Early detection of variants of concern via funnel plots of regional reproduction numbers

Abstract

Early detection of the emergence of a new variant of concern (VoC) is essential to develop strategies that contain epidemic outbreaks. For example, knowing in which region a VoC starts spreading enables prompt actions to circumscribe the geographical area where the new variant can spread, by containing it locally. This paper presents ‘funnel plots’ as a statistical process control method that, unlike tools whose purpose is to identify rises of the reproduction number ($${R}_{t}$$ R t ), detects when a regional $${R}_{t}$$ R t departs from the national average and thus represents an anomaly. The name of the method refers to the funnel-like shape of the scatter plot that the data take on. Control limits with prescribed false alarm rate are derived from the observation that regional $${R}_{t}$$ R t 's are normally distributed with variance inversely proportional to the number of infectious cases. The method is validated on public COVID-19 data demonstrating its efficacy in the early detection of SARS-CoV-2 variants in India, South Africa, England, and Italy, as well as of a malfunctioning episode of the diagnostic infrastructure in England, during which the Immensa lab in Wolverhampton gave 43,000 incorrect negative tests relative to South West and West Midlands territories.

Sources, diffusion and prediction in COVID-19 pandemic: lessons learned to face next health emergency

“…One of the problems is the difficulty of an accurate prediction of the numbers of reported and unreported cases for the COVID-19 pandemic, and similar pandemics for different age classes [146] . Second, the change of Reproduction number in the presence of rapid evolutionary changes of viral agents in variants and sub-variants should be considered to improve the accurateness of epidemiologic modelling of prediction [87] , [147] , [148] . Finally, a lot of confounding and situational factors should be considered for designing accurate measures of preparedness and prediction for future pandemics.…”

Epidemiological inference at the threshold of data availability: an influenza A(H1N2)v spillover event in the United Kingdom

“…A UK-wide project generated and shared hundreds of thousands of SARS-CoV-2 viral sequences [11]. Genome sequence data were used to study virus evolution and transmission on multiple scales [12][13][14][15][16].…”