.Google ScholarCross Ref
- Fotios Petropoulos and Spyros Makridakis. 2020. Forecasting the novel coronavirus COVID-19. PloS one 15, 3 (2020), e0231236.Google ScholarCross Ref
- Narinder Singh Punn, Sanjay Kumar Sonbhadra, and Sonali Agarwal. 2020. COVID-19 epidemic analysis using machine learning and deep learning algorithms. MedRxiv (2020).Google Scholar
- Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra. 2014. Stochastic backpropagation and approximate inference in deep generative models. In International conference on machine learning. PMLR, 1278--1286.Google Scholar
- Furqan Rustam, Imran Ashraf, Arif Mehmood, Saleem Ullah, and Gyu Sang Choi. 2019. Tweets classification on the base of sentiments for US airline companies. Entropy 21, 11 (2019), 1078.Google ScholarCross Ref
- Furqan Rustam, Aijaz Ahmad Reshi, Arif Mehmood, Saleem Ullah, Byung-Won On, Waqar Aslam, and Gyu Sang Choi. 2020. COVID-19 future forecasting using supervised machine learning models. IEEE access 8 (2020), 101489--101499.Google Scholar
- Guillermo Santamaría-Bonfil, Juan Frausto-Solís, and Ignacio Vázquez-Rodarte. 2015. Volatility forecasting using support vector regression and a hybrid genetic algorithm. Computational Economics 45, 1 (2015), 111--133.Google ScholarDigital Library
- Md Satu, Koushik Chandra Howlader, Mufti Mahmud, M Shamim Kaiser, Sheikh Mohammad Shariful Islam, Julian MW Quinn, Salem A Alyami, Mohammad Ali Moni, et al. 2021. Short-term prediction of COVID-19 cases using machine learning models. Applied Sciences 11, 9 (2021), 4266.Google ScholarCross Ref
- Mike Schuster and Kuldip K Paliwal. 1997. Bidirectional recurrent neural networks. IEEE transactions on Signal Processing 45, 11 (1997), 2673--2681.Google ScholarDigital Library
- Farah Shahid, Aneela Zameer, and Muhammad Muneeb. 2020. Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM. Chaos, Solitons & Fractals 140 (2020), 110212.Google ScholarCross Ref
- R Sujath, Jyotir Moy Chatterjee, and Aboul Ella Hassanien. 2020. A machine learn- ing forecasting model for COVID-19 pandemic in India. Stochastic Environmental Research and Risk Assessment 34 (2020), 959--972.Google ScholarCross Ref
- MRM Talabis, R McPherson, I Miyamoto, JL Martin, and D Kaye. 2015. Analytics defined. Information Security Analytics (2015), 1--12.Google Scholar
- Sean J Taylor and Benjamin Letham. 2018. Forecasting at scale. The American Statistician 72, 1 (2018), 37--45.Google ScholarCross Ref
- Robert Tibshirani. 1996. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological) 58, 1 (1996), 267--288.Google ScholarCross Ref
- Shahadat Uddin, Arif Khan, Md Ekramul Hossain, and Mohammad Ali Moni. 2019. Comparing different supervised machine learning algorithms for disease prediction. BMC medical informatics and decision making 19, 1 (2019), 1--16.Google Scholar
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in neural information processing systems. 5998--6008.Google Scholar
- Neo Wu, Bradley Green, Xue Ben, and Shawn O'Banion. 2020. Deep transformer models for time series forecasting: The influenza prevalence case. arXiv preprint arXiv:2001.08317 (2020).Google Scholar
- Dharminder Yadav, Himani Maheshwari, Umesh Chandra, and Avinash Sharma. 2020. COVID-19 Analysis by Using Machine and Deep Learning. In Internet of Medical Things for Smart Healthcare. Springer, 31--63.Google Scholar
- Abdelhafid Zeroual, Fouzi Harrou, Abdelkader Dairi, and Ying Sun. 2020. Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study. Chaos, Solitons & Fractals 140 (2020), 110121.Google ScholarCross Ref
Index Terms
- Analysis the use of machine learning algorithm-based methods in predicting COVID-19 infection
Recommendations
Analysis and Prediction of COVID-19 Data using Machine Learning Models
ICCPR '21: Proceedings of the 2021 10th International Conference on Computing and Pattern RecognitionThe COVID-19 pandemic has devastated the world by killing millions of people. The goal of this project is to utilize advanced machine learning models to analyze the global COVID-19 data and predict the future patterns of the COVID-19 pandemic. The COVID-...
Machine learning approach for data analysis and predicting coronavirus using COVID-19 India dataset
According to the World Health Organisation (WHO), the COVID-19 virus would infect 83,558,756 persons worldwide in 2020, resulting in 646,949 deaths. In this research, we aim to find the link between the time series data and current circumstances to ...
Machine learning research towards combating COVID-19: Virus detection, spread prevention, and medical assistance
Graphical abstractDisplay Omitted
Highlights- This paper is a survey of the Machine Learning (ML) research done against COVID-19.
AbstractCOVID-19 was first discovered in December 2019 and has continued to rapidly spread across countries worldwide infecting thousands and millions of people. The virus is deadly, and people who are suffering from prior illnesses or are ...
Comments