Social Contacts and Transmission of COVID-19 in British Columbia, Canada

Canada's provincial COVID-19 pandemic modelling efforts: A review of mathematical models and their impacts on the responses

SETTING

Mathematical modelling played an important role in the public health response to COVID-19 in Canada. Variability in epidemic trajectories, modelling approaches, and data infrastructure across provinces provides a unique opportunity to understand the factors that shaped modelling strategies.

INTERVENTION

Provinces implemented stringent pandemic interventions to mitigate SARS-CoV-2 transmission, considering evidence from epidemic models. This study aimed to summarize provincial COVID-19 modelling efforts. We identified modelling teams working with provincial decision-makers, through referrals and membership in Canadian modelling networks. Information on models, data sources, and knowledge translation were abstracted using standardized instruments.

OUTCOMES

We obtained information from six provinces. For provinces with sustained community transmission, initial modelling efforts focused on projecting epidemic trajectories and healthcare demands, and evaluating impacts of proposed interventions. In provinces with low community transmission, models emphasized quantifying importation risks. Most of the models were compartmental and deterministic, with projection horizons of a few weeks. Models were updated regularly or replaced by new ones, adapting to changing local epidemic dynamics, pathogen characteristics, vaccines, and requests from public health. Surveillance datasets for cases, hospitalizations and deaths, and serological studies were the main data sources for model calibration. Access to data for modelling and the structure for knowledge translation differed markedly between provinces.

IMPLICATION

Provincial modelling efforts during the COVID-19 pandemic were tailored to local contexts and modulated by available resources. Strengthening Canadian modelling capacity, developing and sustaining collaborations between modellers and governments, and ensuring earlier access to linked and timely surveillance data could help improve pandemic preparedness.

Impact of COVID-19 pandemic on hepatocellular carcinoma surveillance in British Columbia, Canada: An interrupted time series study

We assessed the impact of the COVID-19 pandemic on hepatocellular carcinoma (HCC) surveillance among individuals with HCV diagnosed with cirrhosis in British Columbia (BC), Canada. We used data from the British Columbia Hepatitis Testers Cohort (BC-HTC), including all individuals in the province tested for or diagnosed with HCV from 1 January 1990 to 31 December 2015, to assess HCC surveillance. To analyse the impact of the pandemic on HCC surveillance, we used pre-policy (January 2018 to February 2020) and post-policy (March to December 2020) periods. We conducted interrupted time series (ITS) analysis using a segmented linear regression model and included first-order autocorrelation terms. From January 2018 to December 2020, 6546 HCC screenings were performed among 3429 individuals with HCV and cirrhosis. The ITS model showed an immediate decrease in HCC screenings in March and April 2020, with an overall level change of -71 screenings [95% confidence interval (CI): -105.9, -18.9]. We observed a significant decrease in HCC surveillance among study participants, regardless of HCV treatment status and age group, with the sharpest decrease among untreated HCV patients. A recovery of HCC surveillance followed this decline, reflected in an increasing trend of 7.8 screenings (95% CI: 0.6, 13.5) per month during the post-policy period. There was no level or trend change in the number of individuals diagnosed with HCC. We observed a sharp decline in HCC surveillance among people living with HCV and cirrhosis in BC following the COVID-19 pandemic control measures. HCC screening returned to pre-pandemic levels by mid-2020.

Uncertain SEIAR system dynamics modeling for improved community health management of respiratory virus diseases: A COVID-19 case study

The study investigates the significance of employing advanced systemic models in community health management, with a focus on COVID-19 as a respiratory virus. Through the development of a system dynamics model integrating an uncertain SEIAR model, our research addresses the critical issue of parameter uncertainty using Ensemble Kalman Filter (EnKF) and Metropolis-Hastings (MH) algorithms. We present a case study using real COVID-19 outbreaks in Iran, offering insights into effective outbreak control scenarios and considering the global impact of respiratory viruses. The research yields distinctive results, showcasing variable mortality rates (40,500 to 436,500) across scenarios in Iran. Model accuracy is rigorously evaluated using the Normalized Root-Mean-Square Deviation (NRMSD) for new cases, deaths, and recoveries (0.2 %, 1.2 %, and 0.6 % respectively). The outcomes not only contribute to the existing body of knowledge but also offer practical implications for healthcare policies, economic considerations, and sensitivity assessments related to respiratory diseases. This study stands out from others in its approach to modeling and addressing uncertainty within a system dynamics framework. The integration of EnKF and MH algorithms provides a nuanced understanding of parameter uncertainty, adding a layer of sophistication to the analysis. The application of the model to real-world COVID-19 outbreaks in Iran further enhances the study's relevance and applicability. In conclusion, the research introduces an uncertain SEIAR system dynamics model with unique contributions to policymaking, economic considerations, and sensitivity assessments for respiratory diseases. The outcomes and insights derived from the study not only advance our understanding of disease dynamics but also provide actionable information for effective public health management.

Association between close interpersonal contact and vaccine hesitancy: Findings from a population-based survey in Canada

Background

Vaccine hesitancy threatens efforts to bring the coronavirus disease 2019 (COVID-19) pandemic to an end. Given that social or interpersonal contact is an important driver for COVID-19 transmission, understanding the relationship between contact rates and vaccine hesitancy may help identify appropriate targets for strategic intervention. The purpose of this study was to assess the association between interpersonal contact and COVID-19 vaccine hesitancy among a sample of unvaccinated adults in the Canadian province of British Columbia (BC).

Methods

Unvaccinated individuals participating in the BC COVID-19 Population Mixing Patterns Survey (BC-Mix) were asked to indicate their level of agreement to the statement, "I plan to get the COVID-19 vaccine." Multivariable multinomial logistic regression was used to assess the association between self-reported interpersonal contact and vaccine hesitancy, adjusting for age, sex, ethnicity, educational attainment, occupation, household size and region of residence. All analyses incorporated survey sampling weights based on age, sex, geography, and ethnicity.

Results

Results were based on survey responses collected between March 8, 2021 and December 6, 2021, by a total of 4,515 adults aged 18 years and older. Overall, 56.7% of respondents reported that they were willing to get the COVID-19 vaccine, 27.0% were unwilling and 16.3% were undecided. We found a dose-response association between interpersonal contact and vaccine hesitancy. Compared to individuals in the lowest quartile (least contact), those in the fourth quartile (highest contact), third quartile and second quartile groups were more likely to be vaccine hesitant, with adjusted odd ratios (aORs) of 2.85 (95% CI: 2.02, 4.00), 1.91(95% CI: 1.38, 2.64), 1.78 (95% CI: 1.13, 2.82), respectively.

Conclusion

Study findings show that among unvaccinated people in BC, vaccine hesitancy is greater among those who have high contact rates, and hence potentially at higher risk of acquiring and transmitting infection. This may also impact future uptake of booster doses.

Cohort profile: the British Columbia COVID-19 Population Mixing Patterns Survey (BC-Mix)

PURPOSE

Several non-pharmaceutical interventions, such as physical distancing, handwashing, self-isolation, and school and business closures, were implemented in British Columbia (BC) following the first laboratory-confirmed case of COVID-19 on 26 January 2020, to minimise in-person contacts that could spread infections. The BC COVID-19 Population Mixing Patterns Survey (BC-Mix) was established as a surveillance system to measure behaviour and contact patterns in BC over time to inform the timing of the easing/re-imposition of control measures. In this paper, we describe the BC-Mix survey design and the demographic characteristics of respondents.

PARTICIPANTS

The ongoing repeated online survey was launched in September 2020. Participants are mainly recruited through social media platforms (including Instagram, Facebook, YouTube, WhatsApp). A follow-up survey is sent to participants 2-4 weeks after completing the baseline survey. Survey responses are weighted to BC's population by age, sex, geography and ethnicity to obtain generalisable estimates. Additional indices such as the Material and Social Deprivation Index, residential instability, economic dependency, and others are generated using census and location data.

FINDINGS TO DATE

As of 26 July 2021, over 61 000 baseline survey responses were received of which 41 375 were eligible for analysis. Of the eligible participants, about 60% consented to follow-up and about 27% provided their personal health numbers for linkage with healthcare databases. Approximately 83.5% of respondents were female, 58.7% were 55 years or older, 87.5% identified as white and 45.9% had at least a university degree. After weighting, approximately 50% were female, 39% were 55 years or older, 65% identified as white and 50% had at least a university degree.

FUTURE PLANS

Multiple papers describing contact patterns, physical distancing measures, regular handwashing and facemask wearing, modelling looking at impact of physical distancing measures and vaccine acceptance, hesitancy and uptake are either in progress or have been published.

Patterns of social mixing in England changed in line with restrictions during the COVID-19 pandemic (September 2020 to April 2022)

Social mixing contributes to the transmission of SARS-CoV-2. We developed a composite measure for risky social mixing, investigating changes during the pandemic and factors associated with risky mixing. Forty-five waves of online cross-sectional surveys were used (n = 78,917 responses; 14 September 2020 to 13 April 2022). We investigated socio-demographic, contextual and psychological factors associated with engaging in highest risk social mixing in England at seven timepoints. Patterns of social mixing varied over time, broadly in line with changes in restrictions. Engaging in highest risk social mixing was associated with being younger, less worried about COVID-19, perceiving a lower risk of COVID-19, perceiving COVID-19 to be a less severe illness, thinking the risks of COVID-19 were being exaggerated, not agreeing that one's personal behaviour had an impact on how COVID-19 spreads, and not agreeing that information from the UK Government about COVID-19 can be trusted. Our composite measure for risky social mixing varied in line with restrictions in place at the time of data collection, providing some validation of the measure. While messages targeting psychological factors may reduce higher risk social mixing, achieving a large change in risky social mixing in a short space of time may necessitate a reimposition of restrictions.