Publication - Research and analysis

Coronavirus (COVID-19): modelling the epidemic (Issue No. 63)

Published: 5 Aug 2021

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.

Coronavirus (COVID-19): modelling the epidemic (Issue No. 63)
Technical Annex

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.

How the modelling compares to the real data as it emerges

The following charts show the history of our modelling projections in comparison to estimates of the actual data. The infections projections were largely accurate during October to mid-December and from mid‑January onward. During mid-December to mid-January, the projections underestimated the number of infections, due to the unforeseen effects of the new variant.

Figure 20. Infections projections versus actuals, for historical projections published between one and two weeks before the actual data came in.

A combination line and scatter graph comparing infections projections against actuals.

Hospital bed projections have generally been more precise than infections estimates due to being partially based on already known information about numbers of current infections, and number of people already in hospital. The projections are for number of people in hospital due to Covid-19, which is slightly different to the actuals, which are number of people in hospital within 28 days of a positive Covid-19 test.

Figure 21. Hospital bed projections versus actuals, for historical projections published between one and two weeks before the actual data came in.

A combination line and scatter graph comparing hospital bed occupancy projections against actuals.

As with hospital beds, ICU bed projections have generally been more precise than infections. The projections are for number of people in ICU due to Covid-19. The actuals are number of people in ICU within 28 days of a positive Covid-19 test up to 20 January, after which they include people in ICU over the 28 day limit.

Figure 22. ICU bed projections versus actuals, for historical projections published between one and two weeks before the actual data came in.

A combination line and scatter graph comparing ICU occupancy projections against actuals.

Table 1. Probability of local authority areas exceeding thresholds of cases per 100K (15th to 21st August 2021), data to 2nd August.
Probability of exceeding (cases per 100k)
Local Authority (LA) 50 100 300 500
Aberdeen City 50-75% 15-25% 0-5% 0-5%
Aberdeenshire 25-50% 5-15% 0-5% 0-5%
Angus 25-50% 5-15% 0-5% 0-5%
Argyll and Bute 25-50% 25-50% 0-5% 0-5%
City of Edinburgh 25-50% 5-15% 0-5% 0-5%
Clackmannanshire 50-75% 25-50% 15-25% 5-15%
Dumfries & Galloway 50-75% 25-50% 5-15% 0-5%
Dundee City 25-50% 15-25% 0-5% 0-5%
East Ayrshire 25-50% 15-25% 0-5% 0-5%
East Dunbartonshire 25-50% 15-25% 0-5% 0-5%
East Lothian 25-50% 15-25% 0-5% 0-5%
East Renfrewshire 25-50% 15-25% 0-5% 0-5%
Falkirk 25-50% 5-15% 0-5% 0-5%
Fife 25-50% 15-25% 0-5% 0-5%
Glasgow City 50-75% 5-15% 0-5% 0-5%
Highland 25-50% 15-25% 0-5% 0-5%
Inverclyde 75-100% 25-50% 15-25% 5-15%
Midlothian 50-75% 15-25% 0-5% 0-5%
Moray 25-50% 25-50% 0-5% 0-5%
Na h-Eileanan Siar 25-50% 5-15% 0-5% 0-5%
North Ayrshire 50-75% 25-50% 15-25% 5-15%
North Lanarkshire 75-100% 25-50% 0-5% 0-5%
Orkney Islands 0-5% 0-5% 0-5% 0-5%
Perth and Kinross 15-25% 5-15% 0-5% 0-5%
Renfrewshire 25-50% 15-25% 0-5% 0-5%
Scottish Borders 25-50% 15-25% 0-5% 0-5%
Shetland Islands 0-5% 0-5% 0-5% 0-5%
South Ayrshire 25-50% 25-50% 5-15% 0-5%
South Lanarkshire 75-100% 25-50% 25-50% 5-15%
Stirling 50-75% 25-50% 5-15% 0-5%
West Dunbartonshire 75-100% 25-50% 15-25% 0-5%
West Lothian 50-75% 25-50% 5-15% 0-5%

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

Table 2 provides population weighted daily averages for normalised WW Covid-19 levels in the weeks beginning the 17th and 24th July, with no estimate for error. This is given in Million gene copies per person, which approximately corresponds to new cases per 100,000 per day. Coverage is given as percentage of LA inhabitants covered by a wastewater Covid‑19 sampling site delivering data during this period[14].

Table 2. Average daily cases per 100k as given by WW data
Local authority (LA) Average daily WW case estimate,
with outliers included
Average daily WW case estimate,
with outliers removed
Coverage[15]
w/b 17th July w/b 24th July w/b 17th July w/b 24th July
Aberdeen City 44 35 44 35 80 %
Aberdeenshire 30 29 28 28 52 %
Angus 46 31 46 31 56 %
Argyll and Bute 24 15 24 15 18 %
City of Edinburgh 45 31 45 31 96 %
Clackmannanshire 53 49 37 49 23 %
Dumfries & Galloway 23 22 22 22 10 %
Dundee City 45 36 45 36 100 %
East Ayrshire 38 41 38 41 72 %
East Dunbartonshire 48 38 48 38 99 %
East Lothian 44 29 44 29 65 %
East Renfrewshire 50 43 50 43 95 %
Falkirk 33 27 33 27 69 %
Fife 58 55 58 48 50 %
Glasgow City 60 58 60 58 98 %
Highland 28 54 28 49 32 %
Inverclyde 22 44 22 44 92 %
Midlothian 45 33 45 33 88 %
Moray 22 25 22 25 56 %
Na h-Eileanan Siar 13 7 13 7 21 %
North Ayrshire 20 23 20 23 93 %
North Lanarkshire 95 72 95 72 94 %
Orkney Islands 6 11 6 11 34 %
Perth and Kinross 24 27 24 27 44 %
Renfrewshire 85 50 85 50 57 %
Scottish Borders 19 22 17 22 51 %
Shetland Islands 8 7 8 7 29 %
South Ayrshire 42 40 42 40 84 %
South Lanarkshire 75 82 55 82 90 %
Stirling 11 19 11 19 63 %
West Dunbartonshire 30 34 30 34 98 %
West Lothian 53 54 46 45 85 %

Contact

Email: modellingcoronavirus@gov.scot