targeted vaccination for covid-19 based on machine learning model: a case study of jobs’ prioritization

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.