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

Author

Listed:
  • Massimo Cavallaro
  • Juliana Coelho
  • Derren Ready
  • Valerie Decraene
  • Theresa Lamagni
  • Noel D McCarthy
  • Dan Todkill
  • Matt J Keeling

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.Author summary: Rapidly detecting disease outbreaks and identifying epidemiologically linked cases is crucial for the organization and implementation of effective outbreak-control interventions. To help identify the outbreak cases from a background of sporadic events—and thus complement the investigations conducted by public health teams—we developed a numerical approach, which we referred to as random neighbourhood covering (RaNCover). By summarising the statistical properties of the neighbourhoods of all events, RaNCover achieves excellent predictive performances. We applied our approach to invasive group A Streptococcus (GAS) infection events. While asymptomatic GAS carriage is prevalent, when these bacteria invade parts of the body such as the blood, the muscles, or the lungs, they can cause fatal infections. Invasive GAS outbreaks can take place across wide geographic areas and are difficult to detect using standard public health investigations, which can benefit from our novel computational approach.

Suggested Citation

  • Massimo Cavallaro & Juliana Coelho & Derren Ready & Valerie Decraene & Theresa Lamagni & Noel D McCarthy & Dan Todkill & Matt J Keeling, 2022. "Cluster detection with random neighbourhood covering: Application to invasive Group A Streptococcal disease," PLOS Computational Biology, Public Library of Science, vol. 18(11), pages 1-24, November.
    • Handle: RePEc:plo:pcbi00:1010726
    DOI: 10.1371/journal.pcbi.1010726
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    References listed on IDEAS

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