Cluster detection with random neighbourhood covering: Application to invasive Group A Streptococcal disease.
PLoS Comput Biol
; 18(11): e1010726, 2022 11.
Article
en En
| MEDLINE
| ID: mdl-36449515
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.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Infecciones Estreptocócicas
Tipo de estudio:
Clinical_trials
/
Diagnostic_studies
Límite:
Humans
País/Región como asunto:
Europa
Idioma:
En
Revista:
PLoS Comput Biol
Asunto de la revista:
BIOLOGIA
/
INFORMATICA MEDICA
Año:
2022
Tipo del documento:
Article
País de afiliación:
Reino Unido