An Analysis of PCR Ct Scores and Distributions from the ONS Community Infection Survey during the COVID-19 Second Wave in the UK

This work presents an analysis of PCR cycle threshold (Ct) scores and their distributions, i.e. the probabilities that a test is positive with a score Ct, P(Ct), derived from the survey during the second COVID wave in the UK. Their relation to gene target breakdown is exemplified. Thus a significant parameter for tracking the course of COVID in the second wave is the percentage of positive tests with Ct < 25, %Ct <25, which is obtained by plotting weekly percentiles from the survey against Ct to construct the ogive or cumulative frequency curve (CMF). The biological basis for studying this parameter is the strong correlation between %Ct < 25 and the percentage of positive tests comprising target genes ORF1ab+N and ORF1ab+N+S, or %Inf. Furthermore, the probability distributions, obtained by differentiating the ogives, were found to be predominantly bimodal with a hot peak at Ct = 20.31+/- 4.65 and a cold peak with Ct = 32.95+/-1.11. These closely match the peaks found for the target genes ORF1ab+N, viz. Ct=18.54+/-2.31 and Ct=32.02+/-0.49 as well as in Walker et al [12]. Similar results were found in [13] and [14]. The cold peak seems likely to be associated with residue from a previous infection. The distributions for gene targets in cfvroc Pillar 2 [15,16] are also bimodal but the peaks occur at lower values of Ct. This suggests the results are machine/sample dependent and emphasises the need for calibration, if quality control in PCR testing is to be improved.

A Scaling Law for PCR Positivity in the COVID Second Wave

In this preliminary report, PCR positivity data in the second wave of the COVID pandemic (September-January 2020) are shown to obey a scaling law given by: O_FD O_INLINEFIG[Formula 1]C_INLINEFIGM_FD(1)C_FD where % P0 and {Sigma}0 are the y- and x-intercepts of a plot of positivity, %P, against the number of tests, {Sigma}. The law holds across international, regional and local boundaries, as demonstrated for Great Britain, Austria, Germany and Sweden, the nine English regions, London - Yorkshire & Humber, and various Local Health Authorities in England. One possible explanation for scaling might be Dorfman pooling. The scaling law can be used to remove a systematic or false positive (FP) component from the daily number of positive tests, or cases, to yield the real number of cases. The results correlate strongly with the ZOE survey for London (R2=0.787) and Excess Deaths for England (R2=0.833). The cumulative total of FPs can be estimated as 1.4M by the beginning of 2021, in line with other estimates.

GPS-estimated foot traffic data and venue selection for COVID-19 serosurveillance studies

The initial phase of the COVID-19 pandemic in the US was marked by limited diagnostic testing, resulting in the need for seroprevalence studies to estimate cumulative incidence and define epidemic dynamics. In lieu of systematic representational surveillance, venue-based sampling was often used to rapidly estimate a communitys seroprevalence. However, biases and uncertainty due to site selection and use of convenience samples are poorly understood. Using data from a SARS-CoV-2 serosurveillance study we performed in Somerville, Massachusetts, we found that the uncertainty in seroprevalence estimates depends on how well sampling intensity matches the known or expected geographic distribution of seropositive individuals in the study area. We use GPS-estimated foot traffic to measure and account for these sources of bias. Our results demonstrated that study-site selection informed by mobility patterns can markedly improve seroprevalence estimates. Such data should be used in the design and interpretation of venue-based serosurveillance studies.