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Predictive Modelling of Covid-19 Confirmed, Death related and Recovered Cases in Italy, Wuhan, South Korea and India
| International Journal of Applied Information Systems |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 12 - Number 36 |
| Year of Publication: 2021 |
| Authors: Ajinaja Micheal Olalekan, Abiona Akeem Adekunle, Ibiyomi Michael Adeniyi |
10.5120/ijais2020451899
|
Ajinaja Micheal Olalekan, Abiona Akeem Adekunle, Ibiyomi Michael Adeniyi . Predictive Modelling of Covid-19 Confirmed, Death related and Recovered Cases in Italy, Wuhan, South Korea and India. International Journal of Applied Information Systems. 12, 36 ( January 2021), 7-19. DOI=10.5120/ijais2020451899
Abstract
The coronavirus pandemic has been ravaging the world since December 2019.The pandemic started from Wuhan in China late 2019 and has greatly affected thousands of people with the impact felt in different economy. This required the need to observe, monitor and predict infected, recovered and death related COVID-19 cases for suitable control. Prophet was used to predict future values by creating a base model with no tweaking of seasonality-related parameters and additional regressors. The data was extracted from the John Hopkins state of art data centre website which spanned from 31st January 2020 to 25th Match 2020. We adopted the machine learning library (prophet) to predict a week data and also showed analysis using Plotly. Italy, Wuhan, South Korea and India were compared and analysed using the model. The results showed that confirmed cases in Italy continually kept rising exponentially with a certain fixed pattern, confirmed cases in India continually kept rising exponentially with no fixed pattern with the graph attaining sigmoid early but later increased exponentially with no particular fixed pattern which was as a result of very less test done in India, confirmed cases in South Korea was rising gradually with sigmoid attained at some level with the government able to reduce the spike in infected persons and in Wuhan, there have been almost a negligible number confirmed cases in a week. This prediction showed how government can prepare for any eventualities in the future and be able to combat future pandemic occurrence, Emphasis should be on testing and enforcing early lockdown
References
- Zlatan Car, Sandi Baressi Šegota , Nikola Andelic, Ivan Lorencin, and Vedran Mrzljak, “Modeling the Spread of COVID-19 Infection Using a Multilayer Perceptron”, 2020 Computational and Mathematical Methods in Medicine, Volume 2020, Article ID 5714714, pg. 1 - 10. DOI://doi.org/10.1155/2020/5714714
- 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, 249; pg. 1 – 36. doi: 10.3390/a13100249
- Sirage Zeynu Ahmed “Analysis and forecasting the outbreak of covid-19 in Ethiopia using Machine learning”. European Journal of Computer Science and Information Technology Vol.8, No.4, pp.1-13, August, 2020
- Gergo Pinter, Imre Felde, Amir Mosavi, Pedram Ghamisi and Richard Gloaguen, 2017 “COVID-19 Pandemic Prediction for Hungary; a Hybrid Machine Learning Approach” Hungarian State and the European Union.
- Durga Mahesh Matta and Meet Kumar Saraf, , May 2020 .“Prediction of COVID-19 using Machine Learning Techniques”. Bachelor of Science in Computer Science thesis, Faculty of Computing, Blekinge Institute of Technology, 371 79 Karlskrona, Sweden.
- Richa Tamhane and Sumeet Mulge, “Prediction of COVID-19 Outbreak using Machine Learning”, May 2020. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 (p-ISSN: 2395-0072), Volume: 07 Issue: 05
- Aman Khakharia, Vruddhi Shah, Sankalp Jain, Jash Shah, Amanshu Tiwari, Prathamesh Daphal, Mahesh Warang and Ninad Mehendale, “Outbreak Prediction of COVID-19 for Dense and Populated Countries Using Machine Learning”; Annals of Data Science, DOI: https://doi.org/10.1007/s40745-020-00314-9, October 2020
- Wei Feng and Ying-Hui Quan, “Trend and forecasting of the COVID-19 outbreak in China”, February 2020. Journal of Infection. Pg 472 – 472, vol: 80 DOI: https://doi.org/10.1016/j.jinf.2020.02.014
- Zeynep Ceylan, April 2020 , “Estimation of COVID-19 prevalence in Italy, Spain, and France”, Science of the Total Environment Journal,.
- Kayode Ayinde, Adewale F. Lukman, Rauf I. Rauf, Olusegun O. Alabi, Charles E. Okon and Opeyemi E. Ayinde, May 2020. “Modeling Nigerian Covid-19 cases: A comparative analysis of models and estimators”, Chaos, Solitons and Fractals Nonlinear Science, and Nonequilibrium and Complex Phenomena
- Parul Arora, Himanshu Kumar and Bijaya Ketan Panigrahi, June 2020. “Prediction and analysis of COVID-19 positive cases using deep learning models: A descriptive case study of India”: Chaos, Solitons and Fractals Nonlinear Science, and Nonequilibrium and Complex Phenomena, DOI: https://doi.org/10.1016/j.chaos.2020.110017. Pg. 1 – 9.
- Vikas Chaurasia and Saurabh Pal, 2020. “Covid-19 Pandemic: Application of Machine Learning Time Series Analysis for Prediction of Human Future”; Research Square Journal DOI: https://doi.org/10.21203/rs.3.rs-39149/v1; pg. 1 – 6,
- Das AK, Mishra S and Saraswathy Gopalan S. 2020. Predicting CoVID-19 community mortality risk using machine learning and development of an online prognostic tool. PeerJ 8:e10083: DOI 10.7717/peerj.10083
- Roseline O. Ogundokun, Adewale F. Lukman, Golam B.M. Kibria, Joseph B. Awotunde and Benedita B. Aladeitan, August 2020: “Predictive modelling of COVID-19 confirmed cases in Nigeria Infectious Disease Modelling” Infectious Disease Modelling 5: 543 – 548.
- Mostafa Salaheldin and Abdelsalam Abotaleb. April, 2020; “Predicting COVID-19 Cases Using Some Statistical Models: An Application to the Cases Reported in China Italy and USA”; Academic Journal of Applied Mathematical Sciences. ISSN(e): 2415-2188, ISSN(p): 2415-5225, Vol. 6, Issue. 4, pp: 32-40, URL: https://arpgweb.com/journal/journal/17, DOI: https://doi.org/10.32861/ajams.64.32.40.
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