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Cluster detection with random neighbourhood covering: Application to invasive Group A Streptococcal disease
The rapid detection of outbreaks is a key step in the effective control and containment of infectious diseases. In particular, the identification of cases which might be epidemiologically linked is crucial in directing outbreak-containment efforts and shaping the intervention of public health author...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9744322/ https://www.ncbi.nlm.nih.gov/pubmed/36449515 http://dx.doi.org/10.1371/journal.pcbi.1010726 |
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author | Cavallaro, Massimo Coelho, Juliana Ready, Derren Decraene, Valerie Lamagni, Theresa McCarthy, Noel D. Todkill, Dan Keeling, Matt J. |
author_facet | Cavallaro, Massimo Coelho, Juliana Ready, Derren Decraene, Valerie Lamagni, Theresa McCarthy, Noel D. Todkill, Dan Keeling, Matt J. |
author_sort | Cavallaro, Massimo |
collection | PubMed |
description | The rapid detection of outbreaks is a key step in the effective control and containment of infectious diseases. In particular, the identification of cases which might be epidemiologically linked is crucial in directing outbreak-containment efforts and shaping the intervention of public health authorities. Often this requires the detection of clusters of cases whose numbers exceed those expected by a background of sporadic cases. Quantifying exceedances rapidly is particularly challenging when only few cases are typically reported in a precise location and time. To address such important public health concerns, we present a general method which can detect spatio-temporal deviations from a Poisson point process and estimate the odds of an isolate being part of a cluster. This method can be applied to diseases where detailed geographical information is available. In addition, we propose an approach to explicitly take account of delays in microbial typing. As a case study, we considered invasive group A Streptococcus infection events as recorded and typed by Public Health England from 2015 to 2020. |
format | Online Article Text |
id | pubmed-9744322 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-97443222022-12-13 Cluster detection with random neighbourhood covering: Application to invasive Group A Streptococcal disease Cavallaro, Massimo Coelho, Juliana Ready, Derren Decraene, Valerie Lamagni, Theresa McCarthy, Noel D. Todkill, Dan Keeling, Matt J. PLoS Comput Biol Research Article The rapid detection of outbreaks is a key step in the effective control and containment of infectious diseases. In particular, the identification of cases which might be epidemiologically linked is crucial in directing outbreak-containment efforts and shaping the intervention of public health authorities. Often this requires the detection of clusters of cases whose numbers exceed those expected by a background of sporadic cases. Quantifying exceedances rapidly is particularly challenging when only few cases are typically reported in a precise location and time. To address such important public health concerns, we present a general method which can detect spatio-temporal deviations from a Poisson point process and estimate the odds of an isolate being part of a cluster. This method can be applied to diseases where detailed geographical information is available. In addition, we propose an approach to explicitly take account of delays in microbial typing. As a case study, we considered invasive group A Streptococcus infection events as recorded and typed by Public Health England from 2015 to 2020. Public Library of Science 2022-11-30 /pmc/articles/PMC9744322/ /pubmed/36449515 http://dx.doi.org/10.1371/journal.pcbi.1010726 Text en © 2022 Cavallaro 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 Cavallaro, Massimo Coelho, Juliana Ready, Derren Decraene, Valerie Lamagni, Theresa McCarthy, Noel D. Todkill, Dan Keeling, Matt J. Cluster detection with random neighbourhood covering: Application to invasive Group A Streptococcal disease |
title | Cluster detection with random neighbourhood covering: Application to invasive Group A Streptococcal disease |
title_full | Cluster detection with random neighbourhood covering: Application to invasive Group A Streptococcal disease |
title_fullStr | Cluster detection with random neighbourhood covering: Application to invasive Group A Streptococcal disease |
title_full_unstemmed | Cluster detection with random neighbourhood covering: Application to invasive Group A Streptococcal disease |
title_short | Cluster detection with random neighbourhood covering: Application to invasive Group A Streptococcal disease |
title_sort | cluster detection with random neighbourhood covering: application to invasive group a streptococcal disease |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9744322/ https://www.ncbi.nlm.nih.gov/pubmed/36449515 http://dx.doi.org/10.1371/journal.pcbi.1010726 |
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