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Weekly Nowcasting of New COVID-19 Cases Using Past Viral Load Measurements
The rapid spread of the coronavirus disease COVID-19 has imposed clinical and financial burdens on hospitals and governments attempting to provide patients with medical care and implement disease-controlling policies. The transmissibility of the disease was shown to be correlated with the patient’s...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9317659/ https://www.ncbi.nlm.nih.gov/pubmed/35891394 http://dx.doi.org/10.3390/v14071414 |
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author | Khalil, Athar Al Handawi, Khalil Mohsen, Zeina Abdel Nour, Afif Feghali, Rita Chamseddine, Ibrahim Kokkolaras, Michael |
author_facet | Khalil, Athar Al Handawi, Khalil Mohsen, Zeina Abdel Nour, Afif Feghali, Rita Chamseddine, Ibrahim Kokkolaras, Michael |
author_sort | Khalil, Athar |
collection | PubMed |
description | The rapid spread of the coronavirus disease COVID-19 has imposed clinical and financial burdens on hospitals and governments attempting to provide patients with medical care and implement disease-controlling policies. The transmissibility of the disease was shown to be correlated with the patient’s viral load, which can be measured during testing using the cycle threshold (Ct). Previous models have utilized Ct to forecast the trajectory of the spread, which can provide valuable information to better allocate resources and change policies. However, these models combined other variables specific to medical institutions or came in the form of compartmental models that rely on epidemiological assumptions, all of which could impose prediction uncertainties. In this study, we overcome these limitations using data-driven modeling that utilizes Ct and previous number of cases, two institution-independent variables. We collected three groups of patients (n = 6296, n = 3228, and n = 12,096) from different time periods to train, validate, and independently validate the models. We used three machine learning algorithms and three deep learning algorithms that can model the temporal dynamic behavior of the number of cases. The endpoint was 7-week forward number of cases, and the prediction was evaluated using mean square error (MSE). The sequence-to-sequence model showed the best prediction during validation (MSE = 0.025), while polynomial regression (OLS) and support vector machine regression (SVR) had better performance during independent validation (MSE = 0.1596, and MSE = 0.16754, respectively), which exhibited better generalizability of the latter. The OLS and SVR models were used on a dataset from an external institution and showed promise in predicting COVID-19 incidences across institutions. These models may support clinical and logistic decision-making after prospective validation. |
format | Online Article Text |
id | pubmed-9317659 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-93176592022-07-27 Weekly Nowcasting of New COVID-19 Cases Using Past Viral Load Measurements Khalil, Athar Al Handawi, Khalil Mohsen, Zeina Abdel Nour, Afif Feghali, Rita Chamseddine, Ibrahim Kokkolaras, Michael Viruses Article The rapid spread of the coronavirus disease COVID-19 has imposed clinical and financial burdens on hospitals and governments attempting to provide patients with medical care and implement disease-controlling policies. The transmissibility of the disease was shown to be correlated with the patient’s viral load, which can be measured during testing using the cycle threshold (Ct). Previous models have utilized Ct to forecast the trajectory of the spread, which can provide valuable information to better allocate resources and change policies. However, these models combined other variables specific to medical institutions or came in the form of compartmental models that rely on epidemiological assumptions, all of which could impose prediction uncertainties. In this study, we overcome these limitations using data-driven modeling that utilizes Ct and previous number of cases, two institution-independent variables. We collected three groups of patients (n = 6296, n = 3228, and n = 12,096) from different time periods to train, validate, and independently validate the models. We used three machine learning algorithms and three deep learning algorithms that can model the temporal dynamic behavior of the number of cases. The endpoint was 7-week forward number of cases, and the prediction was evaluated using mean square error (MSE). The sequence-to-sequence model showed the best prediction during validation (MSE = 0.025), while polynomial regression (OLS) and support vector machine regression (SVR) had better performance during independent validation (MSE = 0.1596, and MSE = 0.16754, respectively), which exhibited better generalizability of the latter. The OLS and SVR models were used on a dataset from an external institution and showed promise in predicting COVID-19 incidences across institutions. These models may support clinical and logistic decision-making after prospective validation. MDPI 2022-06-28 /pmc/articles/PMC9317659/ /pubmed/35891394 http://dx.doi.org/10.3390/v14071414 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Khalil, Athar Al Handawi, Khalil Mohsen, Zeina Abdel Nour, Afif Feghali, Rita Chamseddine, Ibrahim Kokkolaras, Michael Weekly Nowcasting of New COVID-19 Cases Using Past Viral Load Measurements |
title | Weekly Nowcasting of New COVID-19 Cases Using Past Viral Load Measurements |
title_full | Weekly Nowcasting of New COVID-19 Cases Using Past Viral Load Measurements |
title_fullStr | Weekly Nowcasting of New COVID-19 Cases Using Past Viral Load Measurements |
title_full_unstemmed | Weekly Nowcasting of New COVID-19 Cases Using Past Viral Load Measurements |
title_short | Weekly Nowcasting of New COVID-19 Cases Using Past Viral Load Measurements |
title_sort | weekly nowcasting of new covid-19 cases using past viral load measurements |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9317659/ https://www.ncbi.nlm.nih.gov/pubmed/35891394 http://dx.doi.org/10.3390/v14071414 |
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