Prediction of COVID-19 Individual Susceptibility using Demographic Data: A Case Study on Saudi Arabia

Abstract

The transmission dynamics of COVID-19 depend largely on the structure of the underlying contact network and individuals susceptibility. The latter factor is a primary concern of Covid-19 since recent studies reported conflicting conclusions regarding the characteristics of susceptible individuals. A susceptibility classification model of individuals is of great importance to governments and decision makers, as it facilitates imposing personalized protective strategies, which can save lives and minimize social and economic consequences. Machine learning models have been successfully used to predict individual susceptibility to different diseases such as cancer and visceral fat associated diseases. Existing contributions that predict susceptibility of COVID-19 are limited and use a large number of features including clinical data and medical history, which may be hard to obtain or unavailable. In this work, we investigate the use of machine learning models, namely multilayer perceptron (MLP), support vector machine (SVM), decision tree (DT), and random forest (RF) to predict susceptibility of individuals based on demographic data, including age, gender, nationality, and location. We use a dataset of test records of all individuals who were admitted to take nasopharyngeal swabs for COVID-19 in Saudi Arabia between March 2, 2020 until April 25, 2020. Our experiments show promising results with prediction accuracies of 85.6%, 85.3%, 77.2%, and 74.2% using DT, RF, MLP, and SVM, respectively.