University of Limerick
A NEW paper by researchers at University of Limerick has revealed the approach behind calculating the 'R-number' in Ireland.
UL’s Professor James Gleeson and his team describe in the paper how the main model used to guide the government's response to Covid-19 works.
The reproduction 'R-number' is used for detailing the amount of Coronavirus cases on a daily basis.
The paper, published in the journal Philosophical Transactions of the Royal Society A, details the modelling of Covid-19 by the Irish Epidemiological Modelling Advisory Group (IEMAG).
Professor Gleeson is a lecturer at UL and a member of IEMAG which provides mathematical and statistical modelling advice to NPHET.
“Since we were asked to join IEMAG in March 2020, we have been developing and running models to help provide advice to NPHET,” Professor Gleeson, lead author on the paper, explained.
“The models use a combination of mathematical and statistical techniques, some of which we created specifically for the IEMAG work, to help understand the trajectory of the virus to date, and the possible future scenarios,” added Professor Gleeson.
“Susceptible people are healthy, while exposed people have had recent contact with the virus, infected people can transmit the virus to others, and removed people are no longer infectious".
“Models of SEIR type are the standard choice for COVID-19 but there are a number of scientific challenges in applying these models that required us to combine statistical and mathematical techniques based on the expertise of several people in IEMAG and indeed in the wider mathematical sciences community in Ireland,” he added.
The paper, co-authored by researchers from UL and UCD, describes a technique that enables the level of contacts to be inferred from the observed data on confirmed cases.
“The model assumes a time-varying effective contact rate or a time-varying reproduction number, to model the effect of non-pharmaceutical interventions,” Professor Gleeson said.
“There is a technical challenge in applying such models accurately to the observed data – for example the daily number of confirmed new cases, as the past history of the disease strongly affects predictions of future scenarios.
“We overcame this using an approach that inverts the SEIR equations in conjunction with statistical modelling and data analysis techniques to calibrate the model,” he added.
“We are delighted to have this work peer-reviewed and appearing in Philosophical Transactions of the Royal Society A,” said Professor Gleeson.
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