ABSTRACT
PurposeClose-contact rates are thought to be a significant driving force behind the dynamics of transmission for many infectious respiratory diseases. Efforts to control such infections typically focus on the practice of strict contact-avoidance measures. Yet, contact rates and their relation to transmission, and the impact of control measures, are seldom quantified. Here, we quantify the response of contact rates, transmission and new cases of COVID-19 to public health contact-restriction orders, and the associations among these three variables, in the Canadian province of British Columbia (BC) and within its two most densely populated regional health authorities: Fraser Health Authority (FHA) and Vancouver Coastal Health Authority (VCHA). MethodsWe obtained time series for self-reported close-contact rates from the BC Mix COVID-19 Survey, new reported cases of COVID-19 from the BC Center for Disease Control, and transmission rates based on dynamic model fits to reported cases. Our study period was from September 13, 2020 to February 19, 2021, during which three public health contact-restriction orders were introduced (October 26, November 7 and November 19, 2020). We used segmented linear regression to quantify impacts of public health orders, Pearson correlation to assess the instantaneous relation between contact rates and transmission, and vector autoregressive modeling to study the lagged relations among the three variables. ResultsOverall, declines in contact rates and transmission occurred concurrently with the announcement of public health orders, whereas declines in new cases showed a reporting delay of roughly two weeks. The impact of the first public health order (October 26, 2020) on contact rates and transmission was more pronounced than that of the other two health orders. Contact rates and transmission on the same day were strongly correlated (correlation coefficients = 0.64, 0.53 and 0.34 for BC, FHA, and VCHA, respectively). Moreover, contact rates were a significant time-series driver of COVID-19 and explained roughly 30% and 18% of the variation in new cases and transmission, respectively. Interestingly, increases in transmission and new cases were followed by reduced rates of contact: overall, average daily cases explained about 10% of the variation in provincial contact rates. ConclusionWe show that close-contact rates were a significant driver of transmission of COVID-19 in British Columbia, Canada and that they varied in response to public health orders. Our results also suggest a possible feedback, by which contact rates respond to recent changes in reported cases. Our findings help to explain and validate the commonly assumed, but rarely measured, response of close contact rates to public health guidelines and their impact on the dynamics of infectious diseases.
