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Defining genomic epidemiology thresholds for common-source bacterial outbreaks: a modelling study

BACKGROUND: Epidemiological surveillance relies on microbial strain typing, which defines genomic relatedness among isolates to identify case clusters and their potential sources. Although predefined thresholds are often applied, known outbreak-specific features such as pathogen mutation rate and du...

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Autores principales: Duval, Audrey, Opatowski, Lulla, Brisse, Sylvain
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10156608/
https://www.ncbi.nlm.nih.gov/pubmed/37003286
http://dx.doi.org/10.1016/S2666-5247(22)00380-9
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author Duval, Audrey
Opatowski, Lulla
Brisse, Sylvain
author_facet Duval, Audrey
Opatowski, Lulla
Brisse, Sylvain
author_sort Duval, Audrey
collection PubMed
description BACKGROUND: Epidemiological surveillance relies on microbial strain typing, which defines genomic relatedness among isolates to identify case clusters and their potential sources. Although predefined thresholds are often applied, known outbreak-specific features such as pathogen mutation rate and duration of source contamination are rarely considered. We aimed to develop a hypothesis-based model that estimates genetic distance thresholds and mutation rates for point-source single-strain food or environmental outbreaks. METHODS: In this modelling study, we developed a forward model to simulate bacterial evolution at a specific mutation rate (μ) over a defined outbreak duration (D). From the distribution of genetic distances expected under the given outbreak parameters and sample isolation dates, we estimated a distance threshold beyond which isolates should not be considered as part of the outbreak. We embedded the model into a Markov Chain Monte Carlo inference framework to estimate the most probable mutation rate or time since source contamination, which are both often imprecisely documented. A simulation study validated the model over realistic durations and mutation rates. We then identified and analysed 16 published datasets of bacterial source-related outbreaks; datasets were included if they were from an identified foodborne outbreak and if whole-genome sequence data and collection dates for the described isolates were available. FINDINGS: Analysis of simulated data validated the accuracy of our framework in both discriminating between outbreak and non-outbreak cases and estimating the parameters D and μ from outbreak data. Precision of estimation was much higher for high values of D and μ. Sensitivity of outbreak cases was always very high, and specificity in detecting non-outbreak cases was poor for low mutation rates. For 14 of the 16 outbreaks, the classification of isolates as being outbreak-related or sporadic is consistent with the original dataset. Four of these outbreaks included outliers, which were correctly classified as being beyond the threshold of exclusion estimated by our model, except for one isolate of outbreak 4. For two outbreaks, both foodborne Listeria monocytogenes, conclusions from our model were discordant with published results: in one outbreak two isolates were classified as outliers by our model and in another outbreak our algorithm separated food samples into one cluster and human samples into another, whereas the isolates were initially grouped together based on epidemiological and genetic evidence. Re-estimated values of the duration of outbreak or mutation rate were largely consistent with a priori defined values. However, in several cases the estimated values were higher and improved the fit with the observed genetic distance distribution, suggesting that early outbreak cases are sometimes missed. INTERPRETATION: We propose here an evolutionary approach to the single-strain conundrum by estimating the genetic threshold and proposing the most probable cluster of cases for a given outbreak, as determined by its particular epidemiological and microbiological properties. This forward model, applicable to foodborne or environmental-source single point case clusters or outbreaks, is useful for epidemiological surveillance and may inform control measures. FUNDING: European Union Horizon 2020 Research and Innovation Programme.
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spelling pubmed-101566082023-05-05 Defining genomic epidemiology thresholds for common-source bacterial outbreaks: a modelling study Duval, Audrey Opatowski, Lulla Brisse, Sylvain Lancet Microbe Articles BACKGROUND: Epidemiological surveillance relies on microbial strain typing, which defines genomic relatedness among isolates to identify case clusters and their potential sources. Although predefined thresholds are often applied, known outbreak-specific features such as pathogen mutation rate and duration of source contamination are rarely considered. We aimed to develop a hypothesis-based model that estimates genetic distance thresholds and mutation rates for point-source single-strain food or environmental outbreaks. METHODS: In this modelling study, we developed a forward model to simulate bacterial evolution at a specific mutation rate (μ) over a defined outbreak duration (D). From the distribution of genetic distances expected under the given outbreak parameters and sample isolation dates, we estimated a distance threshold beyond which isolates should not be considered as part of the outbreak. We embedded the model into a Markov Chain Monte Carlo inference framework to estimate the most probable mutation rate or time since source contamination, which are both often imprecisely documented. A simulation study validated the model over realistic durations and mutation rates. We then identified and analysed 16 published datasets of bacterial source-related outbreaks; datasets were included if they were from an identified foodborne outbreak and if whole-genome sequence data and collection dates for the described isolates were available. FINDINGS: Analysis of simulated data validated the accuracy of our framework in both discriminating between outbreak and non-outbreak cases and estimating the parameters D and μ from outbreak data. Precision of estimation was much higher for high values of D and μ. Sensitivity of outbreak cases was always very high, and specificity in detecting non-outbreak cases was poor for low mutation rates. For 14 of the 16 outbreaks, the classification of isolates as being outbreak-related or sporadic is consistent with the original dataset. Four of these outbreaks included outliers, which were correctly classified as being beyond the threshold of exclusion estimated by our model, except for one isolate of outbreak 4. For two outbreaks, both foodborne Listeria monocytogenes, conclusions from our model were discordant with published results: in one outbreak two isolates were classified as outliers by our model and in another outbreak our algorithm separated food samples into one cluster and human samples into another, whereas the isolates were initially grouped together based on epidemiological and genetic evidence. Re-estimated values of the duration of outbreak or mutation rate were largely consistent with a priori defined values. However, in several cases the estimated values were higher and improved the fit with the observed genetic distance distribution, suggesting that early outbreak cases are sometimes missed. INTERPRETATION: We propose here an evolutionary approach to the single-strain conundrum by estimating the genetic threshold and proposing the most probable cluster of cases for a given outbreak, as determined by its particular epidemiological and microbiological properties. This forward model, applicable to foodborne or environmental-source single point case clusters or outbreaks, is useful for epidemiological surveillance and may inform control measures. FUNDING: European Union Horizon 2020 Research and Innovation Programme. Elsevier Ltd 2023-05 /pmc/articles/PMC10156608/ /pubmed/37003286 http://dx.doi.org/10.1016/S2666-5247(22)00380-9 Text en © 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Articles
Duval, Audrey
Opatowski, Lulla
Brisse, Sylvain
Defining genomic epidemiology thresholds for common-source bacterial outbreaks: a modelling study
title Defining genomic epidemiology thresholds for common-source bacterial outbreaks: a modelling study
title_full Defining genomic epidemiology thresholds for common-source bacterial outbreaks: a modelling study
title_fullStr Defining genomic epidemiology thresholds for common-source bacterial outbreaks: a modelling study
title_full_unstemmed Defining genomic epidemiology thresholds for common-source bacterial outbreaks: a modelling study
title_short Defining genomic epidemiology thresholds for common-source bacterial outbreaks: a modelling study
title_sort defining genomic epidemiology thresholds for common-source bacterial outbreaks: a modelling study
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10156608/
https://www.ncbi.nlm.nih.gov/pubmed/37003286
http://dx.doi.org/10.1016/S2666-5247(22)00380-9
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