The use of data has been essential throughout the unfolding COVID-19 pandemic. We have
needed it to populate our models, inform our understanding, and shape our responses to the …

With increasing deployment of machine learning systems in various real-world tasks, there is
a greater need for accurate quantification of predictive uncertainty. While the common goal …

Among the many ways of quantifying uncertainty in a regression setting, specifying the full
quantile function is attractive, as quantiles are amenable to interpretation and evaluation. A …

Background Short-term forecasts of infectious disease can aid situational awareness and
planning for outbreak response. Here, we report on multi-model forecasts of Covid-19 in the …

In the recent COVID-19 pandemic, a wide range of epidemiological modelling approaches
were used to predict the effective reproduction number, R (t), and other COVID-19-related …

Understanding the timing of the peak of a disease outbreak forms an important part of
epidemic forecasting. In many cases, such information is essential for planning increased …

COVID-19 disease, which highly affected global life in 2020, led to a rapid scientific
response. Versatile optimization methods found their application in scientific studies related …

The COVID-19 pandemic has seen infectious disease modelling at the forefront of
government decision-making. Models have been widely used throughout the pandemic to …

Whether and how data scientists, statisticians and modellers should be accountable for the
AI systems they develop remains a controversial and highly debated topic, especially given …

Mathematical modelling has played an important role in offering informed advice during the
COVID-19 pandemic. In England, a cross government and academia collaboration …