# Map

## Column

### 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 2021-01-21. Last model run performed at: Sat Jan 23 17:11:17 2021. 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.

Austria

# Table

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 2021-01-21. Last model run performed at: Sat Jan 23 17:11:17 2021. 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.

## Row

### Hotspots: Probability of more than X weekly reported cases per 100k

hotspots.csv

#### Description

Model estimated probability of exceeding a given threshold of weekly reported cases per 100k population.

#### Fields

date_start
date (YYYY-mm-dd)

Start date of the period for which the probability estimate applies.

date_end
date (YYYY-mm-dd)

End date of the period for which the probability estimate applies.

area
string

Area name, can be either a district name, combined districts (districts might be combined together for the model runs, see below) or a region wide estimate. The region name “Austria” is used for the country-wide estimate.

hotspot_50
double (probability, from 0 to 1)

Probability of exceeding 50 weekly cases per 100k population.

hotspot_100
double (probability, from 0 to 1)

Probability of exceeding 100 weekly cases per 100k population.

hotspot_200
double (probability, from 0 to 1)

Probability of exceeding 200 weekly cases per 100k population.

hotspot_500
double (probability, from 0 to 1)

Probability of exceeding 500 weekly cases per 100k population.

last_update
date (YYYY-mm-dd)

Date of last observation (cases and deaths) included in the model run.

### Change in new infections: Probability of a reproduction number greater than 1

reproduction_number_gt_1.csv

#### Description

Probability of the reproduction number being greater than one for the last week up to the latest observation included in the model.

#### Fields

date_start
date (YYYY-mm-dd)

Start date of the period for which the probability estimate applies.

date_end
date (YYYY-mm-dd)

End date of the period for which the probability estimate applies.

area
string

Area name, can be either a district name, combined districts (districts might be combined together for the model runs, see below) or a region wide estimate. The region name “Austria” is used for the country-wide estimate.

pr_gt_1
double (probability, from 0 to 1)

Probability of the reproduction number being greater than 1.

last_update
date (YYYY-mm-dd)

Date of last observation (cases and deaths) included in the model run.

### Reproduction number

reproduction_number.csv

#### Description

Estimates of the time-varying reproduction number for each area.

#### Fields

date
date (YYYY-mm-dd)

Date of the estimate.

area
string

Area name, can be either a district name, combined districts (districts might be combined together for the model runs, see below) or a region wide estimate. The region name “Austria” is used for the country-wide estimate.

prediction
bit (0 or 1)

Prediction flag, which is 0 if the date field is equal to or smaller than the last date of data included in the model, 1 otherwise.

median
double

Median of the reproduction number estimate for a given date.

ci30_low
double

Lower end of the 30% credible interval of the reproduction number estimate.

ci30_high
double

Upper end of the 30% credible interval of the reproduction number estimate.

ci60_low
double

Lower end of the 60% credible interval of the reproduction number estimate.

ci60_high
double

Upper end of the 60% credible interval of the reproduction number estimate.

ci90_low
double

Lower end of the 90% credible interval of the reproduction number estimate.

ci90_high
double

Upper end of the 90% credible interval of the reproduction number estimate.

last_update
date (YYYY-mm-dd)

Date of last observation (cases and deaths) included in the model run.

## Row

### Cases

cases.csv

#### Description

Model estimated weekly cases per 100k population.

#### Fields

date_start
date (YYYY-mm-dd)

Start date of the period for which the estimate applies.

date_end
date (YYYY-mm-dd)

End date of the period for which the estimate applies.

area
string

Area name, can be either a district name, combined districts (districts might be combined together for the model runs, see below) or a region wide estimate. The region name “Austria” is used for the country-wide estimate.

prediction
bit (0 or 1)

Prediction flag, which is 0 if the date field is equal to or smaller than the last date of data included in the model, 1 otherwise.

median
double

Median of the estimated weekly cases per 100k.

ci30_low
double

Lower end of the 30% credible interval of the estimated weekly cases per 100k.

ci30_high
double

Upper end of the 30% credible interval of the estimated weekly cases per 100k.

ci60_low
double

Lower end of the 60% credible interval of the estimated weekly cases per 100k.

ci60_high
double

Upper end of the 60% credible interval of the estimated weekly cases per 100k.

ci90_low
double

Lower end of the 90% credible interval of the estimated weekly cases per 100k.

ci90_high
double

Upper end of the 90% credible interval of the estimated weekly cases per 100k.

last_update
date (YYYY-mm-dd)

Date of last observation (cases and deaths) included in the model run.

### Daily infections

infections.csv

#### Description

Model estimates of daily new infections per 100k population.

#### Fields

date
date (YYYY-mm-dd)

Date of the estimate.

area
string

Area name, can be either a district name, combined districts (districts might be combined together for the model runs, see below) or a region wide estimate. The region name “Austria” is used for the country-wide estimate.

prediction
bit (0 or 1)

Prediction flag, which is 0 if the date field is equal to or smaller than the last date of data included in the model, 1 otherwise.

median
double

Median of the estimated daily new infections per 100k.

ci30_low
double

Lower end of the 30% credible interval of daily new infections per 100k.

ci30_high
double

Upper end of the 30% credible interval of daily new infections per 100k.

ci60_low
double

Lower end of the 60% credible interval of daily new infections per 100k.

ci60_high
double

Upper end of the 60% credible interval of daily new infections per 100k.

ci90_low
double

Lower end of the 90% credible interval of daily new infections per 100k.

ci90_high
double

Upper end of the 90% credible interval of daily new infections per 100k.

last_update
date (YYYY-mm-dd)

Date of last observation (cases and deaths) included in the model run.

### Population

population.csv

#### Description

Population used for each area in the model.

#### Fields

area
string

Area name, can be either a district name, combined districts (districts might be combined together for the model runs, see below) or a region wide estimate. The region name “Austria” is used for the country-wide estimate.

pop
integer

Population estimate used for the given area.

## Row

### Licence and citing

The model output provided above is licenced under the Attribution 4.0 International (CC BY 4.0) Licence.

If you use the data above please cite us appropriately and we would also ask you to let us know how you used the data via email to: contact@covid19model.at

### API stability and automated processing

We provide a semantic versioning of the file formats as a stable API for any consumers of the files above. The current latest version is v1 and the stable API path is: downloads/v1/

If you are processing the data automatically in a script please either perform a HEAD request before a new download to check if the Modified header has changed or download the metadata first and check either the output version or last observation date for changes.

# Details

## Row

### 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 (2020-12-07).
• 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.
• 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%.

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.

## Row

### 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 Rt 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

Data last updated on Sat Jan 23 17:11:17 2021.

Observations up to 2021-01-21 are included.

## Row

### 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.

## 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

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

## Column

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