Evidence was brought forward in England and the USA that Black, Asian, Latino and Minority Ethnic people exhibit higher mortality risk from COVID-19 than White people. While socioeconomic factors were suggested to contribute to this trend, they arguably do not explain the range of the differences observed, allowing for possible genetic implications. Almost concurrently, the analysis of a cohort in Chinese COVID-19 patients proposed an association between the severity of the disease and the presence of the minor allele of rs12252 of the Interferon-induced transmembrane protein 3 (IFITM3) gene. This SNP, together with rs34481144, are the two most studied polymorphisms of IFITM3 and have been associated in the past with increased severity in Influenza, Dengue, Ebola, and HIV viruses. IFITM3 is an immune effector protein that is pivotal for the restriction of viral replication, but also for the regulation of cytokine production. Following up on these two developments in the ongoing SARS-CoV-2 pandemic, the present study investigates a possible association between the differences in mortality of ethnic groups in England and the combined haplotypes of rs12252 and rs34481144. The respective allele frequencies were collected for 26 populations from the 1000 Genomes Project and subgroups were pooled wherever possible to create correspondences with ethnic groups in England. A significant correlation (r = 0.9687, p = 0.0003) and a striking agreement was observed between the reported Standardized Mortality Ratios and the frequency of the combined haplotype of both reference alleles, suggesting that the combination of the reference alleles of the specific SNPs may be implicated in more severe outcomes of COVID-19. This study calls for further focus on the role of IFITM3 variants in the mechanism of cellular invasion of SARS-CoV-2, their impact in COVID-19 severity and their possible implications in vaccination efficacy.

The positivity rate of testing is currently used both as a benchmark of testing adequacy and for assessing the evolution of the COVID-19 pandemic. However, since the former is a prerequisite for the latter, its interpretation is often conflicting. We propose as a benchmark for COVID-19 testing effectiveness a new metric, termed ‘Severity Detection Rate’ (SDR), that represents the daily needs for new Intensive Care Unit (ICU) admissions, per 100 cases detected (t − i) days ago, per 10,000 tests performed (t − i) days ago. Based on the announced COVID-19 monitoring data in Greece from May 2020 until August 2021, we show that beyond a certain threshold of daily tests, SDR reaches a plateau of very low variability that begins to reflect testing adequacy. Due to the stabilization of SDR, it was possible to predict with great accuracy the daily needs for new ICU admissions, 12 days ahead of each testing data point, over a period of 10 months, with Pearson r = 0.98 (p = 10–197), RMSE = 7.16. We strongly believe that this metric will help guide the timely decisions of both scientists and government officials to tackle pandemic spread and prevent ICU overload by setting effective testing requirements for accurate pandemic monitoring. We propose further study of this novel metric with data from more countries to confirm the validity of the current findings.

Testing for COVID-19 is an important tool that health administrations dispose to adequately monitor and respond to the pandemic, but it is still unclear at which point the number and strategies of testing become effective for these purposes. The percentage of tests that return a positive result is a metric currently used both as a benchmark of testing adequacy and for assessing the viral spread. However, since the former is a prerequisite for the latter, the interpretation is often conflicting, especially during times of testing scaling-up, or during phases of increasing viral spread. We propose as a benchmark for COVID-19 testing effectiveness a simple metric that creates a link between the cases detected and tests performed, with specific observable outcomes that are actively being monitored in most countries, such as the number of new Intensive Care Unit (ICU) admissions and the number of deaths in the community. This new metric, named "Severity Detection Rate", or SDR, represents the current number of daily needs for new ICU admissions, per 100 cases detected (t-i) days ago, per 10,000 tests performed (t-i) days ago. Based on the announced COVID-19 monitoring data in Greece from May 2020 until end of January 2021, we show that beyond a threshold of daily testing number, SDR reaches a plateau of weak variability that begins to reflect testing adequacy. Because of this stabilization, it was possible to predict with great accuracy the daily needs for new ICU admissions, 12 days ahead of each testing data point, over a period of 6 months that included the second wave of the pandemic in the country, with Pearson r = 0.99 (p = 10^-180), RMSE = 4,34. We suggest the further study of the metric with data from more countries in order to confirm the proposed functionality and utility.

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