Federal Institute for Population Research

Control Prognosis of Intensive Care COVID-19 Capacities (SPoCK)

Content and Objectives

The BiB is a member of the project consortium Steuerungs-Prognose von intensivmedizinischen COVID-19-Kapazitäten (Control Prognosis of Intensive Care COVID-19 Capacities) (SPoCK) funded by the Federal Ministry of Health and led by the Robert Koch Institute and the Deutsche Interdisziplinären Vereinigung für Intensiv- und Notfallmedizin (DIVI). SPoCK will initially run for 2 years and aims to provide daily updated predictions of COVID-19 patient numbers in intensive care units. Together with the German Aerospace Center (DLR), the BiB is analysing the vulnerability of populations in hospital catchment areas to enable a more accurate estimation of COVID-19 capacities at regional level. This is because in addition to older persons, smokers and people with certain pre-existing conditions are also part of the risk group for a severe course and thus intensive medical treatment of COVID-19. The forecast model takes into account the regional differences in the proportion of these vulnerable population groups in the total population.

The catchment areas of intensive care units cannot be precisely recorded on the basis of administrative units such as municipalities and rural districts. Therefore, SPoCK calculates the catchment areas using an accessibility model independent of administrative units. By intersecting the accessibilities with data on the population structure, the risk groups in the catchment areas of individual hospitals can be determined and used to predict the COVID-19 patients requiring intensive care treatment at the local level. In addition to the elderly population, other risk groups are also precisely located in order to make the risk of severe courses spatially differentiated and quantifiable depending on current new infections. The forecast model also pays special attention to the effects of commuting behaviour on the incidence of infection.

Data and Methods

Spatially differentiated analyses and forecasts of capacity bottlenecks are made at the level of dynamic catchment areas of individual hospital locations, which are calculated via a temporal accessibility model based on publicly usable data (e.g., Open Street Map). Population density, age distribution and information, smoking behaviour and pre-existing conditions in the respective catchment areas of the hospitals are used to forecast the need for intensive care beds and are determined on the basis of 100-metre grid cells from the 2011 Census (after correction for the 2018 population level). The Federal Statistical Office provides current population data at regional level as well as data on smoking behaviour from the microcensus. These are supplemented by data from the health insurance funds on the prevalence of pre-existing conditions at regional level. In addition, the potential of movement data for spatially resolved modelling will be investigated. Direct primary data collection on typical care patterns (e.g., referrals, specialities, cooperation with neighbouring health care providers) for each hospital location complements the specific local forecast modelling.

Duration

08/2020-07/2022

Partners

  • German Aerospace Center (DLR), Köln, Germany
  • Robert Koch Institute (RKI), Berlin, Germany
  • Deutsche Interdisziplinäre Vereinigung für Intensiv- und Notfallmedizin (DIVI), Berlin, Germany
  • Rheinisch-Westfälische Technische Hochschule (RWTH), Aachen, Germany
  • Albert-Ludwigs-Universität Freiburg, Germany
  • Universität Würzburg, Germany
  • Technische Universität Berlin, Germany
  • Berlin Institute of Health (BIH), Germany

Funding

Funding is provided by third-party funds from the Federal Ministry of Health (BMG) and the Robert Koch Institute (RKI).

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