JUNE:

Modelling the spread of Covid-19 in the UK

June Dalziel Almeida

Creating a UK digital twin

  • Population density
  • Tourism
  • Commuting
  • etc.

Why?

The UK is not homogeneous

Some things are homogeneous, though...

Pubs in the UK

How?

We use census data (mainly from NOMIS) to populate the UK with a realistic population.

Output areas as our statistical unit

Source : ukdataservice.ac.uk

  • Output area's population ~ 300 residents.
  • England & Wales contain 180k OAs

Available information at the OA level

  • Single year age for every resident
  • Sex in ~5 years bins
  • Socio-economic index of the area
  • Ethnicity
  • Household composition
  • Number of students
  • Industry sector of workers
  • Communal establishments
  • Carehomes
  • etc...

Households

We use data (NOMIS) on age, sex, and living arrangements to create households.

Age/sex distribution.

  • Number of households that contain at least one person over 65 years old.
  • Number of couples
  • ....

Households

Care Homes

  • ~380k people live in care homes in EW in ~15k carehomes.
  • Care homes are filled by first born first served basis.
  • Care homes have three subgroups: residents, workers, and visitors

Schools

Data on every school in England & Wales

  • Age range
  • Location (coordinates)
  • Number of pupils

Assign each kid to the closest school that fits their age range.

  • Kids are subdivided into year groups
  • We add teachers assuming constant ratio.

Hospitals

Data on every hospital in England & Wales

  • Location (coordinates)
  • Number of beds
  • Similar to schools, organize hospitals in a NN tree.
  • Infected people are sent to a certain Hospital based on distance and active policies.
  • Every hospital contains 3 subgroups: medics, patients, and ICU patients.

Hospitals

We also (plan to) model

  • Regular hospital admission rates (including additional subgroups)
  • Number of ICU beds over time

Workplace

  • For every OA we know at which industry sector everyone works.
  • We also know where all business/industries are.
  • We use flow data to match the two.

Flow data

Durham, works inĀ  sector X

Newcastle, industry sector X

Sector info also used for medics and teachers.

Commute

  • From census data, we know the method of transport people use.
  • Two kinds: Inner city commute, outer city commute

Hub

Hub

  • Train carriage's passengers randomised every day.
  • Intensity factor captures busy times

National travel (by rail)

  • Data on number of travelers at the 15 largest UK cities (internal commutes excluded).
  • We match the number of people at each station to infer travel routes in a probabilistic way.

London

Manchester

Liverpool

Birmingham

p \propto n(\mathrm{Manchester}) \times \mathrm{Connection \; quality}

Leisure

  • Our virtual residents also enjoy different leisure activities.
  • So far: pubs, grocery shopping, and cinemas
  • We use exact locations of all Pubs and Cinemas, and a distribution of groceries at the super area level.
  • Each "Social Venue" attracts different people depending on their characteristics.
  • Probability of dragging the household depending on activity.

JUNE UK digital twin

By arnauqb

JUNE UK digital twin

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