Mathematicians develop revolutionary modeling toolkit to predict local impact of COVID-19 – ScienceDaily

The study used daily COVID-19 situation reports from the local Sussex hospital and healthcare, including admissions, discharges, bed occupancy and deaths.

Throughout the pandemic, recently released modeling has been used by the local NHS and public health services to predict infection levels so that public services can plan when and how to allocate health resources – and it s ‘has been conclusively proven to be accurate. The team is now making their modeling available to other local authorities to use through the Halogen Toolkit.

Anotida Madzvamuse, professor of mathematical and computational biology at the School of Mathematical and Physical Sciences at the University of Sussex, who led the study, said:

“We undertook this study as a rapid response to the COVID-19 pandemic. Our aim was to provide support and improve the ability of local NHS and public health teams to accurately predict and forecast the impact of local epidemics to guide demand and capacity for health care, the development of public health policies and decisions.

“Working with outstanding mathematicians, Dr James Van Yperen and Dr Eduard Campillo-Funollet, we formulated an epidemiological model and deduced the model parameters by fitting the model to local data sets to allow predictions and predictions in the short and medium term of the impact of COVID -19 outbreaks.

“I am truly delighted that our modeling has been of such value to local health departments and people. The modeling approach can be used by local authorities to predict the dynamics of other conditions such as winter flu. and mental health issues. “

Professor Anjum Memon, chair of epidemiology and public health medicine at BSMS and co-author of the study, said:

“The world is on the verge of local and regional hot spots and spikes in COVID-19 infections. 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, contingency planning and supply response in drugs and oxygen, formulation, hardening or lifting of legal restrictions and implementation of preventive measures.

“The model will also serve as a great tool to monitor the situation after the COVID-19 legal restrictions are lifted in England on July 19, and during the winter months with competing respiratory infections.”

Kate Gilchrist, Public Health Intelligence Officer at Brighton & Hove City Council and co-author of the study, said:

“This unique work has demonstrated that using local data sets, model predictions and forecasts have enabled us to adequately plan for health care demand and capacity, as well as policy development and public health decisions to mitigate the impact of COVID-19 on the local population Understanding how future peaks and waves of COVID-19 could potentially affect local populations allows us to ensure that emergency measures are in place as well as timely commissioning and organization of services. “

Dr Sue Baxter, Director of Innovations and Business Partnerships at the University of Sussex, said:

“The University is delighted that this innovative modeling approach and philosophy has been translated from the mathematical drawing board into a web-based toolkit called Halogen, which can be used by NHS hospitals, local authorities and health services. public health locally and across the UK to help save lives and improve the capacity of hard-pressed public health workers. The successful commercialization of this type of innovation illustrates just one of the transformational impacts that the Fund innovation for higher education can have when applied in a targeted manner. ”

The study is published in the International Journal of Epidemiology. It was supported by the Higher Education Innovation Fund (University of Sussex); Global Challenges Research Fund (Engineering and Physical Sciences Research Council); UK and Africa Institute for Advanced Postgraduate Studies in Mathematical Sciences; Welcome confidence; Health foundation; NIHR; and Dr Perry James (Jim) Browne Research Center on Mathematics and its Applications (University of Sussex).

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Materials provided by University of Sussex. Original written by Anna Ford. Note: Content can be changed for style and length.