Targeted Vaccination for Covid-19 Based on Machine Learning Model: A Case Study of Jobs' Prioritization

Document Type : Research Paper

Authors

1 School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.

2 School of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran.

Abstract

Today, the spread of the coronavirus has affected many models of life. Despite making some vaccines such as AstraZeneca, Pfizer-BioNTech, and Moderna, vaccination has not been widely used. Due to the lack of vaccines in sufficient numbers, COVID-19 vaccination is usually performed in several phases. Using machine learning methods can be influential in selecting the nominated groups to achieve an acceptable level of immunity called herd immunity. The approach of this article is to introduce the high-risk occupational groups that are most exposed to the coronavirus to the vaccination phasing is done effectively, to provide the fastest immunity. The Genetic algorithm was employed to feature selection for getting appropriate performance in the predictive model. The machine learning regression algorithms, such as decision tree, random forest, and logistic regression, were utilized to build a predictive model, in which random forest with 88.3 % accuracy is selected by comparison among other algorithms for this purpose. The different jobs' categories priorities were determined due to the feature importance based on coefficients to get the vaccine, which this help to reduce the covid 19 deaths.

Keywords

References

 
       [1]        Gandon, S. and S. Lion, Targeted vaccination and the speed of SARS-CoV-2 adaptation. Proceedings of the National Academy of Sciences, 2022. 119(3).
       [2]        Chen, F.J.J.o.t.b., A mathematical analysis of public avoidance behavior during epidemics using game theory. 2012. 302: p. 18-28.
       [3]        Wells, C.R., E.Y. Klein, and C.T.J.P.C.B. Bauch, Policy resistance undermines superspreader vaccination strategies for influenza. 2013. 9(3): p. e1002945.
       [4]        Stockwell, M.S., A.G.J.H.v. Fiks, and immunotherapeutics, Utilizing health information technology to improve vaccine communication and coverage. 2013. 9(8): p. 1802-1811.
       [5]        Dharmawardana, K., et al. Predictive model for the dengue incidences in Sri Lanka using mobile network big data. in 2017 IEEE International Conference on Industrial and Information Systems (ICIIS). 2017. IEEE.
       [6]        Bhopal, S. and M.J.A.o.D.i.C. Nielsen, Vaccine hesitancy in low-and middle-income countries: potential implications for the COVID-19 response. 2021. 106(2): p. 113-114.
       [7]        Altmann, D.M., D.C. Douek, and R.J.J.T.L. Boyton, What policy makers need to know about COVID-19 protective immunity. 2020. 395(10236): p. 1527-1529.
       [8]        Holzmann-Littig, C., et al., COVID-19 vaccination acceptance among healthcare workers in Germany. 2021.
       [9]        Madison, A.A., et al., Psychological and behavioral predictors of vaccine efficacy: Considerations for COVID-19. 2021. 16(2): p. 191-203.
     [10]      Cook, T. and J.J.A. Roberts, Impact of vaccination by priority group on UK deaths, hospital admissions and intensive care admissions from COVID‐19. 2021. 76(5): p. 608-616.
     [11]      Razai, M.S., et al., Covid-19 vaccine hesitancy among ethnic minority groups. 2021, British Medical Journal Publishing Group.
     [12]      Buckner, J.H., G. Chowell, and M.R.J.P.o.t.N.A.o.S. Springborn, Dynamic prioritization of COVID-19 vaccines when social distancing is limited for essential workers. 2021. 118(16).
     [13]      Rachaniotis, N.P., et al., A two-phase stochastic dynamic model for COVID-19 mid-term policy recommendations in Greece: a pathway towards mass vaccination. 2021. 18(5): p. 2497.
     [14]      Jadidi, M., et al., A two-step vaccination technique to limit COVID-19 spread using mobile data. 2021. 70: p. 102886.
     [15]      Sun, X., et al. Targeted vaccination based on a wireless sensor system. in 2015 IEEE International Conference on Pervasive Computing and Communications (PerCom). 2015. IEEE.
     [16]      Salvamani, S., et al., Understanding the dynamics of COVID-19; implications for therapeutic intervention, vaccine development and movement control. 2020. 77(4): p. 168-184.
     [17]      Wilder-Smith, A. and D.O.J.J.o.t.m. Freedman, Isolation, quarantine, social distancing and community containment: pivotal role for old-style public health measures in the novel coronavirus (2019-nCoV) outbreak. 2020.
     [18]      Behdinian, A., et al., An integrating Machine learning algorithm and simulation method for improving Software Project Management: A real case study. Journal of Quality Engineering and Production Optimization, 2022.
     [19]      Amani, M.A. and F. Marinello, A Deep Learning-Based Model to Reduce Costs and Increase Productivity in the Case of Small Datasets: A Case Study in Cotton Cultivation. Agriculture, 2022. 12(2): p. 267.
     [20]      Rahman, M., et al., Machine Learning on the COVID-19 Pandemic, Human Mobility and Air Quality: A Review. 2021.
     [21]      Al Zobbi, M., et al., Measurement Method for Evaluating the Lockdown Policies during the COVID-19 Pandemic. 2020. 17(15): p. 5574.
     [22]      Asad, S.M., et al. Travelers-Tracing and Mobility Profiling Using Machine Learning in Railway Systems. in 2020 International Conference on UK-China Emerging Technologies (UCET). 2020. IEEE.
     [23]      Hosseini, M.S. and A.M. Gittler. Factors Influencing Human Mobility During The COVID-19 Pandemic in Selected Countries of Europe and North America. in 2020 IEEE International Conference on Big Data (Big Data). 2020. IEEE.
     [24]      Ahmadi, M.R. and R.S. Shahabi, Cutoff grade optimization in open pit mines using genetic algorithm. Resources Policy, 2018. 55: p. 184-191.
     [25]      Amani, M.A., et al., A machine learning-based model for the estimation of the temperature-dependent moduli of graphene oxide reinforced nanocomposites and its application in a thermally affected buckling analysis. Engineering with Computers, 2021. 37(3): p. 2245-2255.
     [26]      Lan, T., et al., A comparative study of decision tree, random forest, and convolutional neural network for spread-F identification. Advances in Space Research, 2020. 65(8): p. 2052-2061.
     [27]      Joshi, T., et al., An improved ant colony optimization with correlation and Gini importance for feature selection, in Communication and Intelligent Systems. 2021, Springer. p. 629-641.
 
Advances in Industrial Engineering
Volume 55, Issue 4
October 2021
Pages 433-446

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