Machine Learning Model for Predicting Number of COVID-19 Cases in Countries with Low Number of Tests

Hashim, Samy; Farooq, Sally; Syriopoulos, Eleni; la Lande Cremer, Kai de; Vogt, Alexander; de Jong, Nol; Aguado, Victor L.; Popescu, Mihai; Mohamed, Ashraf K.; Amin, Muhamed

Machine Learning Model for Predicting Number of COVID-19 Cases in Countries with Low Number of Tests

Samy Hashim¹, Sally Farooq¹, Eleni Syriopoulos¹, Kai de la Lande Cremer¹, Alexander Vogt¹, Nol de Jong¹, Victor L. Aguado¹, Mihai Popescu¹, Ashraf K. Mohamed¹, Muhamed Amin^{1, *}

¹ Department of Sciences, University College Groningen, Hoendiepskade 23/24 9718 BG Groningen, Netherlands

Article Information

Identifiers and Pagination:

Year: 2022
Volume: 15
E-location ID: e187503622208290
Publisher ID: e187503622208290
DOI: 10.2174/18750362-v15-e2208290

Article History:

Received Date: 16/11/2021
Revision Received Date: 16/2/2022
Acceptance Date: 16/3/2022
Electronic publication date: 25/10/2022
Collection year: 2022

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© 2022 Hashim et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

^* Address correspondence to this author at the Department of Sciences, University College Groningen, Hoendiepskade 23/24 9718 BG Groningen, Netherlands; E-mail: m.a.a.amin@rug.nl

Background:

The COVID-19 pandemic has presented a series of new challenges to governments and healthcare systems. Testing is one important method for monitoring and controlling the spread of COVID-19. Yet with a serious discrepancy in the resources available between rich and poor countries, not every country is able to employ widespread testing.

Methods and Objective:

Here, we have developed machine learning models for predicting the prevalence of COVID-19 cases in a country based on multilinear regression and neural network models. The models are trained on data from US states and tested against the reported infections in European countries. The model is based on four features: Number of tests, Population Percentage, Urban Population, and Gini index.

Results:

The population and the number of tests have the strongest correlation with the number of infections. The model was then tested on data from European countries for which the correlation coefficient between the actual and predicted cases R² was found to be 0.88 in the multi-linear regression and 0.91 for the neural network model

Conclusion:

The model predicts that the actual prevalence of COVID-19 infection in countries where the number of tests is less than 10% of their populations is at least 26 times greater than the reported numbers.

Keywords: Machine learning, Model, COVID-19 cases, Healthcare systems, Testing, RNA viruses.

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RESEARCH ARTICLE

Machine Learning Model for Predicting Number of COVID-19 Cases in Countries with Low Number of Tests

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