A Novel Ensemble Machine Learning and an Evolutionary Algorithm in Modeling the COVID-19 Epidemic and Optimizing Government Policies

Abstract

The spread of the COVID-19 disease has prompted a need for immediate reaction by governments to curb the pandemic. Many countries have adopted different policies and studies are performed to understand the effect of each of the policies on the growth rate of the infected cases. In this paper, the data about the policies taken by all countries at each date, and the effect of the policies on the growth rate of the pandemic are used to build a model of the pandemic's behaviour. The model takes as input a set of policies and predicts the growth rate of the pandemic. Then, a population-based multi-objective optimisation algorithm is developed which uses the model to search through the policy space and finds a set of policies that minimise the cost induced to the society due to the policies and the growth rate of the pandemic. Because of the complexity of the modelling problem and the uncertainty in measuring the growth rate of the pandemic via the models, an ensemble learning algorithm is proposed in this paper to improve the performance of individual learning algorithms. The ensemble consists of ten learning algorithms and a meta-model algorithm that is built to predict the accuracy of each learning algorithm for a given data record. The meta-model is a set of Support Vector Machine (SVM) algorithms that is used in the aggregation phase of the ensemble algorithm. Because there is uncertainty in measuring the growth rate via the models, a landscape smoothing operator is proposed in the optimisation process which aims at reducing uncertainty. The algorithm is tested on open access data online and experiments on the ensemble learning and the policy optimisation algorithms are performed.

Game and Reference: Efficient Policy Making for Epidemic Prevention and Control

“…For the task of epidemic decision, one of the most important foundations is the model's capability for epidemic prediction. Based on high performance epidemic prediction models, early epidemic policy-making studies usually consider simple policies such as assessing risk level, conducting nucleic acid testing, and reassigning medical supplies etc [8] [9]. The most significant commonality of these studies is that all the policies can be easily determined based on the prediction of future epidemics.…”