More Web Proxy on the site http://driver.im/

article

Research on COVID-19 Prevention and Control Model Based on Evolutionary Games

Authors:

Tian ZhongAuthors Info & Claims

Journal of Global Information Management (JGIM), Volume 30, Issue 10

Pages 1 - 17

https://doi.org/10.4018/JGIM.300818

Published: 29 June 2022 Publication History

Abstract

In view of epidemic prevention costs and social benefits, an evolutionary game model of epidemic prevention and control strategies between government departments and local people was constructed based on evolutionary game theory to explore the influence of strategic behaviors between government departments and local people, and MATLAB was used to conduct systematic simulation of the game model. Studies have shown that local people will cooperate with government departments when they implement surveillance strategies. Reducing the cost of emergency epidemic prevention, and improving the social benefits of epidemic prevention are conducive to the development of government departments towards the direction of supervision strategy, and local people towards the direction of active epidemic prevention strategy, so as to achieve effective epidemic prevention and control.

References

[1]

Abdel-Basset, M., Chang, V., Hawash, H., Chakrabortty, R. K., & Ryan, M. (2021). FSS-2019-nCov: A deep learning architecture for semi-supervised few-shot segmentation of COVID-19 infection. Knowledge-Based Systems, 212, 106647.10.1016/j.knosys.2020.106647

[2]

Abdel-Basset, M., Chang, V., & Mohamed, R. (2020). HSMA_WOA: A hybrid novel Slime mould algorithm with whale optimization algorithm for tackling the image segmentation problem of chest X-ray images. Applied Soft Computing, 95, 106642.10.1016/j.asoc.2020.106642

[3]

Abdel-Basset, M., Chang, V., & Nabeeh, N. A. (2021). An intelligent framework using disruptive technologies for COVID-19 analysis. Technological Forecasting and Social Change, 163, 120431. 10.1016/j.techfore.2020.120431

[4]

Adiga, A., Dubhashi, D., Lewis, B., Marathe, M., Venkatramanan, S., & Vullikanti, A. (2020). Mathematical models for covid-19 pandemic: A comparative analysis. Journal of the Indian Institute of Science, 100(4), 793-807. 10.1007/s41745-020-00200-6

[5]

Alankar, B., Kaur, H., Ahsaan, S., Sharma, G., & Chang, V. (2021). Towards Reviewing an Immediate Impact of COVID-19 on the Integrative World Economy: An Evolving Perspective. Journal of Global Information Management, 30(4), 1–19.

Digital Library

[6]

Alimadadi, A., Aryal, S., Manandhar, I., Munroe, P. B., Joe, B., & Cheng, X. (2020). Artificial intelligence and machine learning to fight COVID-19. Physiological Genomics, 52(4), 200-202. 10.1152/physiolgenomics.00029.2020

[7]

Calafiore, G. C., Novara, C., & Possieri, C. (2020, December). A modified SIR model for the COVID-19 contagion in Italy. In 2020 59th IEEE Conference on Decision and Control (CDC) (pp. 3889-3894). IEEE.10.1109/CDC42340.2020.9304142

[8]

Chang, V., Goble, C., Ramachandran, M., Deborah, L. J., & Behringer, R. (2021). Editorial on Machine Learning, AI and Big Data Methods and Findings for COVID-19. Information Systems Frontiers, 1-5. 10.1007/s10796-021-10216-7

[9]

Chatterjee, K., Chatterjee, K., Kumar, A., & Shankar, S. (2020). Healthcare impact of COVID-19 epidemic in India: A stochastic mathematical model. Medical Journal Armed Forces India, 76(2), 147-155. 10.1016/j.mjafi.2020.03.022

[10]

Chatterjee, S., Sarkar, A., Chatterjee, S., Karmakar, M., & Paul, R. (2021). Studying the progress of COVID-19 outbreak in India using SIRD model. Indian Journal of Physics, 95(9), 1941-1957. 10.1007/s12648-020-01766-8

[11]

Chimmula, V. K. R., & Zhang, L. (2020). Time series forecasting of COVID-19 transmission in Canada using LSTM networks. Chaos, Solitons & Fractals, 135, 109864. 10.1016/j.chaos.2020.109864

[12]

Cho, V., Roll, L. C., Wu, C. H., & Tang, V. (2021). Changing Digital Age in the Wake of COVID-19: How Does Humility Impact on Virtual Leaderless Teams? Journal of Global Information Management, 30(4), 1–23.

[13]

Cooper, I., Mondal, A., & Antonopoulos, C. G. (2020). A SIR model assumption for thespread of COVID-19 in different communities. Chaos, Solitons & Fractals, 139, 110057. 10.1016/j.chaos.2020.110057

[14]

Dhiman, G., Chang, V., Kant Singh, K., & Shankar, A. (2021). Adopt: automatic deep learning and optimization-based approach for detection of novel coronavirus covid-19 disease using x-ray images. Journal of Biomolecular Structure and Dynamics, 1-13. 10.1080/07391102.2021.1875049

[15]

Fernández-Villaverde, J., & Jones, C. I. (2020). Estimating and simulating a sird model of covid-19 for many countries, states. and cities. Technical report, National Bureau of Economic Research. 10.3386/w27128

[16]

Gao, J., Tian, Z., & Yang, X. (2020). Breakthrough: Chloroquine phosphate has shown apparent efficacy in treatment of COVID-19 associated pneumonia in clinical studies. Bioscience Trends, 14(1), 72–73. Advance online publication. 32074550.

[17]

Huang, W., Berube, J., McNamara, M., Saksena, S., Hartman, M., Arshad, T., Bornheimer, S. J., & O’Gorman, M. (2020). Lymphocyte subset counts in COVID‐19 patients: A meta‐analysis. Cytometry. Part A, 97(8), 772–776. 32542842.

[18]

Lalmuanawma, S., Hussain, J., & Chhakchhuak, L. (2020). Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review. Chaos, Solitons & Fractals, 139, 110059. 10.1016/j.chaos.2020.110059

[19]

Li, J., Wang, Y., Wu, J., Ai, J. W., Zhang, H. C., Gamber, M., . . . Sun, W. J. (2021, February). Do Stay at Home Orders and Cloth Face Coverings Control COVID-19 in New York City? Results From a SIER Model Based on Real-world Data. In Open Forum Infectious Diseases (Vol. 8, No. 2, p. ofaa442). Oxford University Press.

[20]

Mandal, M., Jana, S., Nandi, S. K., Khatua, A., Adak, S., & Kar, T. K. (2020). A model based study on the dynamics of COVID-19: Prediction and control. Chaos, Solitons & Fractals, 136, 109889. 10.1016/j.chaos.2020.109889

[21]

Mehla, L., Sheorey, P. A., Tiwari, A. K., & Behl, A. (2021). Paradigm Shift in the Education Sector Amidst COVID-19 to Improve Online Engagement: Opportunities and Challenges. Journal of Global Information Management, 30(5), 1–21.

Digital Library

[22]

Ndaïrou, F., Area, I., Nieto, J. J., & Torres, D. F. (2020). Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan. Chaos, Solitons & Fractals, 135, 109846. 10.1016/j.chaos.2020.109846

[23]

Noureddine, O., Issaoui, N., Medimagh, M., Al-Dossary, O., & Marouani, H. (2021). Quantum chemical studies on molecular structure, AIM, ELF, RDG and antiviral activities of hybrid hydroxychloroquine in the treatment of COVID-19: Molecular docking and DFT calculations. Journal of King Saud University-Science, 33(2), 101334. 10.1016/j.jksus.2020.101334

[24]

Samui, P., Mondal, J., & Khajanchi, S. (2020). A mathematical model for COVID-19 transmission dynamics with a case study of India. Chaos, Solitons & Fractals, 140, 110173. 10.1016/j.chaos.2020.110173

[25]

Sardar, T., Nadim, S. S., Rana, S., & Chattopadhyay, J. (2020). Assessment of lockdown effect in some states and overall India: A predictive mathematical study on COVID-19 outbreak. Chaos, Solitons & Fractals, 139, 110078. 10.1016/j.chaos.2020.110078

[26]

Singh, A., Jindal, V., Sandhu, R., & Chang, V. (2021). A scalable framework for smart COVID surveillance in the workplace using Deep Neural Networks and cloud computing. Expert Systems: International Journal of Knowledge Engineering and Neural Networks, 12704. Advance online publication. 34177036.

[27]

Song, J. W., Zhang, C., Fan, X., Meng, F. P., Xu, Z., Xia, P., ... Zhang, J. Y. (2020). Immunological and inflammatory profiles in mild and severe cases of COVID-19. Nature Communications, 11(1), 1-10. 10.1038/s41467-020-17240-2

[28]

Tai, Y., Gao, B., Li, Q., Yu, Z., Zhu, C., & Chang, V. (2021). Trustworthy and intelligent covid-19 diagnostic iomt through xr and deep learning-based clinic data access. IEEE Internet of Things Journal, 8(21), 15965–15976. Advance online publication.

[29]

Vaishya, R., Javaid, M., Khan, I. H., & Haleem, A. (2020). Artificial Intelligence (AI) applications for COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(4), 337-339. 10.1016/j.dsx.2020.04.012

[30]

Van der Schaar, M., Alaa, A. M., Floto, A., Gimson, A., Scholtes, S., Wood, A., ... & Ercole, A. (2021). How artificial intelligence and machine learning can help healthcare systems respond to COVID-19. Machine Learning, 110(1), 1-14. 10.1007/s10994-020-05928-x

[31]

Yan, L., Zhang, H. T., Goncalves, J., Xiao, Y., Wang, M., Guo, Y., ... Yuan, Y. (2020). An interpretable mortality prediction model for COVID-19 patients. Nature Machine Intelligence, 2(5), 283-288. 10.1038/s42256-020-0180-7

[32]

Yuen, K. S., Ye, Z. W., Fung, S. Y., Chan, C. P., & Jin, D. Y. (2020). SARS-CoV-2 and COVID-19: The most important research questions. Cell & Bioscience, 10(1), 1-5. 10.1186/s13578-020-00404-4

[33]

Zhang, B., Zhou, X., Zhu, C., Song, Y., Feng, F., Qiu, Y., Feng, J., Jia, Q., Song, Q., Zhu, B., & Wang, J. (2020). Immune phenotyping based on the neutrophil-to-lymphocyte ratio and IgG level predicts disease severity and outcome for patients with COVID-19. Frontiers in Molecular Biosciences, 7, 157. 32719810.

Cited By

He JWu JSiau K(2024)Task Scheduling Strategy for 3DPCP Considering Multidynamic Information Perturbation in Green SceneJournal of Global Information Management10.4018/JGIM.35115632:1(1-23)Online publication date: 17-Sep-2024
https://dl.acm.org/doi/10.4018/JGIM.351156
Peng GTalaei-Khoei AYang ALiu C(2023)Valuing Your Patient's OpinionJournal of Global Information Management10.4018/JGIM.32452031:1(1-24)Online publication date: 9-Jun-2023
https://dl.acm.org/doi/10.4018/JGIM.324520

Index Terms

Research on COVID-19 Prevention and Control Model Based on Evolutionary Games

Index terms have been assigned to the content through auto-classification.

Recommendations

How to promote COVID-19 pandemic prevention and control: A game theory analysis

The outbreak of the COVID-19 (Corona Virus Disease 2019) has been becoming the most serious public health event in the world, threatening the health of hundreds of millions of people. The anti-pandemic policies mostly aim to prevent the spread at ...
Machine learning research towards combating COVID-19: Virus detection, spread prevention, and medical assistance
Graphical abstract

Display Omitted
Highlights
- This paper is a survey of the Machine Learning (ML) research done against COVID-19.
Abstract
COVID-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 ...
Applying an Epidemiological Model to Evaluate the Propagation of Misinformation and Legitimate COVID-19-Related Information on Twitter
Social, Cultural, and Behavioral Modeling
Abstract
Evaluating the propagation of ideas on social media platforms such as Twitter allows researchers to gain an understanding of the characteristics of online communication patterns. The expansion and popularity of Twitter has increased the instances ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Journal of Global Information Management

Journal of Global Information Management Volume 30, Issue 10

Jun 2022

111 pages

ISSN:1062-7375

Issue’s Table of Contents

Copyright © 2022.

Publisher

IGI Global

United States

Publication History

Published: 29 June 2022

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 06 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

He JWu JSiau K(2024)Task Scheduling Strategy for 3DPCP Considering Multidynamic Information Perturbation in Green SceneJournal of Global Information Management10.4018/JGIM.35115632:1(1-23)Online publication date: 17-Sep-2024
https://dl.acm.org/doi/10.4018/JGIM.351156
Peng GTalaei-Khoei AYang ALiu C(2023)Valuing Your Patient's OpinionJournal of Global Information Management10.4018/JGIM.32452031:1(1-24)Online publication date: 9-Jun-2023
https://dl.acm.org/doi/10.4018/JGIM.324520

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents