Speaker
Description
Transmission models are used to predict the course of epidemics and inform policy makers. For respiratory diseases, many models incorporate rates of contacts within and between age groups, summarized in contact matrices. Contact rates may change due to control measures. To forecast transmission, contact matrices should reflect this impact before measures are introduced. We present a protocol to predict future contact matrices using data on time-use, contacts, and demographics, collected before an epidemic.
For each set of control measures, we identified activities on which less time will be spent. The protocol assumes that reductions in time lead to proportional reductions in numbers of contacts made during these activities. School and work time were stratified by educational level and profession based on demographics. We validated the protocol by applying it to measures against COVID-19, comparing predicted to observed matrices.
Predicted matrices agreed with observed matrices. By age, predicted contact rates matched observed rates, especially for contacts involving adults. For child-child contacts, predicted rates were lower than observed rates, with observed school contacts likely overreported. Predicted workplace contact rates matched office occupancy data.
The protocol uses pre-epidemic data, providing consistent and transparent contact matrices for transmission models. It is suited for future epidemics when data are periodically collected.
