Cargando…

Causal, Bayesian, & non-parametric modeling of the SARS-CoV-2 viral load distribution vs. patient’s age

The viral load of patients infected with SARS-CoV-2 varies on logarithmic scales and possibly with age. Controversial claims have been made in the literature regarding whether the viral load distribution actually depends on the age of the patients. Such a dependence would have implications for the C...

Descripción completa

Detalles Bibliográficos
Autores principales: Guardiani, Matteo, Frank, Philipp, Kostić, Andrija, Edenhofer, Gordian, Roth, Jakob, Uhlmann, Berit, Enßlin, Torsten
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9534394/
https://www.ncbi.nlm.nih.gov/pubmed/36197849
http://dx.doi.org/10.1371/journal.pone.0275011
_version_ 1784802530995208192
author Guardiani, Matteo
Frank, Philipp
Kostić, Andrija
Edenhofer, Gordian
Roth, Jakob
Uhlmann, Berit
Enßlin, Torsten
author_facet Guardiani, Matteo
Frank, Philipp
Kostić, Andrija
Edenhofer, Gordian
Roth, Jakob
Uhlmann, Berit
Enßlin, Torsten
author_sort Guardiani, Matteo
collection PubMed
description The viral load of patients infected with SARS-CoV-2 varies on logarithmic scales and possibly with age. Controversial claims have been made in the literature regarding whether the viral load distribution actually depends on the age of the patients. Such a dependence would have implications for the COVID-19 spreading mechanism, the age-dependent immune system reaction, and thus for policymaking. We hereby develop a method to analyze viral-load distribution data as a function of the patients’ age within a flexible, non-parametric, hierarchical, Bayesian, and causal model. The causal nature of the developed reconstruction additionally allows to test for bias in the data. This could be due to, e.g., bias in patient-testing and data collection or systematic errors in the measurement of the viral load. We perform these tests by calculating the Bayesian evidence for each implied possible causal direction. The possibility of testing for bias in data collection and identifying causal directions can be very useful in other contexts as well. For this reason we make our model freely available. When applied to publicly available age and SARS-CoV-2 viral load data, we find a statistically significant increase in the viral load with age, but only for one of the two analyzed datasets. If we consider this dataset, and based on the current understanding of viral load’s impact on patients’ infectivity, we expect a non-negligible difference in the infectivity of different age groups. This difference is nonetheless too small to justify considering any age group as noninfectious.
format Online
Article
Text
id pubmed-9534394
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-95343942022-10-06 Causal, Bayesian, & non-parametric modeling of the SARS-CoV-2 viral load distribution vs. patient’s age Guardiani, Matteo Frank, Philipp Kostić, Andrija Edenhofer, Gordian Roth, Jakob Uhlmann, Berit Enßlin, Torsten PLoS One Research Article The viral load of patients infected with SARS-CoV-2 varies on logarithmic scales and possibly with age. Controversial claims have been made in the literature regarding whether the viral load distribution actually depends on the age of the patients. Such a dependence would have implications for the COVID-19 spreading mechanism, the age-dependent immune system reaction, and thus for policymaking. We hereby develop a method to analyze viral-load distribution data as a function of the patients’ age within a flexible, non-parametric, hierarchical, Bayesian, and causal model. The causal nature of the developed reconstruction additionally allows to test for bias in the data. This could be due to, e.g., bias in patient-testing and data collection or systematic errors in the measurement of the viral load. We perform these tests by calculating the Bayesian evidence for each implied possible causal direction. The possibility of testing for bias in data collection and identifying causal directions can be very useful in other contexts as well. For this reason we make our model freely available. When applied to publicly available age and SARS-CoV-2 viral load data, we find a statistically significant increase in the viral load with age, but only for one of the two analyzed datasets. If we consider this dataset, and based on the current understanding of viral load’s impact on patients’ infectivity, we expect a non-negligible difference in the infectivity of different age groups. This difference is nonetheless too small to justify considering any age group as noninfectious. Public Library of Science 2022-10-05 /pmc/articles/PMC9534394/ /pubmed/36197849 http://dx.doi.org/10.1371/journal.pone.0275011 Text en © 2022 Guardiani et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Guardiani, Matteo
Frank, Philipp
Kostić, Andrija
Edenhofer, Gordian
Roth, Jakob
Uhlmann, Berit
Enßlin, Torsten
Causal, Bayesian, & non-parametric modeling of the SARS-CoV-2 viral load distribution vs. patient’s age
title Causal, Bayesian, & non-parametric modeling of the SARS-CoV-2 viral load distribution vs. patient’s age
title_full Causal, Bayesian, & non-parametric modeling of the SARS-CoV-2 viral load distribution vs. patient’s age
title_fullStr Causal, Bayesian, & non-parametric modeling of the SARS-CoV-2 viral load distribution vs. patient’s age
title_full_unstemmed Causal, Bayesian, & non-parametric modeling of the SARS-CoV-2 viral load distribution vs. patient’s age
title_short Causal, Bayesian, & non-parametric modeling of the SARS-CoV-2 viral load distribution vs. patient’s age
title_sort causal, bayesian, & non-parametric modeling of the sars-cov-2 viral load distribution vs. patient’s age
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9534394/
https://www.ncbi.nlm.nih.gov/pubmed/36197849
http://dx.doi.org/10.1371/journal.pone.0275011
work_keys_str_mv AT guardianimatteo causalbayesiannonparametricmodelingofthesarscov2viralloaddistributionvspatientsage
AT frankphilipp causalbayesiannonparametricmodelingofthesarscov2viralloaddistributionvspatientsage
AT kosticandrija causalbayesiannonparametricmodelingofthesarscov2viralloaddistributionvspatientsage
AT edenhofergordian causalbayesiannonparametricmodelingofthesarscov2viralloaddistributionvspatientsage
AT rothjakob causalbayesiannonparametricmodelingofthesarscov2viralloaddistributionvspatientsage
AT uhlmannberit causalbayesiannonparametricmodelingofthesarscov2viralloaddistributionvspatientsage
AT enßlintorsten causalbayesiannonparametricmodelingofthesarscov2viralloaddistributionvspatientsage