Heterogeneous transmission (aka superspreading) is a notable feature of the severe acute respiratory syndrome (SARS) and coronavirus disease 2019 (COVID-19) epidemics though previous efforts to estimate how estimates of heterogeneity change over time are limited. Using empirical offspring distributions generated from contact tracing data, we compared outbreaks of SARS and COVID-19 infection in Hong Kong to infer how measures of transmission heterogeneity changed over time and in response to non-pharmaceutical interventions (NPIs). We found that, surprisingly, measures of heterogeneity increased as chains of transmission were broken and the stringency of NPIs increased. This raises concerns when estimating heterogeneity from contact tracing data because estimates are potentially biased by the time and window of data collection, especially during ongoing epidemics. Time-varying or real-time estimates of transmission heterogeneity could become a complementary indicator for epidemic intelligence alongside Rt to support epidemic response.