Our particle physics and cosmology research students are using their knowledge of maths and big data in the fight against Covid-19.
They are part of a team of experts, comprised of United Nations' (UN) personnel and academics, who are working to model the spread of the virus in different scenarios.
The team's computer generated models can help predict what the best public health interventions and measures could be to limit the effects of the disease.
Covid-19 threat
Researchers have been applying their model to the Kutupalong-Batukhali Expansion Site region of the Cox's Bazar refugee operation in Bangladesh.
The site houses almost 600,000 people and the spread of Covid-19 is a real threat in such a heavily populated environment with poor sanitation.
Measures that have been used to slow the spread of the virus, like social distancing and household quarantining, are not always possible in the overcrowded conditions.
Researchers, including Durham's experts, have focused their modelling on the movement of people within the settlement to explore where and how they may interact.
Using existing data they looked to identify places where people might gather, such as water pumps and aid distribution centres.
They also looked at where people live and where they might go during the day to simulate their daily routines and interactions.
Effective interventions
This information allows them to model what might happen if no additional measures were taken against Covid-19 and how useful different interventions might be.
One of the areas modelled was the effect of using face masks in certain locations across the settlement.
As a result the team found that face masks could be an effective measure to curb the spread of the virus in these areas.
Their model is being used by experts in the settlement, technical teams at the UN and researchers to adapt other scenarios so they replicate those faced by people on the ground.
The team is also designing a tool that will allow relief organisations, decision makers and modellers to visualise multiple scenarios at the same time in order to determine what interventions could work best.
