International Journal of Information Engineering and Electronic Business(IJIEEB)
ISSN: 2074-9023 (Print), ISSN: 2074-9031 (Online)
Published By: MECS Press
IJIEEB Vol.15, No.2, Apr. 2023
A Novel Approach in Determining Areas to Lockdown during a Pandemic: COVID-19 as a Case Study
Full Text (PDF, 654KB), PP.30-37
In December 2019, the Novel Coronavirus became a global epidemic. Because of COVID-19, all ongoing plans had been postponed. Lockdowns were imposed in areas where there was an excessive number of patients. Constantly locking down areas had a significant negative influence on the economy, particularly on developing and underdeveloped countries. But the majority of countries were locking down their areas without making any assumptions where some were successful and some were failures. In this situation, this paper presents a novel approach for determining which parts of a country should be immediately placed under lockdown during any pandemic situation while considering the lockdown history at the time of COVID-19. This work makes use of a self-established dataset containing data from several countries of the world and uses the successful presence of lockdown in that area as the target attribute for machine learning algorithms to determine the areas to keep under lockdown in the future. Here, the Random Forest algorithm has provided the highest accuracy of 92.387% indicating that this model can identify the areas with an impressive level of accuracy to retain under lockdown.
Cite This Paper
Md. Motaleb Hossen Manik, "A Novel Approach in Determining Areas to Lockdown during a Pandemic: COVID-19 as a Case Study", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.15, No.2, pp. 30-37, 2023. DOI:10.5815/ijieeb.2023.02.04
“Coronavirus.” https://www.who.int/health-topics/coronavirus#tab=tab_1 (accessed Jul. 01, 2022).
“COVID-19 - Wikipedia.” https://en.wikipedia.org/wiki/COVID-19 (accessed Jul. 01, 2022).
“Coronavirus (COVID-19) origin: Cause and how it spreads.” https://www.medicalnewstoday.com/articles/coronavirus-causes (accessed Jul. 01, 2022).
“COVID Live - Coronavirus Statistics - Worldometer.” https://www.worldometers.info/coronavirus/ (accessed Jul. 01, 2022).
“The great lockdown: was it worth it? CEPS Policy Insights No 2020-11 / May 2020 - CORE Reader.” https://core.ac.uk/reader/322823610 (accessed Jul. 01, 2022).
“IMF and Covid-19.” https://www.imf.org/en/Topics/imf-and-covid19 (accessed Jul. 02, 2022).
“Sweden Faces Coronavirus Without Lockdown - The New York Times.” https://www.nytimes.com/2020/04/28/world/europe/sweden-coronavirus-herd-immunity.html (accessed Jul. 01, 2022).
D. Ding, B. del Pozo Cruz, M. A. Green, and A. E. Bauman, “Is the COVID-19 lockdown nudging people to be more active: a big data analysis,” Br J Sports Med, vol. 54, no. 20, pp. 1183–1184, Oct. 2020, doi: 10.1136/BJSPORTS-2020-102575.
D. Liu et al., “A machine learning methodology for real-time forecasting of the 2019-2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic models,” ArXiv, Apr. 2020, doi: 10.48550/arxiv.2004.04019.
N. Singh, P. · Sanjay, K. Sonbhadra, S. Agarwal, and S. K. Sonbhadra, “COVID-19 Epidemic Analysis using Machine Learning and Deep Learning Algorithms,” medRxiv, p. 2020.04.08.20057679, Jun. 2020, doi: 10.1101/2020.04.08.20057679.
M. M. Rahman et al., “Machine Learning Approaches for Tackling Novel Coronavirus (COVID-19) Pandemic,” SN Computer Science 2021 2:5, vol. 2, no. 5, pp. 1–10, Jul. 2021, doi: 10.1007/S42979-021-00774-7.
M. A. Cole, R. J. R. Elliott, and B. Liu, “The Impact of the Wuhan Covid-19 Lockdown on Air Pollution and Health: A Machine Learning and Augmented Synthetic Control Approach,” Environ Resour Econ (Dordr), vol. 76, no. 4, pp. 553–580, Aug. 2020, doi: 10.1007/S10640-020-00483-4/TABLES/7.
“A Simple Planning Problem for COVID-19 Lockdown.” https://ideas.repec.org/p/nbr/nberwo/26981.html (accessed Jul. 01, 2022).
H. Lau et al., “The positive impact of lockdown in Wuhan on containing the COVID-19 outbreak in China,” J Travel Med, vol. 27, no. 3, pp. 1–7, May 2020, doi: 10.1093/JTM/TAAA037.
S. ; Armbruster and V. Klotzbücher, “Lost in lockdown? COVID-19, social distancing, and mental health in Germany,” 2020, Accessed: Oct. 22, 2022. [Online]. Available: https://www.econstor.eu/handle/10419/218885
M. Owens, E. Townsend, E. Hall, T. Bhatia, R. Fitzgibbon, and F. Miller-Lakin, “Mental Health and Wellbeing in Young People in the UK during Lockdown (COVID-19),” International Journal of Environmental Research and Public Health 2022, Vol. 19, Page 1132, vol. 19, no. 3, p. 1132, Jan. 2022, doi: 10.3390/IJERPH19031132.
S. Agha, “Mental well-being and association of the four factors coping structure model: A perspective of people living in lockdown during COVID-19,” Ethics Med Public Health, vol. 16, p. 100605, Mar. 2021, doi: 10.1016/J.JEMEP.2020.100605.
T. Elmer, K. Mepham, and C. Stadtfeld, “Students under lockdown: Comparisons of students’ social networks and mental health before and during the COVID-19 crisis in Switzerland,” PLoS One, vol. 15, no. 7, p. e0236337, Jul. 2020, doi: 10.1371/JOURNAL.PONE.0236337.
S. Kharroubi and F. Saleh, “Are Lockdown Measures Effective Against COVID-19?,” Front Public Health, vol. 8, p. 610, Oct. 2020, doi: 10.3389/FPUBH.2020.549692/BIBTEX.
L. Zhao, Y. Chen, and D. W. Schaffner, “Comparison of Logistic Regression and Linear Regression in Modeling Percentage Data,” Appl Environ Microbiol, vol. 67, no. 5, pp. 2129–2135, May 2001, doi: 10.1128/AEM.67.5.2129-2135.2001/ASSET/43FD1C24-FD3C-4FD5-8667-38A7AFA5FEEC/ASSETS/GRAPHIC/AM0511815005.JPEG.
A. Patle and D. S. Chouhan, “SVM kernel functions for classification,” 2013 International Conference on Advances in Technology and Engineering, ICATE 2013, 2013, doi: 10.1109/ICADTE.2013.6524743.
M. H. Manik, “Noble Machine Learning Approaches for Lock Downing Area during Coronavirus (COVID-19) Pandemic Waves,” American Journal of Computer Science and Information Technology, vol. 9, no. 8, Aug. 2021, doi: 10.36648/2349-39220.127.116.11.