Cluster detection with random neighbourhood covering: Application to invasive Group A Streptococcal disease

Massimo Cavallaro*, Juliana Coelho, Derren Ready, Valerie Decraene, Theresa Lamagni, Noel D. McCarthy, Dan Todkill, Matt J. Keeling

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Article numbere1010726
JournalPLoS Computational Biology
Volume18
Issue number11
DOIs
Publication statusPublished - Nov 2022

Bibliographical note

Publisher Copyright:
© 2022 Cavallaro et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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