Spatial assessment of disease surveillance during the first wave of the COVID-19 pandemic

ABSTRACT

Background: Early surveillance of COVID-19 in Scotland included routine monitoring of positive test rates and COVID-19-related NHS 24 calls. The COVID Symptom Study (CSS) provides another surveillance source, collating self-reported symptoms in the general population and predictions of likely infection. Aim(s) To capture spatial patterns of COVID-19 infection using Spatio-temporal (ST) analyses on three data streams positive test rates, NHS24 calls, and CSS predicted cases. These were compared to assess which was best for early disease surveillance. Method(s) Data streams recorded weekly counts of activity by postcode district (PCD) during the first wave of the pandemic. ST analyses assessed the relationship between COVID-19 testing, NHS 24 COVID-19 calls, and CSS predicted COVID-19 cases, applying a Leroux conditional auto-regression (CAR) spatial GLM, adjusting for spatial covariates. Result(s) Positive test rates were associated with the proportion of NHS 24 calls related to COVID-19 per PCD (OR=1.038, 95% credible interval, 1.024-1.052) and the proportion of CSS app users predicted as cases, (OR=1.014, 0.974-1.056). A temporal effect was seen between all streams, after adjusting for spatial covariates. Using both NHS24 and the CSS to model COVID-19 positive test rates accounted for more ST variability than with the separate models, implying that combining sources may improve surveillance accuracy. Conclusion(s) NHS 24 and the CSS can identify similar trends/clusters of COVID-19 and gold-standard testing data, particularly when used in parallel. In the early stages of a pandemic, when widespread testing might not be available, alternative sources of data may be used to inform outbreak management.