Map

We define an area to be a hotspot x if weekly projected cases per 100,000 population (“7-Tage-Inzidenz”) exceed x.
Data included up to 2022-03-30. Last model run performed at: Sat Apr 2 03:10:24 2022. For past weeks we compare the reported cases to the threshold. For future weeks, we give probabilities based on our model, which assumes a situation in which no change in interventions (e.g. local lockdowns) occur. To define weeks we use lab diagnosis dates, as reported in the Austrian epidemiological surveillance sytem (EMS). We consider an area to have increasing new infections if our model estimates that the reproduction number R is greater than 1 with probability of at least 90%. “Likely increasing” indicates a probability between 75% and 90%. Decreasing and likely decreasing are defined analogously, but consider R less than 1.

Model description

The results on this page have been computed using epidemia 0.6.0. Epidemia extends the Bayesian semi-mechanistic model proposed in Flaxman, S., Mishra, S., Gandy, A. et al. Nature 2020.

The model is based on a self-renewal equation which uses time-varying reproduction number \(R_{t}\) to calculate the infections. However, due to a lot of uncertainty around reported cases in early part of epidemics, we use reported deaths to back-calculate the infections as a latent variable. Then the model utilizes these latent infections together with probabilistic lags related to SARS-CoV-2 to calibrate against the reported deaths and the reported cases since the beginning of June 2020. A detailed mathematical description of the original model can be found here.

\(R_t\) model

\(R_{t}\) for each district is parameterized as a linear function of:

• \(R_t\) of the whole country, which we also fit, which is fed into the model from mid Feburary up to 45 days before the observation end (2022-02-13).
• A random effect specific to each district or area for each week over the course of the epidemic.
  • The weekly random effects are encoded as a random walk, where at each successive step the random effect has an equal chance of moving upward or downward.
• Additional change points are included in the model, which allow a larger change than the random walk on certain dates.
  • The current change points are:
    • First lockdown on the 16th of March 2020.
    • Second lockdown, first step, on the 3rd of November 2020.
    • Second lockdown, second step, on the 17th of November 2020.
    • Lockdown in Wien, Niederösterreich and Burgenland, on the 1st of April 2021.
    • New rules, including 2G and 3G in the workplace, on the 8th of November 2021.
    • Lockdown for unvaccinated individuals, on the 15th of November 2021.
  • The priors for these change points model an equal chance for an increase or decrease in \(R_t\) on the day of each lockdown for the country-wide model.
  • The local models use the country-wide estimate of the lockdown effects on \(R_t\) as their prior.

Time-varying ascertainment and infection fatality rate

We model changes in the ascertainment of cases (IAR) based upon the UK covid19local model.

Changes to the infection fatality ratio (IFR) are also modeled based upon the UK covid19local model, but scaled to a baseline IFR of 1.04% and stopped at an IFR of 0.7%.

From the 1st of July 2021 an additional variation in the IFR is allowed to account for changes in the age structure of infections due to the heterogenous vaccination coverage and to account for the vaccine effectiveness against severe disease and mortality in breakthrough infections.

For details on the baseline IFR estimate please refer to Brazeau, N. et al. Report 34: COVID-19 infection fatality ratio: estimates from seroprevalence.

A detailed description of the UK covid19local model can be found in Mishra, S. et al. A COVID-19 Model for Local Authorities of the United Kingdom.

Explanation of terms

• Dates for cases are lab diagnosis dates, as reported in the Austrian epidemiological surveillance sytem (EMS).
• An area is defined as hotspot X if weekly cases per 100,000 population exceed X.
• Change in new infections gives the probability (chance) of new infections increasing or decreasing in the district.
• Columns with column name as Weekly Cases per 100k [Date], in the Table tab, show the reported number of cases per 100,000 population for the week specified by the Date.
• Columns with column name as P(hotspot) [Date] shows the estimated probability of an area being a hotspot for the week specified by the Date.
• Column P(R>1) [Date] gives the probability of time varying reproduction number being greater than 1 for the week specified by the Date.

Limitations

• Predictions on this page assume no change in current interventions (lockdowns, school closures, and others) in the local area beyond those already taken about a week before the end of observations.
• Levels of hotspot carry no specific meaning and are not based on any official cutoffs.
• An increase in cases in an area can be due to an increase in testing. The model currently does not account for this.
• Each area (district) is treated independently apart from the overall R_t estimate for Austria (up to 45 days before the last included observation). Thus the epidemic in a region is neither affected by nor affects any other region. It also does not include importations from other countries.
• The population within an area is considered to be homogeneous, i.e., all individuals are considered equally likely to be affected by the disease progression.

Data sources

• Daily cases and deaths for Austria and all districts is taken from open government data which contain exports of the Austrian epidemiological surveillance system (EMS). Source: BMSGPK, Österreichisches COVID-19 Open Data Informationsportal (https://www.data.gv.at/covid-19)
• Anonymized line-list data from the Austrian epidemological surveillance system (EMS) is used for estimating the time distribution from onset of symptoms to lab diagnosis and from onset of symptoms to death. Source: Datenplatform COVID-19
• The boundaries of the districts are taken from open government data. Source: Datenquelle: Statistik Austria - data.statistik.gv.at

Updates

Data last updated on Sat Apr 2 03:10:24 2022.

Observations up to 2022-03-30 are included.

Generation time distribution

We use the serial interval of COVID-19 assumed in Flaxman, S., Mishra, S., Gandy, A. et al. Nature 2020 as the generation time distribution.

Infection to cases

We assume an infections to cases time distribution estimated by combining the incubation period published in Zhang, J. et al. Lancet 2020 with the time differences between symptom onset and first positive lab test for COVID-19 cases from anonymized line-list data from the Austrian epidemiological surveillance system (EMS). Time differences between the 1st of June and 22nd of October 2020 were included in the estimation.

Infection to deaths

We assume an infections to cases time distribution estimated by combining the incubation period published in Zhang, J. et al. Lancet 2020 with the time differences between symptom onset and first positive lab test for COVID-19 cases from anonymized line-list data from the Austrian epidemiological surveillance system (EMS). Time differences between the 1st of June and 22nd of October 2020 were included in this estimation. And finally the time differences between reported lab diagnosis date and death were added, which were also obtained from anonymized line-list data from the Austrian epidemiological surveillance system (EMS), cases with a lab diagnosis date before the 3rd of September 2020 were included.

Imprint

Publisher

Authors: Fabian Valka¹*, Swapnil Mishra²*, Jamie Scott³, Seth Flaxman³, Samir Bhatt², Axel Gandy³

* Contributed equally
¹vektorraum
²MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, School of Public Health, Imperial College London
³Department of Mathematics, Imperial College London

This research was partly funded by the The Imperial College COVID19 Research Fund.

Licence

The results, maps and figures shown on this website are licenced under a Attribution 4.0 International (CC BY 4.0) Licence.

Data Sources

• BMSGPK, Österreichisches COVID-19 Open Data Informationsportal
• Datenplatform COVID-19
• Statistik Austria - data.statistik.gv.at

For a detailed description of the data used from each data source please refer to the Details page.

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Contact

Contact

For any enquiries please contact:

Fabian Valka
contact@covid19model.at

External Relationships and Communications Manager
Sabine L. van Elsland
+44 (0)20 7594 3896

s.van-elsland@imperial.ac.uk