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The outbreak of the severe acute respiratory syndrome coronavirus 2 started in Wuhan, China, towards the end of 2019 and spread worldwide. The rapid spread of the disease can be attributed to many factors including its high infectiousness and the high rate of human mobility around the world. Although travel/movement restrictions and other non-pharmaceutical interventions aimed at controlling the disease spread were put in place during the early stages of the pandemic, these interventions did not stop COVID-19 spread. To better understand the impact of human mobility on the spread of COVID-19 between regions, we propose a hybrid gravity-metapopulation model of COVID-19. Our model explicitly incorporates time-dependent human mobility into the disease transmission rate, and has the potential to incorporate other factors that affect disease transmission such as facemasks, physical distancing, contact rates, etc. An important feature of this modeling framework is its ability to independently assess the contribution of each factor to disease transmission. Using a Bayesian hierarchical modeling framework, we calibrate our model to the weekly reported cases of COVID-19 in thirteen local health areas in metro Vancouver, British Columbia (BC), Canada, from July 2020 to January 2021. We consider two main scenarios in our model calibration: using a fixed distance matrix and time-dependent weekly mobility matrices. We found that the distance matrix provides a better fit to the data, whilst the mobility matrices have the ability to explain the variance in transmission between regions. This result shows that the mobility data provides more information in terms of disease transmission than the distances between the regions.
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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.
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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.
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BackgroundThis study identified factors associated with hospital admission among people with laboratory-diagnosed COVID-19 cases in British Columbia. MethodsThis study was performed using the BC COVID-19 Cohort, which integrates data on all COVID-19 cases, hospitalizations, medical visits, emergency room visits, prescription drugs, chronic conditions and deaths. The analysis included all laboratory-diagnosed COVID-19 cases in British Columbia as of January 15th, 2021. We evaluated factors associated with hospital admission using multivariable Poisson regression analysis with robust error variance. FindingsFrom 56,874 COVID-19 cases included in the analyses, 2,298 were hospitalized. Models showed significant association of the following factors with increased hospitalization risk: male sex (adjusted risk ratio (aRR)=1.27; 95%CI=1.17-1.37), older age (p-trend <0.0001 across age groups with a graded increase in hospitalization risk with increasing age [aRR 30-39 years=3.06; 95%CI=2.32-4.03, to aRR 80+years=43.68; 95%CI=33.41-57.10 compared to 20-29 years-old]), asthma (aRR=1.15; 95%CI=1.04-1.26), cancer (aRR=1.19; 95%CI=1.09-1.29), chronic kidney disease (aRR=1.32; 95%CI=1.19-1.47), diabetes (treated without insulin aRR=1.13; 95%CI=1.03-1.25, requiring insulin aRR=5.05; 95%CI=4.43-5.76), hypertension (aRR=1.19; 95%CI=1.08-1.31), injection drug use (aRR=2.51; 95%CI=2.14-2.95), intellectual and developmental disabilities (aRR=1.67; 95%CI=1.05-2.66), problematic alcohol use (aRR=1.63; 95%CI=1.43-1.85), immunosuppression (aRR=1.29; 95%CI=1.09-1.53), and schizophrenia and psychotic disorders (aRR=1.49; 95%CI=1.23-1.82). Among women of reproductive age, in addition to age and comorbidities, pregnancy (aRR=2.69; 95%CI=1.42-5.07) was associated with increased risk of hospital admission. InterpretationOlder age, male sex, substance use, intellectual and developmental disability, chronic comorbidities, and pregnancy increase the risk of COVID-19-related hospitalization. FundingBC Centre for Disease Control, Canadian Institutes of Health Research. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSFactors such as older age, social inequities and chronic health conditions have been associated to severe COVID-19 illness. Most of the evidence comes from studies that dont include all COVID-19 diagnoses in a jurisdiction), focusing on in-hospital mortality. In addition, mental illness and substance use were not evaluated in these studies. This study assessed factors associated with hospital admission among people with laboratory-diagnosed COVID-19 cases in British Columbia. Added value of this studyIn this population-based cohort study that included 56,874 laboratory-confirmed COVID-19 cases, older age, male sex, injection drug use, problematic alcohol use, intellectual and developmental disability, schizophrenia and psychotic disorders, chronic comorbidities and pregnancy were associated with the risk of hospitalization. Insulin-dependent diabetes was associated with higher risk of hospitalization, especially in the subpopulation younger than 40 years. To the best of our knowledge this is the first study reporting this finding, (insulin use and increased risk of COVID-19-related death has been described previously). Implications of all the available evidencePrioritization of vaccination in population groups with the above mentioned risk factors could reduce COVID-19 serious outcomes. The findings indicate the presence of the syndemic of substance use, mental illness and COVID-19, which deserve special public health considerations.
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IntroductionSeveral non-pharmaceutical interventions such as physical distancing, hand washing, self-isolation, and schools and business closures, were implemented in British Columbia (BC) following the first laboratory-confirmed case of coronavirus disease 2019 (COVID-19) on January 26, 2020, to minimize 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. MethodsThe 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 two to four weeks after completing the baseline survey. Survey responses are weighted to BCs population by age, sex, geography, and ethnicity to obtain generalizable estimates. Additional indices such as the material and social deprivation index, residential instability, economic dependency, and others are generated using census and location data. ResultsAs of July 26, 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 50% of respondents were female, 39% were 55 years or older, 65% identified as white and 50% had at least a university degree. ConclusionThe pandemic response is best informed by surveillance systems capable of timely assessment of behaviour patterns. BC-Mix survey respondents represent a large cohort of British Columbians providing near real-time information on behavioural and contact patterns in BC. Data from the BC-Mix survey would inform provincial COVID-19-related control measures.
