eu-ecdc Goto Github PK

backendcovidapp

The backend to the COVID-19 RShiny app (github.com/EU-ECDC/RShinyCovidApp)

covid-19_weekly-data

ecdc-mathematical-models

ecdccolors

Development of an R package for using colour palettes following March 2018 ECDC guidelines for presentation of surveillance data

ecdcsupportsnippets

Development of various support R code snippets and packages for use for analysis and modelling purposes.

epireport

ECDC in collaboration with Epiconcept developed an R package to create epidemiological reports

episignaldetection

ECDC in collaboration with Epiconcept developed an R package for monitoring infectious disease surveillance data

epitweetr

ECDC Early warning tool using Twitter data

herpeszostermodel

Development of an R package for Herpes Zoster modeling

ipdforecasting

Forecasts of invasive pneumococcal disease (IPD). Dynamic regression model based on demography, vaccination coverage, childcare patterns and incidence of IPD and influenza.

jiacra2019-2021

Fourth joint inter-agency report on integrated analysis of antimicrobial agent consumption and occurrence of antimicrobial resistance in bacteria from humans and food-producing animals in the EU/EEA

mixtureofexperts

A flexible mixture of experts framework, calibrated with expectation maximisation. Options for mixtures of Gaussians, regression models etc.

monthlymeaslesrubella

Development of an R package for production of the monthly measles and rubella surveillance report

pooledprevalence

A set of tools to help design a laboratory based prevalence study harnessing the pooling of laboratory samples to increase resource efficiency.

respiratory_viruses_weekly_data

rshinycovidapp

The RShiny app is designed to make three features accessible to the user: 1) Forecasts: Current and previous four-week ahead forecasts of the number of cases in each region (and age group when available), 2) Predictors: The risks of secondary transmission and importations estimated by the model, and 3) Scenarios: The impact of changes in transmission, be it due to more infectious variants, changes in behaviour, or NPIs. These changes in transmission represent the potential impact of control measures on transmission, and should be interpreted with caution in a constantly changing epidemiological situation (impacted for example by behaviour, adherence..). Similarly, this model only considers the epidemiological impact of NPIs, the social or economic costs of different control measures is not considered in this analysis.