Publication - Research and analysis

Coronavirus (COVID-19): modelling the epidemic (issue no. 67)

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. 67)
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.
Figure 20. 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.
Figure 21. 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.

Figure 22. 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 (12th to 18th September 2021), data to 27th August.
Probability of exceeding (cases per 100k)
Local Authority (LA) 50 100 300 500 1000 1500
Aberdeen City 75-100% 75-100% 75-100% 50-75% 25-50% 25-50%
Aberdeenshire 75-100% 75-100% 75-100% 50-75% 25-50% 15-25%
Angus 75-100% 75-100% 50-75% 25-50% 15-25% 15-25%
Argyll and Bute 75-100% 75-100% 75-100% 50-75% 25-50% 25-50%
City of Edinburgh 75-100% 75-100% 75-100% 50-75% 50-75% 50-75%
Clackmannanshire 75-100% 75-100% 50-75% 50-75% 25-50% 15-25%
Dumfries & Galloway 75-100% 75-100% 75-100% 75-100% 15-25% 5-15%
Dundee City 75-100% 75-100% 75-100% 50-75% 25-50% 25-50%
East Ayrshire 75-100% 75-100% 75-100% 50-75% 25-50% 25-50%
East Dunbartonshire 75-100% 75-100% 75-100% 75-100% 50-75% 25-50%
East Lothian 75-100% 75-100% 75-100% 25-50% 15-25% 15-25%
East Renfrewshire 75-100% 75-100% 75-100% 75-100% 25-50% 25-50%
Falkirk 75-100% 75-100% 75-100% 75-100% 25-50% 25-50%
Fife 75-100% 75-100% 75-100% 50-75% 50-75% 25-50%
Glasgow City 75-100% 75-100% 75-100% 75-100% 50-75% 50-75%
Highland 75-100% 75-100% 75-100% 50-75% 25-50% 15-25%
Inverclyde 75-100% 75-100% 50-75% 50-75% 50-75% 25-50%
Midlothian 75-100% 75-100% 50-75% 25-50% 25-50% 25-50%
Moray 75-100% 50-75% 25-50% 15-25% 15-25% 5-15%
Na h-Eileanan Siar 25-50% 25-50% 5-15% 0-5% 0-5% 0-5%
North Ayrshire 75-100% 75-100% 75-100% 75-100% 25-50% 25-50%
North Lanarkshire 75-100% 75-100% 75-100% 75-100% 75-100% 50-75%
Orkney Islands 5-15% 0-5% 0-5% 0-5% 0-5% 0-5%
Perth and Kinross 75-100% 75-100% 50-75% 25-50% 15-25% 15-25%
Renfrewshire 75-100% 75-100% 75-100% 75-100% 50-75% 25-50%
Scottish Borders 75-100% 75-100% 50-75% 25-50% 15-25% 15-25%
Shetland Islands 25-50% 15-25% 0-5% 0-5% 0-5% 0-5%
South Ayrshire 75-100% 75-100% 75-100% 75-100% 25-50% 25-50%
South Lanarkshire 75-100% 75-100% 75-100% 75-100% 75-100% 50-75%
Stirling 75-100% 75-100% 75-100% 50-75% 25-50% 25-50%
West Dunbartonshire 75-100% 75-100% 75-100% 75-100% 50-75% 25-50%
West Lothian 75-100% 75-100% 75-100% 75-100% 25-50% 25-50%

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 14th and 21st August, 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 14th August w/b 21st August w/b 14th August w/b 21st August
Aberdeen City 40 88 40 88 80%
Aberdeenshire 28 62 28 56 40%
Angus 44 83 44 83 56%
Argyll and Bute 0%
City of Edinburgh 75 97 75 97 96%
Clackmannanshire 98 183 98 183 70%
Dumfries & Galloway 108 104 88 104 39%
Dundee City 53 96 53 96 100%
East Ayrshire 81 115 81 115 57%
East Dunbartonshire 119 108 119 108 99%
East Lothian 75 92 75 92 65%
East Renfrewshire 148 161 70 161 89%
Falkirk 27 55 27 55 59%
Fife 59 111 59 111 24%
Glasgow City 134 132 106 132 98%
Highland 27 94 27 94 37%
Inverclyde 38 52 38 52 92%
Midlothian 75 114 75 114 88%
Moray 20 24 20 24 70%
Na h-Eileanan Siar 41 0%
North Ayrshire 91 86 91 86 85%
North Lanarkshire 72 123 72 123 80%
Orkney Islands 35 11 35 11 34%
Perth and Kinross 63 63 9%
Renfrewshire 132 156 115 156 13%
Scottish Borders 65 108 65 110 38%
Shetland Islands 0 1 0 1 29%
South Ayrshire 75 115 75 115 77%
South Lanarkshire 95 195 75 195 62%
Stirling 40 40 63%
West Dunbartonshire 118 108 118 108 48%
West Lothian 78 78 2%

Contact

Email: modellingcoronavirus@gov.scot