CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Data Analysis, Modeling and Forecasting of COVID-19 using Machine Learning

by Toshaniwali Bhargav, Toran Verma
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 11
Year of Publication: 2021
Authors: Toshaniwali Bhargav, Toran Verma
10.5120/ijca2021921425

Toshaniwali Bhargav, Toran Verma . Data Analysis, Modeling and Forecasting of COVID-19 using Machine Learning. International Journal of Computer Applications. 183, 11 ( Jun 2021), 39-46. DOI=10.5120/ijca2021921425

@article{ 10.5120/ijca2021921425,
author = { Toshaniwali Bhargav, Toran Verma },
title = { Data Analysis, Modeling and Forecasting of COVID-19 using Machine Learning },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2021 },
volume = { 183 },
number = { 11 },
month = { Jun },
year = { 2021 },
issn = { 0975-8887 },
pages = { 39-46 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number11/31973-2021921425/ },
doi = { 10.5120/ijca2021921425 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:16:32.035622+05:30
%A Toshaniwali Bhargav
%A Toran Verma
%T Data Analysis, Modeling and Forecasting of COVID-19 using Machine Learning
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 11
%P 39-46
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this work, we present a global analysis and exploring the World Wide data of Covid-19. SIR Model and Mathematical Curve Fitting Method have been used to predict the future spread of the pandemic in India. Odisha, Madhya Pradesh, and Chhattisgarh three states of INDIA are selected based on the pattern of the disease spread in INDIA. The parameters of the models are estimated by utilizing real-time data. The models predict the ending of the pandemic in these states and estimate the number of people that would be affected under the prevailing conditions. For analyzing and designing this model available datasets have been used. These consist of a record of cases globally from March 21st to June 1st, 2020. Hence we will make two predictions from our model. The first model will analyze the COVID confirmed cases globally. And, the second model fetches real-time data through which we will predict total confirmed cases, total deaths, and total recovery in India. This model proposes the aim for understanding its everyday exponential behaviour along with the prediction of future reach ability of the COVID-2019 across the nations by utilizing real-time. With lockdown continuing even after May 2020, we expect our model to reflect the peak cases either in the month of September or October 2020.

References
  1. Bai, H.X., Hsieh, B., Xiong, Z., Halsey, K., Choi, J.W., Tran, T.M.L., Pan, I., Shi, L.B., Wang, D.C., Mei, J. and Jiang, X.L., 2020. Performance of radiologists in differentiating COVID-19 from non-COVID-19 viral pneumonia at chest CT. Radiology, 296(2), pp.E46-E54.
  2. Punn, N.S., Sonbhadra, S.K. and Agarwal, S., 2020. COVID-19 epidemic analysis using machine learning and deep learning algorithms. MedRxiv.
  3. Roosa, K., Lee, Y., Luo, R., Kirpich, A., Rothenberg, R., Hyman, J.M., Yan, P. and Chowell, G., 2020. Real-time forecasts of the COVID-19 epidemic in China from February 5th to February 24th, 2020. Infectious Disease Modelling, 5, pp.256-263.
  4. He, K., Zhang, X., Ren, S. and Sun, J., 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
  5. Li, L., Zhang, Q., Wang, X., Zhang, J., Wang, T., Gao, T.L., Duan, W., Tsoi, K.K.F. and Wang, F.Y., 2020. Characterizing the propagation of situational information in social media during covid-19 epidemic: A case study on weibo. IEEE Transactions on Computational Social Systems, 7(2), pp.556-562.
  6. Zhang, S., Diao, M., Yu, W., Pei, L., Lin, Z. and Chen, D., 2020. Estimation of the reproductive number of novel coronavirus (COVID-19) and the probable outbreak size on the Diamond Princess cruise ship: A data-driven analysis. International journal of infectious diseases, 93, pp.201-204.
  7. Hamzah, F.B., Lau, C., Nazri, H., Ligot, D.V., Lee, G., Tan, C.L., Shaib, M.K.M., Zaidon, U.H., Abdullah, A. and Chung, M.H., 2020. CoronaTracker: worldwide COVID-19 outbreak data analysis and prediction. Bull World Health Organ, 1(32).
  8. Xu, X., Jiang, X., Ma, C., Du, P., Li, X., Lv, S., Yu, L., Ni, Q., Chen, Y., Su, J. and Lang, G., 2020. A deep learning system to screen novel coronavirus disease 2019 pneumonia. Engineering, 6(10), pp.1122-1129.
  9. Kanne, J.P., 2020. Chest CT findings in 2019 novel coronavirus (2019-nCoV) infections from Wuhan, China: key points for the radiologist.
  10. Chung, M., Bernheim, A., Mei, X., Zhang, N., Huang, M., Zeng, X., Cui, J., Xu, W., Yang, Y., Fayad, Z.A. and Jacobi, A., 2020. CT imaging features of 2019 novel coronavirus (2019-nCoV). Radiology, 295(1), pp.202-207.
  11. Ng, T.W., Turinici, G. and Danchin, A., 2003. A double epidemic model for the SARS propagation. BMC Infectious Diseases, 3(1), pp.1-16.
  12. Mackolil, J. and Mahanthesh, B., 2020. Mathematical Modelling of Coronavirus disease (COVID-19) Outbreak in India using Logistic Growth and SIR Models.
  13. Moremedi, G.M., Kaondera-Shava, R., Lubuma, J.M., Morris, N. and Tsanou, B., 2015. A Simple Mathematical Model for Ebola in Africa. Biomath Communications, 2(1).
  14. Du, S., Wang, J., Zhang, H., Cui, W., Kang, Z., Yang, T., Lou, B., Chi, Y., Long, H., Ma, M. and Yuan, Q., 2020. Predicting COVID-19 using hybrid AI model.
  15. Babbar, S., 2020. Battle with COVID-19 Under Partial to Zero Lockdowns in India. medRxiv.
  16. Beck, B.R., Shin, B., Choi, Y., Park, S. and Kang, K., 2020. Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model. Computational and structural biotechnology journal, 18, pp.784-790.
  17. Mackolil, J. and Mahanthesh, B., 2020. Mathematical Modelling of Coronavirus disease (COVID-19) Outbreak in India using Logistic Growth and SIR Models.
  18. Stübinger, J. and Schneider, L., 2020, June. Epidemiology of coronavirus covid-19: Forecasting the future incidence in different countries. In Healthcare (Vol. 8, No. 2, p. 99). Multidisciplinary Digital Publishing Institute.
  19. Sujath, R., Chatterjee, J.M. and Hassanien, A.E., 2020. A machine learning forecasting model for COVID-19 pandemic in India. Stochastic Environmental Research and Risk Assessment, 34, pp.959-972.
  20. Greenfield, J., Sears, M., Nagrani, R., Mazzaferro, G., Widyastuti, A. and Austin, C.C., the RDA-COVID19-WG.(2020). Common Data Models and Full Spectrum Epidemiology: Epi-STACK architecture for COVID-19 epidemiology datasets. Data Sharing in Epidemiology, p.65.
  21. Official website for data collection https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide and https://www.covid19india.org/
  22. SIR model https://www.lewuathe.com/covid-19-dynamics-with-sir-model.html
  23. https://en.wikipedia.org/wiki/Curve_fitting
Index Terms

Computer Science
Information Sciences

Keywords

COVID-19 Data Analysis Modelling Forecasting SIR Mathematical Curve Fitting SARS-CoV-2 WHO Corona Virus

Powered by PhDFocusTM