Assessing the importance of demographic risk factors across two waves of SARS-CoV-2 using fine-scale case data

For the long term control of an infectious disease such as COVID-19, it is crucial to identify the most likely individuals to become infected and the role that differences in demographic characteristics play in the observed patterns of infection. As high-volume surveillance winds down, testing data from earlier periods are invaluable for studying risk factors for infection in detail. Observed changes in time during these periods may then inform how stable the pattern will be in the long term. To this end we analyse the distribution of cases of COVID-19 across Scotland in 2021, where the location (census areas of order 500-1,000 residents) and reporting date of cases are known. We consider over 450,000 individually recorded cases, in two infection waves triggered by different lineages: B.1.1.529 ("Omicron") and B.1.617.2 ("Delta"). We use random forests, informed by measures of geography, demography, testing and vaccination. We show that the distributions are only adequately explained when considering multiple explanatory variables, implying that case heterogeneity arose from a combination of individual behaviour, immunity, and testing frequency. Despite differences in virus lineage, time of year, and interventions in place, we find the risk factors remained broadly consistent between the two waves. Many of the observed smaller differences could be reasonably explained by changes in control measures. The COVID-19 pandemic has seen unprecedented amounts of high-quality data collected for a human disease. For longer-term control in the absence of widespread testing, these data are invaluable for understanding whom amongst the population is at the highest risk of infection. In this work we fit the detailed distributions of COVID-19 cases over Scotland, across two infection waves driven by different variants, to identify risk factors. These were at a time when Scotland had substantial population immunity from prior infection and vaccination, and strict control measures were being relaxed. Differences across the waves may then indicate how stable the pattern of infection will be in the longer term. Despite Scotland's high geographic and demographic diversity, we effectively fit the case distribution in both waves, and find only minor variation between the two. Uniquely, our model was informed by the volume of negative COVID-19 lateral flow tests, and we find that a high rate of negative test reporting was a risk factor for a high rate of cases. This, combined with high variability in testing across demographics, leads us to suggest that patterns in reported case data may in fact be quite different to those of all infections, reported and unreported.