The Fight-Covid-19 project uses geographic decision-making systems in the fight against the coronavirus pandemic by visualising important key figures on maps. These are used by Covid-19 planning staff, for example to control bed utilisation in hospitals based on incidence rates. We are developing additional intuitive Excel tools for visualising the company situation on maps, which can be used by enterprises free of charge.
The map shows the 7-day COVID-19 incidence rate and compares it to the utilisation of ventilator beds (high care, ECMO, cumulative).
Interactive map: Zoom in for a better overview or a more detailed view.
By clicking on the selection list, you can filter by the type of available ventilation beds (high care, ECMO, cumulative). You can obtain detailed information by clicking on an object (hospital, district, etc.). If objects overlap, you can navigate through them using the triangle symbols ▶ in the top right-hand corner.
Analysis: Dark area (= many COVID-19 cases) and red dots (= high intensive care bed utilisation) ➔ Need for action? (See legend for details)
Click here for full screen view or on the map, click on the full screen icon.
Sources and references
Sources: Hochschule Fulda, ESRI (www.esri.de), Robert Koch-Institut (www.rki.de), dl-de/by-2-0; Bundesamt für Kartographie und Geodäsie, © GeoBasis-DE / BKG (www.bkg.bund.de), Divi Intensivregister (www.intensivregister.de/#/intensivregister), Statistisches Bundesamt (www.destatis.de)
*Type of ventilation stations:
High care: Ventilation station where pressure is built up in the lungs via a tube.
ECMO: Most complex treatment: A machine takes over some or all of the patient's respiratory function.
Cumulative: Red = At least one type (high care, ECMO) has red status; yellow = at least one type has yellow status; green = all types have green status.
In the following video interview, Prof Dr Jozo Acksteiner (Fulda University of Applied Sciences) and Dr Michael von Wagner (University Hospital Frankfurt) explain under the facilitation of Erik Lorenz, explain how, on the basis of the Fight-Covid-19 project, a cooperation between the University Hospital Frankfurt and the Hessian Ministry for Social Affairs and Integration (HMSI). The end of project served to support the Hessian Corona planning staff in the planning of ventilation units. In doing so, they share experiences and perspectives for geographic data analyses in the health system and beyond.
Immunisation ranking of federal states and county
The map shows how successful county and federal states have been in terms of Covid-19 vaccinations compared to each other.
By clicking on the layer symbol, you can choose to display a ranking of immunisation status at county and federal state level.
The rankings are determined on the basis of the vaccination rate (proportion of fully immunised people in the total population) of the respective federal state/county. A distinction is made between four ranking categories (quartiles): top 25%, top 50%, top 75% and bottom 25%.
The darker green the colour of the county, the higher their vaccination rate compared to the other county. The rankings of the federal states are shown by colouring their borders. The colouring runs from red (federal states with the lowest vaccination rate) to green (federal states with the highest vaccination rate).
By clicking on a county or a federal state border, further information can be displayed, such as the number of people vaccinated at least once, basic immunisation rates and population.
Click here for full screen view or on the full screen symbol in the map
Sources: Fulda University of Applied Sciences, ESRI(www.esri.de), Robert Koch Institute(www.rki.de), dl-de/by-2-0; Federal Agency for Cartography and Geodesy, © GeoBasis-DE / BKG(www.bkg.bund.de), Divi Intensive Register(www.intensivregister.de/#/intensivregister), Federal Statistical Office(www.destatis.de), Baden-Württemberg Ministry of Social Affairs(www.sozialministerium.baden-wuerttemberg.de)
Makebusiness crisis decisions quickly and efficiently through geographical analyses.
Use the freely available Excel templates and set up your company's own early warning system with your own data.
Please note: The Excel add-in GeoLyx is required to use the Excel templates.
To request GeoLyx, please use the contact form.
Monitoring Covid-19 cases at your locations:
Map your company locations by type and size and compare them with the confirmed COVID-19 cases. This allows you to anticipate any staff shortages at your locations more quickly.
You can generate this view as often as you like and thus dynamically display the infection history.
Resource overview:
Recognise at a glance at which locations resource bottlenecks occur or may occur (e.g. masks, gloves, etc.).
Use the visualisations to plan suitable countermeasures.
downloadExample Hesse
Legend
Target group
Pollen allergy sufferers
Local doctors
Situation
Covid-19 and pollen allergy have similar symptoms and are difficult to distinguish at first
Allergy sufferers are more susceptible to Covid-19 infection
Goal
Enable differentiation between Covid-19 symptoms and allergic reactions
Special focus on the Covid-19 situation for allergy sufferers
Presentation of the health risk for allergy sufferers
Target group
> Crisis teams and health authorities
Situation
> Distribution of intensive care capacities still as before the pandemic, no special consideration of Covid-19 risk groups
Goal
> Improve the distribution of intensive care capacities, taking into account the size of a district's risk groups
> To make the work of decision-makers easier for faster decisions
Legend
Target group
> Local enterprises with a surplus or shortage of employees
> Job centres
Situation
> Many enterprises have to lay off employees or put them on short-time working due to Covid-19
> Other enterprises, on the other hand, have increased employee requirements that cannot be met
Objective
> Equalisation of employee requirements and overcapacity
> Quickly establish partnerships between enterprises to be able to react flexibly to the crisis
> Increase employee satisfaction by avoiding job insecurity, excessive demands and insufficient demands