Mathematical Sciences lecturer Dr Lloyd Chapman has been looking at COVID-19 in a new way. His recent paper entitled “Impact of vaccinations, boosters and lockdowns on COVID-19 waves in French Polynesia” focused on the virus and how to analyse large amounts of complex data effectively. Lloyd’s research aimed to tackle the issue that there was a lack of robust methods for fitting models to data on a disease like COVID-19 – with many different interventions, disease variants and data sources – and predicting how the pandemic might evolve. This meant that there was a huge amount of guesswork in deciding what approach to take, from every country, with experts in different countries using different approaches and data. This led to wide variation in what those modelling the pandemic estimated about intervention impact, and the actions subsequently taken. It also led to confusion in what approach to take and what to do for the best. Policies such as lockdowns protected large numbers of people through curbing transmission but did not necessarily consider social and mental health issues or economic factors. Lloyd’s research, in collaboration with researchers at the London School of Hygiene and Tropical Medicine, focused on using multiple different types of data. While this is more challenging to model, it gives more robust estimates and a more detailed picture of what is going on.

Lloyd said: “we still don’t have good methods to model pandemics like COVID-19, although things are improving. For my next steps, I want to develop more robust methods to get the answers to important questions, so next time we will be more prepared. Then we can predict the answers to questions such as ‘which groups of people will be most vulnerable’ and ‘which groups will be most susceptible to getting the virus’. This should then give us the opportunity to get ahead of the virus by making best use of the information available and to take a more nuanced approach to control than just using blanket measures such as lockdowns.”

During COVID-19 there was a huge amount of data, but relatively few standardised ways of using it. This research focused on French Polynesia and how the range of interventions it adopted affected factors later, such as infection rates. As French Polynesia is a self-contained area with good data sources, the team were able to put together a range of data sources and use them to discover new information. For example, putting several data sources together allowed them to understand the breakdown of immunity in the population by age, and infection and vaccination status with evidence, rather than guesswork.

Lloyd is passionate about the tools he is involved in developing being openly available across the world. Less affluent countries can put these methods to good use to help themselves in pandemic situations that might arise in the future. Sharing such knowledge with the world could be the catalyst to furthering this research. In a world where further pandemics are likely, with this research we can be ready next time to answer important questions about disease spread quickly and efficiently.