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
Author Information
Massimo Cavallaro: The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, University of Warwick, Coventry, United Kingdom. ORCID
Juliana Coelho: UK Health Security Agency, United Kingdom.
Derren Ready: UK Health Security Agency, United Kingdom.
Valerie Decraene: UK Health Security Agency, United Kingdom.
Theresa Lamagni: UK Health Security Agency, United Kingdom. ORCID
Noel D McCarthy: The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, University of Warwick, Coventry, United Kingdom. ORCID
Dan Todkill: UK Health Security Agency, United Kingdom.
Matt J Keeling: The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, University of Warwick, Coventry, United Kingdom. ORCID
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.
