This analysis is based on the data of newly emerged (incident) COVID19 cases entered into the Austrian Epidemiological Reporting System (EMS). Relevant case data were collected by the Infectious Disease Epidemiology & Surveillance Department of AGES from the competent authorities and completed in the EMS. The evaluation was performed with data as of 06.06.2023, 07:00. At that time, 6.081.510 COVID 19 cases had been reported.
Based on the temporal distribution of incident cases, we used statistical models (Poisson regression) to estimate the daily rate of increase in the incidence of incident cases and the effective reproduction number (i.e., average number of subsequent cases generated by a case). A detailed description of the methods is given in Richter, Schmid, and Stadlober (2020). The serial interval used is based on a gamma distribution with mean 3.37 and standard deviation 1.83 and as such enters the calculation of the effective reproduction number. These serial interval parameters are based on Austrian source case-follow-up case pairs (Richter et al. (2021)).
Epidemiological parameters: Data
Below, the time series of the most recent update of the epidemiological parameters are available as csv (rate of increase and effective reproduction number). The time of the update and the time of the data status are stored in the metadata. The data can be used in compliance with the Creative Commons Attribution 4.0 International license(https://creativecommons.org/licenses/by/4.0/deed.de). In any case, "AGES, TU Graz" must be cited as the source.
In some provinces, the number of incidence cases is very low, which is why fluctuations in the effective reproduction number should be interpreted with great caution. As of now, we require a minimum of 12 cases for estimating the effective reproduction number, so estimators for some days may be missing compared to the previous publication. Data sets included:
meta_data.csv: contains among others last update (creation of the files), data status (status of the survey data for the evaluation) and information about citation and license.
growth.csv: date, estimator of the growth rate (growth) and 95% confidence interval (growth_lwr and growth_upr)
R_eff.csv: date, estimator of effective reproduction number (R_eff) and 95% confidence interval (R_eff_lwr and R_eff_upr)
R_eff_state.csv: State, date, estimator of the effective reproduction number (R_eff) and 95% confidence interval (R_eff_lwr and R_eff_upr)
Lukas Richter (1,2), Alena Chalupka (1), Daniela Schmid (1), Ali Chakeri (1), Sabine Maritschnik (1), Sabine Pfeiffer (1), Ernst Stadlober (2) 1 Department of Infection Epidemiology & Surveillance, AGES 2 Institute of Statistics, Graz University of Technology.