Generalising an outbreak cluster detection method for two groups: An application to rabies

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..., , , # 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

**Descrip...,