Coronavirus (COVID-19): modelling the epidemic (Issue No.96)

Latest findings in modelling the COVID-19 epidemic in Scotland, both in terms of the spread of the disease through the population (epidemiological modelling) and of the demands it will place on the system, for example in terms of health care requirement.

Technical Annex

Epidemiology is the study of how diseases spread within populations. One way we do this is using our best understanding of the way the infection is passed on and how it affects people who catch it to create mathematical simulations. Because people who catch Covid-19 have a relatively long period in which they can pass it on to others before they begin to have symptoms, and the majority of people infected with the virus will experience mild symptoms, this "epidemiological modelling" provides insights into the epidemic that cannot easily be measured through testing e.g. of those with symptoms, as it estimates the total number of new daily infections and infectious people, including those who are asymptomatic or have mild symptoms.

Modelling also allows us to make short-term forecasts of what may happen with a degree of uncertainty. These can be used in health care and other planning. The modelling in this research findings is undertaken using different types of data which going forward aims to both model the progress of the epidemic in Scotland and provide early indications of where any changes are taking place.

The delivery of the vaccination programme will offer protection against severe disease and death. The modelling includes assumptions about compliance with restrictions and vaccine take-up. Work is still ongoing to understand how many vaccinated people might still spread the virus if infected. As Covid-19 is a new disease there remain uncertainties associated with vaccine effectiveness. Furthermore, there is a risk that new variants emerge for which immunisation is less effective.

Which local authorities are likely to experience high levels of Covid-19 in two weeks' time

Table 2. Probability of local authority areas exceeding thresholds of cases per 100K (10th to 16th April 2022), data to 28th March. The local modelling consensus will be paused in future weeks but will be ready to re-instate if required in the future.

Local Authority Probability of exceeding (cases per 100K)
50 100 300 500 600 750 1000
Aberdeen City 75-100% 75-100% 75-100% 75-100% 75-100% 50-75% 50-75%
Aberdeenshire 75-100% 75-100% 75-100% 75-100% 75-100% 50-75% 50-75%
Angus 75-100% 75-100% 75-100% 50-75% 50-75% 50-75% 25-50%
Argyll and Bute 75-100% 75-100% 75-100% 75-100% 50-75% 50-75% 50-75%
City of Edinburgh 75-100% 75-100% 75-100% 75-100% 75-100% 75-100% 50-75%
Clackmannanshire 75-100% 75-100% 75-100% 50-75% 50-75% 50-75% 25-50%
Dumfries & Galloway 75-100% 75-100% 75-100% 75-100% 75-100% 50-75% 50-75%
Dundee City 75-100% 75-100% 75-100% 75-100% 75-100% 50-75% 25-50%
East Ayrshire 75-100% 75-100% 75-100% 50-75% 50-75% 50-75% 50-75%
East Dunbartonshire 75-100% 75-100% 75-100% 50-75% 50-75% 50-75% 50-75%
East Lothian 75-100% 75-100% 75-100% 50-75% 50-75% 50-75% 50-75%
East Renfrewshire 75-100% 75-100% 75-100% 75-100% 50-75% 50-75% 25-50%
Falkirk 75-100% 75-100% 75-100% 75-100% 75-100% 50-75% 50-75%
Fife 75-100% 75-100% 75-100% 75-100% 75-100% 75-100% 50-75%
Glasgow City 75-100% 75-100% 75-100% 75-100% 75-100% 50-75% 25-50%
Highland 75-100% 75-100% 75-100% 75-100% 50-75% 50-75% 50-75%
Inverclyde 75-100% 75-100% 75-100% 50-75% 50-75% 50-75% 25-50%
Midlothian 75-100% 75-100% 75-100% 75-100% 75-100% 50-75% 50-75%
Moray 75-100% 75-100% 75-100% 75-100% 50-75% 50-75% 25-50%
Na h-Eileanan Siar[15] - - - - - - -
North Ayrshire 75-100% 75-100% 75-100% 75-100% 50-75% 50-75% 50-75%
North Lanarkshire 75-100% 75-100% 75-100% 75-100% 75-100% 50-75% 25-50%
Orkney Islands[15] - - - - - - -
Perth and Kinross 75-100% 75-100% 75-100% 75-100% 75-100% 50-75% 50-75%
Renfrewshire 75-100% 75-100% 75-100% 75-100% 50-75% 50-75% 50-75%
Scottish Borders 75-100% 75-100% 75-100% 75-100% 75-100% 75-100% 50-75%
Shetland Islands[15] - - - - - - -
South Ayrshire 75-100% 75-100% 75-100% 75-100% 75-100% 50-75% 50-75%
South Lanarkshire 75-100% 75-100% 75-100% 50-75% 50-75% 50-75% 25-50%
Stirling 75-100% 75-100% 75-100% 75-100% 50-75% 50-75% 50-75%
West Dunbartonshire 75-100% 75-100% 75-100% 50-75% 50-75% 50-75% 25-50%
West Lothian 75-100% 75-100% 75-100% 75-100% 75-100% 50-75% 50-75%

What levels of Covid-19 are indicated by wastewater data?

Table 2 provides population weighted daily averages for normalised WW Covid-19 levels in the weeks ending 22nd March and 29th March 2022, with no estimate for error. This is given in Million gene copies per person per day. Coverage is given as percentage of inhabitants in each local authority covered by a wastewater Covid‑19 sampling site delivering data during this period.[16]

Table 2. Average Covid-19 wastewater levels (Mgc/p/d). [17]
Local authority (LA) w/e 22nd March w/e 29th March Coverage
Aberdeen City 125 210 80%
Aberdeenshire 180 232 28%
Angus 256 258 68%
Argyll and Bute 102 311 29%
City of Edinburgh 205 265 98%
Clackmannanshire 270 752 11%
Dumfries & Galloway 197 156 35%
Dundee City 256 279 100%
East Ayrshire 112 411 72%
East Dunbartonshire 275 297 99%
East Lothian 228 249 65%
East Renfrewshire 196 360 95%
Falkirk 273 333 88%
Fife 184 370 73%
Glasgow City 235 320 75%
Highland 289 421 34%
Inverclyde 248 236 98%
Midlothian 210 242 88%
Moray 313 0%
Na h-Eileanan Siar 0%
North Ayrshire 166 242 84%
North Lanarkshire 196 298 91%
Orkney Islands 168 284 34%
Perth and Kinross 234 136 38%
Renfrewshire 205 272 97%
Scottish Borders 102 175 46%
Shetland Islands 47 0%
South Ayrshire 138 338 77%
South Lanarkshire 141 385 74%
Stirling 45 166 63%
West Dunbartonshire 190 229 98%
West Lothian 227 396 79%

How will Scottish Government models be used in the future?

The following table provides information of the Covid-19 models used in Scottish Government, their outputs, the section they refer to in the Modelling the Epidemic report and changes to the frequency of results.

Over the coming weeks, archiving of models will be undertaken via the publicly available Data Science Scotland GitHub organisation.[18]

Table 4. Scottish Government models

Model

Outputs

Section in Modelling the Epidemic Report

Change

Scottish Contact Survey

Average number and nature of contacts for adults in Scotland per day

What we know about how people's contact patterns have changed

Weekly panels will be merged together and results will published fortnightly

Wastewater

National and local Covid-19 estimates

Sequencing outputs

What can analysis of wastewater samples tell us about local outbreaks of Covid-19 infection?

Weekly updates will still be available. Results will be published fortnightly.

Covasim

Estimates R using contact patterns

Not currently published

This is still in development. Estimates will be published fortnightly as part of the EMRG consensus

Epidemia

Estimates of R incidence and growth rate

Overview of Scottish Government Modelling

Estimates will be published fortnightly as part of the EMRG consensus

Long Covid

A projection of estimated self-reported long Covid-19 rates in the future

Long Covid Estimates

Work will be undertaken to assess if this project can continue.

Medium Term Projections Modelling

Projections for incidence, hospital beds, ICU demand and deaths.

What the modelling tells us about estimated infections and hospitalisations

Estimate will be published fortnightly as part of the EMRG consensus

Local authority cases projections

Gives projected number of cases in each local authority in two weeks' time.

What we know about which local authorities are likely to experience high levels of Covid-19 in two weeks' time

This will be archived.

Enduring Transmission (not published)

Informs estimates of Health Board demand for Hospital and ICU beds

Identifies areas (intermediate zones) of early increasing prevalence and/or slower decline in prevalence

Not currently published

This will be archived.

CANNA Model (not published)

Estimates impact of Test and Protect policy

Not currently published

This will be archived

Kalman Filter (not published used as an internal check)

R estimate as a comparison to other methods

Not currently published

This will be archived

Doubling time

Estimates the time required for the relative abundance of the Omicron variant to double in the Scottish population

What we know about the Omicron variant

This will be archived

Exceedance

Calculates whether the number of confirmed infections (based on testing) in each area exceeds the number that was expected when cases are below 50 per 100,000

What the modelling tells us about whether Covid-19 infections exceeded what would be expected at this stage in the epidemic

This will be archived