The BloodCounts! project is gearing up towards one of the largest-scale applications yet of machine learning in medicine and healthcare.

Was the mathematical modelling projecting the course of the pandemic too pessimistic, or were the projections justified? Matt Keeling tells our colleagues from SBIDER about the COVID models that fed into public policy.

Some diseases spread far more quickly in care homes and other settings with vulnerable people. How can maths help? And what help does maths need?

Was vaccinating vulnerable people first a good choice? Hindsight allows us to assess this question.

Tom Irving tells us about providing a bridge between policy and mathematics during the pandemic, the importance of transparency, and discussing the R number at the hair dressers.

Hear from the epidemiologists who have devoted their lives to fighting the pandemic.

What can we learn from the COVID crisis about finding consensus?

The COVID-19 emergency resulted in some amazing mathematical collaborations.

The COVID-19 pandemic has amplified the differences between us. Understanding these inequalities is crucial for this and future pandemics.

Epidemiologist Matt Keeling tells us about his work on the roadmap out of lockdown, whether the models have been too pessimistic, and what it's been like producing scientific results that carry so much weight.

There have been accusations that the modelling projecting the course of the pandemic was too pessimistic. Are they justified?

Mathematicians help with clearing the massive NHS backlog for heart conditions.