Modelling the effect of vaccination on the spread of COVID-19 via a novel evolutionary ensemble learning algorithm

The spread of the COVID-19 disease has caused a lot of problems for every country around the world. To curb the pandemic, governments have issued various policies, including vaccination. Depending on the percentage of the vaccinated population, the pandemic responds differently to the policies. This paper proposes a modelling algorithm that takes as input the percentage of the vaccinated population and the policies taken by governments and generates as output a prediction of the number of newly infected cases. Then, this model is used as the fitness function in an optimisation algorithm, which for a population with a certain percentage of vaccinated people, searches through the set of policies and finds the best set of policies that minimises the cost to society and the number of infected people. To build the model, an ensemble learning algorithm is proposed, which is a combination of different learning algorithms. In this algorithm, an evolutionary diversifier algorithm is proposed to generate the base learners. The algorithm chooses different subsets of features for each base learner to maximise diversity among them. Then, an evolutionary process is adopted to choose from the base learners a subset that optimises the prediction accuracy of the model. The proposed algorithms are tested on a well-known data set about government policies, the percentage of the population vaccinated, and the number of infected cases. Experimental studies suggest better performance for the proposed ensemble learning algorithm compared to existing ones. Multi-objective optimisation of the policies is also proposed and tested on the model, and the results are presented in this paper.