Modelling the spread of Covid-19 in the UK

June Dalziel Almeida

JUNE

Christoph Becker, Richard Bower, Joseph Bullock*, Tristan Caulfield, Carolina Cuesta-Lazaro*,  Aoife Curran, Edward Elliott, Kevin Fong, Richard Hayes, Miguel Icaza-Lizaola, Frank Krauss, James Nightingale, Arnau Quera-Bofarull*, Aidan Sedgewick, Henry Truong, Ian Vernon*, and Julian Willams

 

* speakers

  1. Creating a digital twin of the UK (Arnau QB)
  2. Dynamics of the world (Joseph B)
  3. Modelling Covid-19 (Carol CL)
  4. Policies (Joseph B)
  5. Model fitting and results (Ian V)
  6. Visualizer demo (Carol CL)

Outline

Creating a digital twin of the UK

Outline

Geography

Demography

Population

Venues

  • Households
  • Companies
  • Schools
  • etc.

June's Geography

local authority districts

super area (MSOA) 

area (OA)

North East => 26,000 output areas

June's Demography

male

female

male

female

Demography

What defines a person in June?

  • age  (27)
  • sex (f)
  • ethnic group (Caribbean)
  • deprivation index 2 (1-10)
  • work sector / subsector (healthcare/doctor)
  • mode of transport (public)
  • area of residence
  • super area of work

Main data source: census data (NOMIS)

Where can people be?

  • Residence
    • Care Home
    • Household
  • Primary activity
    • Company
    • Hospital
    • School
    • Care Home
    • University
  • Travel
    • Commute
    • National travel
  • Leisure
    • Shopping
    • Pubs / restaurants
    • Cinema

Residence - Household

  • Households based on census data.

area population

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

16 different household compositions

Residence - Household

Tests

North East

London

Residence - Household

Tests

Housemates matrix (London)

Probability

Residence - Care Home

  • Each area can have up to one care home
  • Location based on census data
  • Three populations: residents, workers, and visitors

What can people do?

  • Residence
    • Care Home
    • Household
  • Primary activity
    • Company
    • Hospital
    • School
    • Care Home
    • University
  • Travel
    • Commute
    • National travel
  • Leisure
    • Shopping
    • Pubs / restaurants
    • Cinema

Primary activity - School

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.

Primary activity - Workplace

  • Idea: match residence area => work super area

Flow data

Durham, works in  sector X

Newcastle, industry sector X

Sector info also used for medics and teachers.

Workplace - Residence

City of London workers' usual residence

Workplace - Companies

Company size distribution taken from census data

London

North East

Hospitals

  • We model acute hospital trusts

North East region trusts.

beds

ICU beds

University

  • All universities are included at their exact location.
  • We match communal and student households to university enrolment.

Durham University student households

University

University of Bath student households

What can people do?

  • Residence
    • Care Home
    • Household
  • Primary activity
    • Company
    • Hospital
    • School
    • Care Home
    • University
  • Travel
    • Commute
    • National travel
  • Leisure
    • Shopping
    • Pubs / restaurants
    • Cinema

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}

What can people do?

  • Residence
    • Care Home
    • Household
  • Primary activity
    • Company
    • Hospital
    • School
    • Care Home
    • Universities
  • Travel
    • Commute
    • National travel
  • Leisure
    • Shopping
    • Pubs / restaurants
    • Cinema

Leisure

  • Exact locations of every single pub, restaurant, grocery store, cinema, etc. from OpenStreetMap

Modelling Covid-19

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