The SARS-CoV-2 pandemic caused devastating health, social and economic harms. Globally, during the first year of this pandemic, controls were primarily focused on containing the outbreak and minimising infection, disease and health consequences. However, during the second year (2021) with vaccines being deployed, there was a recognition that mitigation measures needed to consider both infection control and socio-economic priorities, inevitably causing a tension between how these conflicting elements are balanced. In England, this was epitomised by the Roadmap out of Lockdown, which through 4 planned steps reduced controls from the January 2021 lockdown to no restrictions on social interactions. Here, we adopt a health-economic framework to consider a range of alternative approaches to the relaxation of controls, allowing us to quantitatively assess health losses (measured in Quality Adjusted Life Years, QALYs) and economic consequences (measured in terms of Gross Domestic Product, GDP) in a unified framework. Using the UK Treasury framework of valuing one QALY at 70,000 pounds, we find that the implemented Roadmap performs extremely well, only being consistently outperformed by a similar strategy that starts all steps one week earlier. This work highlights the power of a holistic framework combining epidemiology and economics, and suggests its utility in future outbreaks.
The Hidden Power of Normalization: Exponential Capacity Control in Deep Neural Networks
arXiv:2511.00958v1 Announce Type: cross Abstract: Normalization methods are fundamental components of modern deep neural networks (DNNs). Empirically, they are known to stabilize optimization dynamics and