We provide an open-source model to estimate the number of secondary Covid-19 infections caused by potentially infectious students returning from university to private homes with other occupants . Using a Monte-Carlo method and data derived from UK sources, we predict that an infectious student would, on average, infect 0.94 other household members . Or, as a rule of thumb, each infected student would generate (just less than) one secondary within-household infection . The total number of secondary cases for all returning students is dependent on the virus prevalence within each student population at the time of their departure from campus back home . Although the proposed estimation method is general and robust, the results are sensitive to the input data . We provide Matlab code and a helpful online app (http: //bit.ly/Secondary_infections_app) that can be used to estimate numbers of secondary infections based on local parameter values . This can be used worldwide to support policy making.
Index: Covid-19, Monte-Carlo simulation, household risk, mathematical modelling, secondary transmissions