Early detection of variants of concern via funnel plots of regional reproduction numbers.
Simone MilanesiFrancesca RossetMarta ColaneriGiulia GiordanoKenneth PesentiFranco BlanchiniPaolo BolzernPatrizio ColaneriPaolo SacchiGiuseppe De NicolaoRaffaele BrunoPublished in: Scientific reports (2023)
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.
Keyphrases
- sars cov
- south africa
- smoking cessation
- human milk
- electronic health record
- coronavirus disease
- copy number
- healthcare
- big data
- respiratory syndrome coronavirus
- mental health
- quality improvement
- low birth weight
- hiv positive
- preterm infants
- human immunodeficiency virus
- data analysis
- infectious diseases
- genome wide
- adverse drug
- monte carlo
- deep learning
- drug induced
- preterm birth