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Coronavirus (COVID-19): modelling the epidemic (issue no.93)

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.

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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 from mid-January 2021 until mid-December 2021, from which point the projections have underestimated the number of infections, due to the unforeseen effects of the Omicron variant. The same is true for the hospital beds projections, however the ICU beds 24 projections have overestimated the actual figures since mid-December 2021, due to the lower severity of Omicron.

Figure 21. Infections projections versus actuals, for historical projections published between one and two weeks before the actual data came in.
A combination line and scatter chart showing infections projections versus actuals, for historical projections published between one and two weeks before the actual data came in.

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

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 2021, after which they include people in ICU over the 28 day limit.

Figure 23. 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 chart showing ICU bed projections versus actuals, for historical projections published between one and two weeks before the actual data came in.

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

Table 1. Probability of local authority areas exceeding thresholds of cases per 100K (20th March to 26th March 2022). Data to 7th March.
Probability of exceeding (cases per 100K)
Local Authority (LA) 50 100 300 500 1000 2000
Aberdeen City 75-100% 75-100% 75-100% 50-75% 25-50% 0-5%
Aberdeenshire 75-100% 75-100% 75-100% 75-100% 75-100% 25-50%
Angus 75-100% 75-100% 75-100% 75-100% 75-100% 25-50%
Argyll and Bute 75-100% 75-100% 75-100% 75-100% 75-100% 25-50%
City of Edinburgh 75-100% 75-100% 75-100% 75-100% 50-75% 15-25%
Clackmannanshire 75-100% 75-100% 75-100% 75-100% 50-75% 15-25%
Dumfries & Galloway 75-100% 75-100% 75-100% 75-100% 50-75% 25-50%
Dundee City 75-100% 75-100% 75-100% 50-75% 25-50% 5-15%
East Ayrshire 75-100% 75-100% 75-100% 75-100% 75-100% 75-100%
East Dunbartonshire 75-100% 75-100% 75-100% 75-100% 75-100% 25-50%
East Lothian 75-100% 75-100% 75-100% 75-100% 50-75% 5-15%
East Renfrewshire 75-100% 75-100% 75-100% 75-100% 50-75% 25-50%
Falkirk 75-100% 75-100% 75-100% 75-100% 75-100% 50-75%
Fife 75-100% 75-100% 75-100% 75-100% 50-75% 25-50%
Glasgow City 75-100% 75-100% 75-100% 75-100% 50-75% 25-50%
Highland 75-100% 75-100% 75-100% 50-75% 25-50% 5-15%
Inverclyde 75-100% 75-100% 75-100% 75-100% 25-50% 5-15%
Midlothian 75-100% 75-100% 75-100% 50-75% 25-50% 0-5%
Moray 75-100% 75-100% 75-100% 75-100% 50-75% 25-50%
Na h-Eileanan Siar 75-100% 75-100% 50-75% 50-75% 15-25% 0-5%
North Ayrshire 75-100% 75-100% 75-100% 75-100% 75-100% 50-75%
North Lanarkshire 75-100% 75-100% 75-100% 75-100% 75-100% 25-50%
Orkney Islands[6] - - - - - -
Perth and Kinross 75-100% 75-100% 75-100% 50-75% 25-50% 0-5%
Renfrewshire 75-100% 75-100% 75-100% 75-100% 50-75% 25-50%
Scottish Borders 75-100% 75-100% 75-100% 75-100% 50-75% 5-15%
Shetland Islands6 - - - - - -
South Ayrshire 75-100% 75-100% 75-100% 75-100% 50-75% 25-50%
South Lanarkshire 75-100% 75-100% 75-100% 75-100% 75-100% 25-50%
Stirling 75-100% 75-100% 75-100% 75-100% 50-75% 15-25%
West Dunbartonshire 75-100% 75-100% 75-100% 75-100% 50-75% 15-25%
West Lothian 75-100% 75-100% 75-100% 75-100% 50-75% 25-50%

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 25th February and 4th 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[7].

Table 2. Average Covid-19 wastewater levels (Mgc/p/d) [8].
Local authority (LA) w/e 25th February w/e 4th March Coverage
Aberdeen City 115 104 99%
Aberdeenshire 80 94 52%
Angus 86 112 68%
Argyll and Bute 77 84 23%
City of Edinburgh 45 96 98%
Clackmannanshire 89 98 92%
Dumfries & Galloway 74 86 29%
Dundee City 92 130 100%
East Ayrshire 66 96 57%
East Dunbartonshire 73 119 99%
East Lothian 43 96 56%
East Renfrewshire 38 87 95%
Falkirk 69 116 96%
Fife 64 113 84%
Glasgow City 75 95 98%
Highland 95 158 39%
Inverclyde 41 104 98%
Midlothian 46 112 88%
Moray 240 180 55%
Na h-Eileanan Siar 21 21%
North Ayrshire 43 75 92%
North Lanarkshire 83 94 92%
Orkney Islands 108 34%
Perth and Kinross 91 73 45%
Renfrewshire 62 75 97%
Scottish Borders 46 83 59%
Shetland Islands 59 8 29%
South Ayrshire 48 102 88%
South Lanarkshire 69 103 77%
Stirling 21 38 63%
West Dunbartonshire 89 114 98%
West Lothian 41 114 95%

Contact

Email: sgcentralanalysisdivision@gov.scot

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