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Cluster detection with random neighbourhood covering : application to invasive Group A Streptococcal disease
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Cavallaro, Massimo, Coelho, Juliana, Ready, Derren, Decraene, Valerie, Lamagni, Theresa, McCarthy, Noel D., Todkill, Daniel and Keeling, Matt J. (2022) Cluster detection with random neighbourhood covering : application to invasive Group A Streptococcal disease. PLoS Computational Biology, 18 (11). e1010726. doi:10.1371/journal.pcbi.1010726 ISSN 1553-7358.
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Official URL: https://doi.org/10.1371/journal.pcbi.1010726
Abstract
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.
| Item Type: | Journal Article | ||||||||||||||||||||||||||||||
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| Subjects: | Q Science > QR Microbiology R Medicine > RA Public aspects of medicine R Medicine > RC Internal medicine |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Life Sciences (2010- ) Faculty of Science, Engineering and Medicine > Science > Mathematics Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School |
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| Library of Congress Subject Headings (LCSH): | Streptococcus pyogenes, Streptococcal infections, Communicable diseases -- Prevention, Communicable diseases -- Transmission, Communicable diseases -- Detection | ||||||||||||||||||||||||||||||
| Journal or Publication Title: | PLoS Computational Biology | ||||||||||||||||||||||||||||||
| Publisher: | Public Library of Science | ||||||||||||||||||||||||||||||
| ISSN: | 1553-7358 | ||||||||||||||||||||||||||||||
| Official Date: | 30 November 2022 | ||||||||||||||||||||||||||||||
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| Volume: | 18 | ||||||||||||||||||||||||||||||
| Number: | 11 | ||||||||||||||||||||||||||||||
| Article Number: | e1010726 | ||||||||||||||||||||||||||||||
| DOI: | 10.1371/journal.pcbi.1010726 | ||||||||||||||||||||||||||||||
| Status: | Peer Reviewed | ||||||||||||||||||||||||||||||
| Publication Status: | Published | ||||||||||||||||||||||||||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||||||||||||||||||||||||||
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