Epidemiological cluster identification using multiple data sources: an approach using logistic regression.

ABSTRACT: In the management of infectious disease outbreaks, grouping cases into clusters and understanding their underlying epidemiology are fundamental tasks. In genomic epidemiology, clusters are typically identified either using pathogen sequences alone or with sequences in combination with epidemiological data such as location and time of collection. However, it may not be feasible to culture and sequence all pathogen isolates, so sequence data may not be available for all cases. This presents challenges for identifying clusters and understanding epidemiology, because these cases may be important for transmission. Demographic, clinical and location data are likely to be available for unsequenced cases, and comprise partial information about their clustering. Here, we use statistical modelling t

SUBMITTER: Susvitasari K 

PROVIDER: S-EPMC10132077 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature