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Jerker statistics sweden covid 19 response

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Jerker Moström presentation on Statistic Sweden's Covid-19 response

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Jerker statistics sweden covid 19 response

  1. 1. Statistics Sweden’s Covid-19 response Jerker Moström (presenter) & Stefan Svanström Statistics Sweden Corona GIS webinar 7.5.2020
  2. 2. What do we do? • Corona realated statistics - Release of tailor-made statistics and re-packaging of existing data to better inform government agencies, research, media and the public • Support to the Public Health Agency of Sweden with micro data management and spatial analysis • Use of mobile network data to assess mobility/activity changes - part of an existing collaboration with Telia (experimental data) Statistics Sweden is a government agency responsible for official statistics and for other government statistics. In addition, we coordinate the system for the official statistics in Sweden.
  3. 3. Corona related statistics • Statistics on risk groups, such as older elderly population, multi-generation dwelling, overcrowding and deaths/mortality (weekly releases) • Statistics to inform on lock-down scenarios (number of helth care workers with school children, number of employees in critical sectors etc) • Statistics to inform on the Covid-19 impact on economy and labour market
  4. 4. Corona related statistics • A Covid-19 task force group to coordinate analyses • Special ”Corona entry” on the web (also urging respondents to submit data) • More than 20 articles published so far • A map tool launched to visualise local level population characteristics
  5. 5. Supporting the Public Health Agency of Sweden (PHA) • A request for help to enrich micro data on individuals infected by Covid-19 with background variables from registers • A request for help to conduct spatial analyses to better understand the geography of Corona (patterns of infection and the role of spatial, temporal and contexual conditions)
  6. 6. Micro data enrichment • A large set of variables derrived from a number of different registers • Based on personal-ids commonly used as identifiers across data repositories • Very sensitive data, only to be shared via safe environments within the PHA and Statistics Sweden Data themes • Household and dwelling charactersistics (type of household, number of persons, dwelling type) • Children (number, age) • Education (level) • Profession, occuoation and employment • Incomes (for household and for individuals) • Year of birth (for infected and for parents) • Country of origin (for infected and for parents
  7. 7. Micro data enrichment • What can background variables explain? • Correlation with specific dwelling conditions, socio economic status or background • Preliminary analyses indicate strong overrepresentation of people with other country of origin Source: Public Health Agency of Sweden
  8. 8. Geography of Corona • A step further – from data on individuals to spatial context around indviduals • Cluster analysis and hot-spots to follow how the infection evolves over time • Neighborhood characteristics (population density, social economic conditions) • Impact from mobility (Local Labour Markets)? • Prediction? Is it possible to foresee areas with high risk of new outbreaks
  9. 9. Källa: Bearbetningar © SCB, övrig geodata © SCB, Lantmäteriet Location Address locations to which data on population and workplaces can be geocoded
  10. 10. Källa: Bearbetningar © SCB, övrig geodata © SCB, Lantmäteriet Location Data on infected indivduals from PHA geocoded to address location
  11. 11. Källa: Bearbetningar © SCB, övrig geodata © SCB, Lantmäteriet Aggregation Aggregation on the basis of location - number of infected individuals on the same location/address
  12. 12. Neighborhood Källa: Bearbetningar © SCB, övrig geodata © SCB, Lantmäteriet Use of small areas to construct neighborhood units
  13. 13. Neighborhood Aggregation of geocoded data for all individuals within the neighborhood Källa: Bearbetningar © SCB, övrig geodata © SCB, Lantmäteriet Type of dwelling Dwelling area per capita Country of origin Population density Household types etc
  14. 14. Yellow dots = infected Can we identify connections between these dots? Källa: Bearbetningar © SCB, övrig geodata © SCB, Lantmäteriet Connections & networks
  15. 15. Red square = workplace Possible connection Källa: Bearbetningar © SCB, övrig geodata © SCB, Lantmäteriet Connections & networks
  16. 16. Mobile network data • On-going (mutual beneficial) collaboration with Telia • We don’t provide rapid response (Telia does this already, guess you will hear more in a few minutes…) • Long-term work to assess usefulness of network data to complement traditional data sources • The Corona pandemic provides an interesting use case from a methodological point-of-wiew
  17. 17. To wrap up… • We’re in the middle of the work right now • New questions and requirements arise along the way • Mostly trial and error – time will tell if it was useful… • We expect requests for Corona related data and analysis to remain high in the near future • Probably gradually shifting from responding to the pandemic itself to responding to the aftermath (economic and social conseqences)
  18. 18. Thank you very much! Jerker Moström (did the talk) Analyst/geospatial expert Statistics Sweden jerker.mostrom@scb.se www.scb.se Stefan Svanström (does most of the work) Analyst/geospatial expert Statistics Sweden stefan.svanstrom@scb.se

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