The effect of control strategies that reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China

Kiesha Prem; Yang Liu; Tim Russell; Adam J Kucharski; Rosalind M Eggo; Nicholas Davies; Mark Jit; Petra Klepac

Social mixing patterns vary across locations-households, workplaces, schools, and other locations. Therefore, we use the method set out in Prem and colleagues 13 which accounts for these differences, and obtain the location-specific contact matrices C for different scenarios. In a normal setting, contacts made at all of these locations contribute to the overall mixing pattern in a population, so we sum the contacts across the different locations to obtain our baseline contact pattern in the population before the outbreak (Fig 2, Fig S1 and Fig S2) . In an outbreak setting, different intervention strategies are aimed at reducing social mixing in different contexts in order to lower the overall transmission in the population. To simulate the effects of interventions aimed at reducing social mixing, we create synthetic contact matrices for each intervention scenario from these building block matrices.