Mathematical modeling of COVID-19 in British Columbia:an age-structured model with time-dependent contact rates

Following the emergence of COVID-19 at the end of 2019, several mathematical models have been developed to study the transmission dynamics of this disease. Many of these models assume homogeneous mixing in the underlying population. However, contact rates and mixing patterns can vary dramatically among individuals depending on their age and activity level. Variation in contact rates among age groups and over time can significantly impact how well a model captures observed trends. To properly model the age-dependent dynamics of COVID-19 and understand the impacts of interventions, it is essential to consider heterogeneity arising from contact rates and mixing patterns. We developed an age-structured model that incorporates time-varying contact rates and population mixing computed from the ongoing BC Mix COVID-19 survey to study transmission dynamics of COVID-19 in British Columbia (BC), Canada. Using a Bayesian inference framework, we fit four versions of our model to weekly reported cases of COVID-19 in BC, with each version allowing different assumptions of contact rates. We show that in addition to incorporating age-specific contact rates and mixing patterns, time-dependent (weekly) contact rates are needed to adequately capture the observed transmission dynamics of COVID-19. Our approach provides a framework for explicitly including empirical contact rates in a transmission model, which removes the need to otherwise model the impact of many non-pharmaceutical interventions. Further, this approach allows projection of future cases based on clear assumptions of age-specific contact rates, as opposed to less tractable assumptions regarding transmission rates.

Impact of routine asymptomatic screening on COVID-19 incidence in a highly vaccinated university population

BackgroundWith the return of in-person classes, an understanding of COVID-19 transmission in vaccinated university campuses is essential. Given the context of high anticipated vaccination rates and other measures, there are outstanding questions of the potential impact of campus-based asymptomatic screening. MethodsWe estimated the expected number of cases and hospitalizations in one semester using rates derived for British Columbia (BC), Canada up to September 15th, 2021 and age-standardizing to a University population. To estimate the expected number of secondary cases averted due to routine tests of unvaccinated individuals in a BC post-secondary institution, we used a probabilistic model based on the incidence, vaccination effectiveness, vaccination coverage and R0. We examined multiple scenarios of vaccine coverage, screening frequency, and pre-vaccination R0. ResultsFor one 12 week semester, the expected number of cases is 67 per 50,000 for 80% vaccination coverage and 37 per 50,000 for 95% vaccination coverage. Screening of the unvaccinated population averts an expected 6-16 cases per 50,000 at 80% decreasing to 1-2 averted cases per 50,000 at 95% vaccination coverage for weekly to daily screening. Further scenarios can be explored using a web-based application. InterpretationRoutine screening of unvaccinated individuals may be of limited benefit if vaccination coverage is 80% or greater within a university setting.

Social contacts and transmission of COVID-19 in British Columbia, Canada

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.

Variant abundance estimation for SARS-CoV-2 in wastewater using RNA-Seq quantification

Effectively monitoring the spread of SARS-CoV-2 variants is essential to efforts to counter the ongoing pandemic. Wastewater monitoring of SARS-CoV-2 RNA has proven an effective and efficient technique to approximate COVID-19 case rates in the population. Predicting variant abundances from wastewater, however, is technically challenging. Here we show that by sequencing SARS-CoV-2 RNA in wastewater and applying computational techniques initially used for RNA-Seq quantification, we can estimate the abundance of variants in wastewater samples. We show by sequencing samples from wastewater and clinical isolates in Connecticut U.S.A. between January and April 2021 that the temporal dynamics of variant strains broadly correspond. We further show that this technique can be used with other wastewater sequencing techniques by expanding to samples taken across the United States in a similar timeframe. We find high variability in signal among individual samples, and limited ability to detect the presence of variants with clinical frequencies <10%; nevertheless, the overall trends match what we observed from sequencing clinical samples. Thus, while clinical sequencing remains a more sensitive technique for population surveillance, wastewater sequencing can be used to monitor trends in variant prevalence in situations where clinical sequencing is unavailable or impractical.