Epidemiological impact of a large number of incorrect negative SARS-CoV-2 test results in South West England during September and October 2021 (preprint)

Background In England, free testing for COVID-19 was widely available from early in the pandemic until 1 April 2022. Based on apparent differences in the rate of positive PCR tests at a single laboratory compared to the rest of the laboratory network, we hypothesised that a substantial number of UK PCR tests processed during September and October 2021 may have been incorrectly reported as negative, compared with the rest of the laboratory network. We investigate the epidemiological impact of this incident. Methods We estimate the additional number of COVID-19 cases that would have been reported had the sensitivity of the laboratory test procedure not dropped for the period 2 September to 12 October. In addition, by making comparisons between the most affected local areas and comparator populations, we estimate the number of additional infections, cases, hospitalisations and deaths that could have occurred as a result of increased transmission due to the misclassification of tests. Results We estimate that around 39,000 tests may have been incorrectly classified during this period and, as a direct result of this incident, the most affected areas in the South West could have experienced between 6,000 and 34,000 additional reportable cases, with a central estimate of around 24,000 additional reportable cases. Using modelled relationships between key variables, we estimate that this central estimate could have translated to approximately 55,000 additional infections, which means that each incorrect negative test likely led to just over two additional infections. In those same geographical areas, our results also suggest an increased number of admissions and deaths. Conclusion The incident is likely to have had a measurable impact on cases and infections in the affected areas in the South West of England.

Diversity of symptom phenotypes in SARS-CoV-2 community infections observed in multiple large datasets (preprint)

Through the use of cutting-edge unsupervised classification techniques from statistics and machine learning, we characterise symptom phenotypes among symptomatic SARS-CoV-2 PCR-positive community cases. We first analyse each dataset in isolation and across age bands, before using methods that allow us to compare multiple datasets. While we observe separation due to the total number of symptoms experienced by cases, we also see a separation of symptoms into gastrointestinal, respiratory and other types, and different symptom co-occurrence patterns at the extremes of age. In this way, we are able to demonstrate the deep structure of symptoms of COVID-19 without usual biases due to study design. This is expected to have implications for the identification and management of community SARS-CoV-2 cases and could be further applied to symptom-based management of other diseases and syndromes.