Interactive tool shows which parts of Wales are likely to see a rise in Covid-19 cases
Six parts of Wales are likely to see a rise in Covid-19 cases over the next month, with Caerphilly, Cardiff and Swansea seeing the greatest rise, according to a new interactive map.
The Imperial College London tool also highlights Powys, Swansea, Pembrokeshire, Merthyr Tydfil and Newport as areas that will see a smaller rise in the number of cases.
Professor Axel Gandy, from the Department of Mathematics at Imperial, said: “The model allows us to project where local hotspots of Covid-19 are likely to develop in England and Wales based on the trends that we are seeing in those areas.
“Covid-19 is, unfortunately, very much still with us, but we hope this will be a useful tool for local and national governments trying to bring hotspots under control.”
Researchers at the university said they “define a local authority to be a hotspot if weekly reported cases per 100,000 population exceed 50”.
The team used data on daily reported cases, weekly reported deaths and mathematical modelling to report the probability a local authority will become a hotspot in the following week.
The site also provides estimates for each local authority in England and Wales on whether cases are likely to be increasing or decreasing in the following week.
The predictions assume no change in current interventions – such as lockdowns, and school closures – in a local authority beyond those already taken about a week before the end of observations.
The team notes an increase in cases in a local authority can be due to an increase in testing, which the model does not currently account for.
The model also assumes all individuals within each local authority are equally likely to be infected, so demographic factors such as the age structure of the population are not considered.
Dr Swapnil Mishra, from the MRC Centre for Global Infectious Disease Analysis, said: “We provide weekly predictions of the evolution of Covid-19 at the local authority level in England and Wales.
“Our model helps to identify hotspots – probable local areas of concern. We hope that our estimates will enable swift action at the local level to control the spread of the epidemic.”