A student infected with COVID-19 returning home from university for Christmas would, on average, have infected just less than one other household member with the virus, according to a new model devised by mathematicians at Cardiff University and published in Health Systems.
Using a Monte-Carlo based approach and current data from the COVID-19 literature, the researchers developed an equation to predict the number of secondary household infections using variables for prevalence of the virus, the probability of secondary transmission, the number of household occupants and the total number of students returning home.
The model predicts that each infected student returning home would produce, on average, 0.94 secondary infections.
“With the potential movement of over 1 million UK students for the Christmas vacation, even a modest 1 per cent infection level (meaning 10 in 1,000 students are infected, perhaps many of them without symptoms at the time of travel) would equate to 9,400 new secondary household cases across the country,” explained Professor Paul Harper, Director of the Data Innovation Research Institute and Professor of Operational Research at Cardiff University
As the study does not consider transmission to the students’ wider home communities or include the journey home, which may give rise to a larger number of cases, particularly if public transport is taken, the true numbers are likely to be higher.
However, although the indicative levels of secondary infections are potentially very large, multiple strategies can be adopted to help reduce the number of students taking Covid-19 home, the authors say. These include strongly advising students not to mix in the days leading up to departure, implementing staggered departure times and facilitating mass testing of students before they head home.
The model was used to informed policy in relation to the two-week firebreak in Wales in October/November. The data have also been communicated to governments of England, Scotland and Northern Ireland.
The authors have made the model available to allow anyone to rerun and adapt the simulations. “The code and app are quick to run with a focus on accessibility so that a user can rapidly change the input probabilities to suit their data, thereby generating their own results based on localised parameters,” the authors say.