epitweetr - Early Detection of Public Health Threats from 'Twitter' Data

It allows you to automatically monitor trends of tweets by time, place and topic aiming at detecting public health threats early through the detection of signals (e.g. an unusual increase in the number of tweets). It was designed to focus on infectious diseases, and it can be extended to all hazards or other fields of study by modifying the topics and keywords. More information is available in the 'epitweetr' peer-review publication (doi:10.2807/1560-7917.ES.2022.27.39.2200177).

Last updated 1 years ago

early-warning-systemsepidemic-surveillancelucenemachine-learningsignal-detectionsparktwitter

6.28 score 55 stars 86 scripts 291 downloads

EpiSignalDetection - Signal Detection Analysis

Exploring time series for signal detection. It is specifically designed to detect possible outbreaks using infectious disease surveillance data at the European Union / European Economic Area or country level. Automatic detection tools used are presented in the paper "Monitoring count time series in R: aberration detection in public health surveillance", by Salmon et al. (2016) <doi:10.18637/jss.v070.i10>. The package includes: - Signal Detection tool, an interactive 'shiny' application in which the user can import external data and perform basic signal detection analyses; - An automated report in HTML format, presenting the results of the time series analysis in tables and graphs. This report can also be stratified by population characteristics (see 'Population' variable). This project was funded by the European Centre for Disease Prevention and Control.

Last updated 6 years ago

5.43 score 16 stars 17 scripts 212 downloads