Lancaster-Liverpool COVID-19 Demand Model

Technical details: Lancaster-Liverpool COVID-19 Demand Model

About the model

The model is a hybrid model with a transmission model feeding its output to a healthcare demand model. The transmission model is a dynamic infectious disease transmission model that takes into account the age-group profile of local populations, social mixing patterns between people of different ages as well as transport data and interactions between locations to represent how the epidemic is likely to spread across the region. The model outputs are then used to explore the probable spread of SARS-CoV-2 and the demands on services caring for people with COVID-19.

The technical classification of the model is a deterministic SEIR (Susceptible, Exposed, Infectious, Recovered) metapopulation model. The population is divided up into LAD geographies and 5-year age/sex groups, using the 2011 census to estimate the number of people in each age/sex group by LAD. The structure of contacts between LAD residents that may lead to SARS-CoV-2 transmission is based on previously measured social mixing information (the POLYMOD survey data) – this is a quantitative, evidence-based way of considering how contacts between people may transmit infectious diseases spread by breathing or close contact.[1] To emulate the effect of school closures, the information from POLYMOD was modified to exclude contact between school-age children outside of the household after 23/03/20. Transmission of the virus between LADs is assumed to follow commuting patterns, derived from 2011 census commuting data, which are down-scaled each day during the epidemic period, using transport data, relative to February, which showed a marked decline leading up to and following 23/03/20.

 

Model Outputs

The healthcare demand model is a discrete event simulation that simulates the individual trajectories of the infected, using output from the transmission model (number of new symptomatic infections per day, by age group and location). The model calculates the following: –

  1. number in each age-group LAD that will seek care
  2. number in each age-group LAD that will seek care and be admitted
  3. number admitted in each age-group LAD that require a bed in an Intensive Care Unit [ICU]
  4. number of admitted non-ICU beds discharged per day
  5. number of admitted to ICU beds discharged per day
  6. number of admitted to non-ICU beds that die per day
  7. number of admitted ICU beds that die per day

 

Modelling parameters and assumptions 

A summary of the model parameters is given below: –

Table 1: Lancaster-Liverpool COVID19 Demand Model main parameters and assumptions

Parameter Assumption/Estimate Source
Latent period (time from being infected to being infectious) 4 days Assumed
The hospitalisation rate (% of infections needing hospital admission) 8% (varying by age from <50 – 2%; >80 – 44%) PHE Joint Modelling Cell
Intensive Care Unit [ICU] rate (% of hospitalisations needing a CCU bed) 25 % (varying by age – peak rate at age 60 reflecting current age distribution of COVID19 patients in ICU) 25% admitted to ICU from Covid-19 NHS dashboard applying the age distribution from ICNARC
Onset to attendance at hospital Emergency Department [ED] Gamma distribution (shape=2, rate=3.4), with median 4 days Estimated from COCIN UK patient data
ED to Admission Same Day Assumed
Admission to ICU Poisson distribution, mean 1 day Assumed
Admission (general) to discharge Uniform distribution, 4-8 days Zhou 2020
ICU to discharge Uniform distribution, 10-14 days Guan 2020

 

Table 2: Items in the output file (values are the medians of 200 simulations – the file does not yet include measures of uncertainty, future versions will)

Variable Description
lad11cd ONS code
ED_0.5 Number of Attendances at A&E
bed_norm_0.5 Number of Normal Beds occupied (levels 1-3)
bed_icu_0.5 Number of ICU Beds occupied (levels 3)
discharge_norm_0.5 Discharges from normal beds
discharge_icu_0.5 Discharges from ICU beds
death_norm_0.5 Deaths from normal beds
death_icu_0.5 Deaths from ICU beds
inf Daily new infections
la_name LA name
date date

Comparing Lancaster-Liverpool COVID-19 models, with the output from applying the national Reasonable Worst Case Scenario model to the local population, the latter gives 18% lower number of infections, 35% lower number of people needing hospitalisation or intensive care, 36% lower peak demand on intensive care beds.

The current model does not fully consider the impact on transmission of recent control measures. It should therefore be understood and interpreted as a worst-case scenario projection. There is considerable uncertainty around the estimates, particularly for smaller geographic areas.

 

Find out more

For more information on the Lancaster-Liverpool COVID-19 Model, including the associated code please see https://github.com/cipha-uk

 

Attribution

Alex Alexiou, Matt Ashton, Ben Barr, Iain Buchan, Martin O’Flaherty, Chris Jewell, Rachel Joynes, Chris Kypridemos, Roberta Piroddi, Jonathan Read and Sally Sheard on behalf of MRF Health Intelligence Cell.

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