Reassessing the evidence for universal school-age BCG vaccination in England and Wales: re-evaluating and updating a modelling study

ObjectivesIn 2005, England and Wales switched from universal BCG vaccination against tuberculosis (TB) disease for school-age children to targeted vaccination of neonates. We aimed to recreate and re-evaluate a previously published model, the results of which informed this policy change.DesignWe recreated an approach for estimating the impact of ending the BCG schools scheme, correcting a methodological flaw in the model, updating the model with parameter uncertainty and improving parameter estimates where possible. We investigated scenarios for the assumed annual decrease in TB incidence rates considered by the UK’s Joint Committee on Vaccination and Immunisation and explored alternative scenarios using notification data.SettingEngland and Wales.Outcome measuresThe number of vaccines needed to prevent a single notification and the average annual additional notifications caused by ending the policy change.ResultsThe previously published model was found to contain a methodological flaw and to be spuriously precise. It greatly underestimated the impact of ending school-age vaccination compared with our updated, corrected model. The updated model produced predictions with wide CIs when parameter uncertainty was included. Model estimates based on an assumption of an annual decrease in TB incidence rates of 1.9% were closest to those estimated using notification data. Using this assumption, we estimate that 1600 (2.5; 97.5% quantiles: 1300, 2000) vaccines would have been required to prevent a single notification in 2004.ConclusionsThe impact of ending the BCG schools scheme was found to be greater than previously thought when notification data were used. Our results highlight the importance of independent evaluations of modelling evidence, including uncertainty, and evaluating multiple scenarios when forecasting the impact of changes in vaccination policy.

Modeling for COVID-19 college reopening decisions: Cornell, a case study

We consider epidemiological modeling for the design of COVID-19 interventions in university populations, which have seen significant outbreaks during the pandemic. A central challenge is sensitivity of predictions to input parameters coupled with uncertainty about these parameters. Nearly 2 y into the pandemic, parameter uncertainty remains because of changes in vaccination efficacy, viral variants, and mask mandates, and because universities’ unique characteristics hinder translation from the general population: a high fraction of young people, who have higher rates of asymptomatic infection and social contact, as well as an enhanced ability to implement behavioral and testing interventions. We describe an epidemiological model that formed the basis for Cornell University’s decision to reopen for in-person instruction in fall 2020 and supported the design of an asymptomatic screening program instituted concurrently to prevent viral spread. We demonstrate how the structure of these decisions allowed risk to be minimized despite parameter uncertainty leading to an inability to make accurate point estimates and how this generalizes to other university settings. We find that once-per-week asymptomatic screening of vaccinated undergraduate students provides substantial value against the Delta variant, even if all students are vaccinated, and that more targeted testing of the most social vaccinated students provides further value.

Decentralized Frequency Regulation of Hybrid MTDC-linked Grids

This paper proposes a new strategy for optimal grid frequency regulation (FR) in an interconnected power system where regional ac grids and an offshore wind farm are linked via a multi-terminal high voltage direct<em>-</em>current (MTDC) network. In the proposed strategy, decentralized <i>H</i>_∞ controllers are developed to coordinate the operations of ac synchronous generators and hybrid MTDC converters, thus achieving optimal power sharing of interconnected ac grids and minimizing frequency deviations in each grid. To develop the controllers, robust optimization problems are formulated and solved using a dynamic model of the hybrid MTDC-linked grids with model parameter uncertainty and decentralized control inputs and outputs. The model orders of the resulting controllers are then reduced using a balanced truncation algorithm to eliminate unobservable and uncontrollable state variables while preserving their dominant response characteristics. Sensitivity and eigenvalue analyses are conducted focusing on the effects of grid measurements, parameter uncertainty levels, and communication time delays. Comparative case studies are also carried out to verify that the proposed strategy improves the effectiveness, stability, and robustness of real-time FR in MTDC-linked grids under various conditions characterized mainly by load demands, communications systems, and weighting functions.

Decentralized Frequency Regulation of Hybrid MTDC-linked Grids

This paper proposes a new strategy for optimal grid frequency regulation (FR) in an interconnected power system where regional ac grids and an offshore wind farm are linked via a multi-terminal high voltage direct<em>-</em>current (MTDC) network. In the proposed strategy, decentralized <i>H</i>_∞ controllers are developed to coordinate the operations of ac synchronous generators and hybrid MTDC converters, thus achieving optimal power sharing of interconnected ac grids and minimizing frequency deviations in each grid. To develop the controllers, robust optimization problems are formulated and solved using a dynamic model of the hybrid MTDC-linked grids with model parameter uncertainty and decentralized control inputs and outputs. The model orders of the resulting controllers are then reduced using a balanced truncation algorithm to eliminate unobservable and uncontrollable state variables while preserving their dominant response characteristics. Sensitivity and eigenvalue analyses are conducted focusing on the effects of grid measurements, parameter uncertainty levels, and communication time delays. Comparative case studies are also carried out to verify that the proposed strategy improves the effectiveness, stability, and robustness of real-time FR in MTDC-linked grids under various conditions characterized mainly by load demands, communications systems, and weighting functions.