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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.

We report on a suite of modeling approaches for the optimization of Avalanche Photodiodes for X-rays detection. Gain and excess noise are computed efficiently using a non-local/history dependent model that has been validated against full-band Monte Carlo simulations. The (stochastic) response of the detector to photon pulses is computed using an improved Random-Path-Length algorithm. As case studies, we consider diodes consisting of AlGaAs/GaAs multi-layers with separated absorption and multiplication regions. A superlattice creating a staircase conduction band structure is employed in the multiplication region to keep the multiplication noise low. Gain and excess noise have been measured in devices fabricated with such structure and successfully compared with the developed models.

Tools to early detect the emergence of a new variant of concern are essential to develop strategies that contain epidemic outbreaks and their health-economic-social consequences. For example, knowing in which region a variant of concern appears or starts spreading enables prompt actions to circumscribe the diffusion area. This paper presents ‘funnel plots’ as a statistical method that can quickly identify regions of a country where the reproduction number is anomalous with respect to the national one, thus triggering cross-cutting research, while keeping false alarms under control. COVID-19 data demonstrate the efficacy of the method in the early detection of Delta and Omicron variants in India, South Africa, England, and Italy, as well as a malfunctioning episode of the diagnostic infrastructure in England.

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