Coronavirus (COVID-19): modelling the epidemic (issue no.91)

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 have the Covid-19 scenarios been developed?

The Covid-19 scenarios have been developed using the same Scottish Government model used for our usual Medium Term Projections modelling. The scenarios we provide look at what could happen for planning purposes, not to forecast what will happen.

The scenarios take the actuals data and project these forward using the same underlying assumptions as are currently used to model Omicron and are included in the SPI-M-O consensus. These assumptions are then adapted as follows:

• Immune world – assumes a lower transmission than currently due to high levels of natural and vaccine acquired immunity.
• Waning world – assumes the same transmission level with vaccine effectiveness waning over time^[13].
• Polarised world - assumes that behavioural changes beginning to occur. Unvaccinated adults and those with one dose are assumed to return to pre-pandemic behaviours within six weeks. Adults with two doses are assumed to return to these behaviours over 12 weeks, with those with three doses assumed to have no significant changes in behaviour.
• Variant world (vaccine escape) – assumes a variant with similar transmissibility and vaccine escape characteristics as Omicron and same severity as Delta.
• Variant world (vaccine escape & increased severity) – assumes a variant with similar transmissibility and vaccine escape characteristics as Omicron and a higher severity than Delta.

The assumptions are based on our most up to date knowledge, but do not include the effect of future changes in treatment of Covid-19 e.g. widespread use of antivirals or changes in behaviour in response to high levels of infections e.g. in Variant world.

To reflect the uncertainty the modelling the graphs provide a range for possible outcomes.

As more information becomes available we will revise the modelling where appropriate.

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

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 14th February and 21st February 2022, with no estimate for error. This is given in Million gene copies per person per day. Coverage is given as percentage of LA inhabitants covered by a wastewater Covid‑19 sampling site delivering data during this period^[14].