DOI: 10.5061/dryad.931zcrjvq
title: Generalising an outbreak cluster detection method for two groups: An application to rabies

description: Identifying linked cases of an infectious disease can improve our
 understanding of its epidemiology by distinguishing sustained local
 transmission from frequent introductions with little onward transmission.
 This evidence can, in turn, inform decisions on the most appropriate
 interventions. Knowledge of key epidemiological distributions and
 reporting probabilities is key in identifying linked cases. However, with
 multi-host pathogens quantitative differences between hosts may need
 consideration, which are not incorporated in existing methods. In this
 study, an existing graph-based approach to detecting outbreak clusters was
 extended to allow for group-specific reporting probabilities and
 epidemiological distributions and to assess the level and importance of
 assortative mixing. This method was applied to data on probable animal
 rabies cases in south-east Tanzania where wildlife comprised over 40% of
 detected animal rabies cases. Group-specific differences (in reporting
 probabilities and epidemiological distributions) and the level of
 assortative mixing had a marked impact on the size and composition of
 identified clusters. The scenario most compatible with the data involved
 higher reporting probabilities for cases in domestic animals compared to
 wildlife, no difference between the mean transmission distance between
 domestic animals versus wildlife and substantial assortative mixing with
 frequent inter-species transmission. The method described here could be
 applied to other multi-host systems or to single-host systems with
 multiple groups (such as age-classes) in which heterogeneities in
 reporting probabilities, distributional parameters and/or levels of mixing
 exist between groups. This would allow more accurate characterisation of
 transmission dynamics which would facilitate implementation of more
 effective interventions.
description: # Generalising an outbreak cluster detection method for two groups: An
 application to rabies
 [https://doi.org/10.5061/dryad.931zcrjvq](https://doi.org/10.5061/dryad.931zcrjvq) ## Description of the data and file structure This repository provides the data used within the paper entitled 'Generalising an outbreak cluster detection method for multiple groups: An application to rabies'. The data are used within the section entitled 'Application to rabies data'. The data reflect probable animal rabies cases that occurred between January 2011 and July 2019 within the 13 districts of Lindi and Mtwara regions of south-east Tanzania.  Each file has the same number of rows and each row represents the same rabies case in all files.  During data collection, there was some uncertainty around the dates and locations of the animal rabies cases. This uncertainty is recorded in the files dates_uncertainty and distance_uncertainty.csv.  ### Files and variables #### File: case_dists.csv **Description:** location data for each of the rabies cases ##### Variables * UTM.Easting * UTM.Northing #### File: case_sp_vect.csv **Description:** data on the species group for each of the rabies cases #### File: case_time_diffs.csv **Description:** Number of days since the start of the study period on which the rabies case occurred #### File: dates_uncertainty.csv **Description:** Uncertainty (in days) around the dates of the rabies cases #### File: distance_uncertainty.csv **Description:** Uncertainty around the location data for the rabies cases (in m) ##### Variables * lower_uncert * upper_uncert ## Code/software [https://github.com/sarahhayes/vimesMulti_for_paper](https://github.com/sarahhayes/vimesMulti_for_paper)  [https://doi.org/10.5281/zenodo.17478478](https://doi.org/10.5281/zenodo.17478478) ## Access information Other publicly accessible locations of the data: * [https://github.com/sarahhayes/vimesMulti_for_paper](https://github.com/sarahhayes/vimesMulti_for_paper)