Academic Journal
Early detection of variants of concern via funnel plots of regional reproduction numbers
| Title: | Early detection of variants of concern via funnel plots of regional reproduction numbers |
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| Authors: | Simone Milanesi, Francesca Rosset, Marta Colaneri, Giulia Giordano, Kenneth Pesenti, Franco Blanchini, Paolo Bolzern, Patrizio Colaneri, Paolo Sacchi, Giuseppe De Nicolao, Raffaele Bruno |
| Source: | Scientific Reports, Vol 13, Iss 1, Pp 1-14 (2023) |
| Publisher Information: | Nature Portfolio, 2023. |
| Publication Year: | 2023 |
| Collection: | LCC:Medicine LCC:Science |
| Subject Terms: | Medicine, Science |
| More Details: | 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. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 2045-2322 |
| Relation: | https://doaj.org/toc/2045-2322 |
| DOI: | 10.1038/s41598-022-27116-8 |
| Access URL: | https://doaj.org/article/7055601f817b49c5b2d47a70336eac73 |
| Accession Number: | edsdoj.7055601f817b49c5b2d47a70336eac73 |
| Database: | Directory of Open Access Journals |
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| ISSN: | 20452322 |
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| DOI: | 10.1038/s41598-022-27116-8 |
| Published in: | Scientific Reports |
| Language: | English |