tetano
Editor, Senior Moderator
Sci Rep
. 2023 Jan 19;13(1):1052.
doi: 10.1038/s41598-022-27116-8.
Early detection of variants of concern via funnel plots of regional reproduction numbers
Simone Milanesi[SUP] 1 [/SUP], Francesca Rosset[SUP] 2 [/SUP], Marta Colaneri[SUP] 3 [/SUP], Giulia Giordano[SUP] 4 [/SUP], Kenneth Pesenti[SUP] 5 [/SUP], Franco Blanchini[SUP] 2 [/SUP], Paolo Bolzern[SUP] 6 [/SUP], Patrizio Colaneri[SUP] 6 7 [/SUP], Paolo Sacchi[SUP] 3 [/SUP], Giuseppe De Nicolao[SUP] 8 9 [/SUP], Raffaele Bruno[SUP] 3 10 [/SUP]
Affiliations
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 ([Formula: see text]), detects when a regional [Formula: see text] 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 [Formula: see text]'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.
. 2023 Jan 19;13(1):1052.
doi: 10.1038/s41598-022-27116-8.
Early detection of variants of concern via funnel plots of regional reproduction numbers
Simone Milanesi[SUP] 1 [/SUP], Francesca Rosset[SUP] 2 [/SUP], Marta Colaneri[SUP] 3 [/SUP], Giulia Giordano[SUP] 4 [/SUP], Kenneth Pesenti[SUP] 5 [/SUP], Franco Blanchini[SUP] 2 [/SUP], Paolo Bolzern[SUP] 6 [/SUP], Patrizio Colaneri[SUP] 6 7 [/SUP], Paolo Sacchi[SUP] 3 [/SUP], Giuseppe De Nicolao[SUP] 8 9 [/SUP], Raffaele Bruno[SUP] 3 10 [/SUP]
Affiliations
- PMID: 36658143
- DOI: 10.1038/s41598-022-27116-8
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 ([Formula: see text]), detects when a regional [Formula: see text] 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 [Formula: see text]'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.