Predicting Local COVID-19 Impact With Novel Modelling Toolkit

Understanding how future waves will impact areas can help officials implement policies to mitigate them.

A recent study conducted by investigators from the School of Mathematical and Physical Sciences at the University of Sussex discusses the development of a groundbreaking modelling toolkit that can predict the impact of COVID-19 at the local level with extreme accuracy.

Results from the study were published in the International Journal of Epidemiology.

"The world is in the cusp of experiencing local and regional hotspots and spikes of COVID-19 infections,” Anjum Memnon, a co-author on the study said. “Our epidemiological model, which is based on local data, can be used by all local authorities in the UK and other countries to inform healthcare demand and capacity, emergency planning and response to the supply of medications and oxygen, formulation, tightening or lifting of legal restrictions and implementation of preventive measures."

For the study, the investigators employed data of daily COVID-19 situation reports collected from local National Health Service (NHS) hospitals, which included the number of daily admissions, discharges and bed occupancy. COVID-19 related weekly death rates were also collected.

They then developed a novel epidemiological predictive and forecasting model based on the data that was collected and estimated the model parameters by fitting it to the data.

Findings showed that the inferred parameters were physically reasonable and matched the widely used parameter values derived from the national data sets. The model exhibited a high level of accuracy in the predictions, even when as few as 20 data points were used.

The investigators believe that the model can be utilized by officials to help guide local policy, healthcare demand and public health measures.

"This unique piece of work demonstrated that by using local datasets, model predictions and forecasting allowed us to plan adequately the healthcare demand and capacity, as well as policy-making and public health decisions to mitigate the impact of COVID-19 on the local population,” Kate Gilchrist, a co-author on the study said. “Understanding how future COVID-19 spikes and waves could possibly affect the local populations empowers us to ensure that contingency measures are in place and the timely commissioning and organization of services."