Long-term spatial patterns in COVID-19 booster vaccine uptake

Vaccination is a critical tool for controlling infectious diseases, with its use to protect against COVID-19 being a prime example. Where a disease is highly transmissible, even a small proportion of unprotected individuals can have substantial implications for disease burden and control. As factors such as deprivation and ethnicity have been shown to influence uptake rates, identifying how uptake varies with socio-demographic indicators is critical for reducing hesitancy and issues of access and identifying plausible future uptake patterns.

We analyse COVID-19 booster vaccinations in Scotland, subdivided by age, sex, dose and location. Linking to public demographic data, we use Random Forests to fit patterns in first booster uptake, with systematic variation restricted to  ~ 1km in urban areas. We introduce a method to predict future distributions using our first booster model, assuming existing trends over deprivation will persist. This provides a quantitative estimate of the impact of changing motivations and efforts to increase uptake.

While age and sex have the greatest impact on the model fit, there is a substantial influence of community deprivation and the proportion of residents belonging to a black or minority ethnicity. Differences between first and second boosters suggest in the longer-term that the impact of deprivation is likely to increase.

This would further the disproportionate impact of COVID-19 on deprived communities. Our methods are based solely on public demographic data and routinely recorded vaccination data, and would be easily adaptable to other countries and vaccination campaigns where data recording is similar.