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1.
Epidemics ; 39: 100559, 2022 Apr 09.
Article in English | MEDLINE | ID: covidwho-1778118

ABSTRACT

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.

2.
CMAJ ; 194(6): E195-E204, 2022 02 14.
Article in English | MEDLINE | ID: covidwho-1686132

ABSTRACT

BACKGROUND: Understanding inequalities in SARS-CoV-2 transmission associated with the social determinants of health could help the development of effective mitigation strategies that are responsive to local transmission dynamics. This study aims to quantify social determinants of geographic concentration of SARS-CoV-2 cases across 16 census metropolitan areas (hereafter, cities) in 4 Canadian provinces, British Columbia, Manitoba, Ontario and Quebec. METHODS: We used surveillance data on confirmed SARS-CoV-2 cases and census data for social determinants at the level of the dissemination area (DA). We calculated Gini coefficients to determine the overall geographic heterogeneity of confirmed cases of SARS-CoV-2 in each city, and calculated Gini covariance coefficients to determine each city's heterogeneity by each social determinant (income, education, housing density and proportions of visible minorities, recent immigrants and essential workers). We visualized heterogeneity using Lorenz (concentration) curves. RESULTS: We observed geographic concentration of SARS-CoV-2 cases in cities, as half of the cumulative cases were concentrated in DAs containing 21%-35% of their population, with the greatest geographic heterogeneity in Ontario cities (Gini coefficients 0.32-0.47), followed by British Columbia (0.23-0.36), Manitoba (0.32) and Quebec (0.28-0.37). Cases were disproportionately concentrated in areas with lower income and educational attainment, and in areas with a higher proportion of visible minorities, recent immigrants, high-density housing and essential workers. Although a consistent feature across cities was concentration by the proportion of visible minorities, the magnitude of concentration by social determinant varied across cities. INTERPRETATION: Geographic concentration of SARS-CoV-2 cases was observed in all of the included cities, but the pattern by social determinants varied. Geographically prioritized allocation of resources and services should be tailored to the local drivers of inequalities in transmission in response to the resurgence of SARS-CoV-2.


Subject(s)
COVID-19/epidemiology , Demography/statistics & numerical data , Social Determinants of Health/statistics & numerical data , COVID-19/economics , Canada/epidemiology , Cities/epidemiology , Cross-Sectional Studies , Demography/economics , Humans , SARS-CoV-2 , Social Determinants of Health/economics , Socioeconomic Factors
3.
R Soc Open Sci ; 9(1): 211710, 2022 Jan.
Article in English | MEDLINE | ID: covidwho-1626952

ABSTRACT

Estimates of the basic reproduction number (R 0) for COVID-19 are particularly variable in the context of transmission within locations such as long-term healthcare (LTHC) facilities. We sought to characterize the heterogeneity of R 0 across known outbreaks within these facilities. We used a unique comprehensive dataset of all outbreaks that occurred within LTHC facilities in British Columbia, Canada as of 21 September 2020. We estimated R 0 in 18 LTHC outbreaks with a novel Bayesian hierarchical dynamic model of susceptible, exposed, infected and recovered individuals, incorporating heterogeneity of R 0 between facilities. We further compared these estimates to those obtained with standard methods that use the exponential growth rate and maximum likelihood. The total size of outbreaks varied dramatically, with range of attack rates 2%-86%. The Bayesian analysis provided an overall estimate of R 0 = 2.51 (90% credible interval 0.47-9.0), with individual facility estimates ranging between 0.56 and 9.17. Uncertainty in these estimates was more constrained than standard methods, particularly for smaller outbreaks informed by the population-level model. We further estimated that intervention led to 61% (52%-69%) of all potential cases being averted within the LTHC facilities, or 75% (68%-79%) when using a model with multi-level intervention effect. Understanding of transmission risks and impact of intervention are essential in planning during the ongoing global pandemic, particularly in high-risk environments such as LTHC facilities.

4.
Liver Int ; 41(12): 2849-2856, 2021 12.
Article in English | MEDLINE | ID: covidwho-1443317

ABSTRACT

BACKGROUND & AIMS: Public health measures introduced to limit transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19), also disrupted various healthcare services in many regions worldwide, including British Columbia (BC), Canada. We assessed the impact of these measures, first introduced in BC in March 2020, on hepatitis C (HCV) testing and first-time HCV-positive diagnoses within the province. METHODS: De-identified HCV testing data for BC residents were obtained from the provincial Public Health Laboratory. Weekly changes in anti-HCV, HCV RNA and genotype testing episodes and first-time HCV-positive (anti-HCV/RNA/genotype) diagnoses from January 2018 to December 2020 were assessed and associations were determined using segmented regression models examining rates before vs after calendar week 12 of 2020, when measures were introduced. RESULTS: Average weekly HCV testing and first-time HCV-positive diagnosis rates fell immediately following the imposition of public health measures by 62.3 per 100 000 population and 2.9 episodes per 1 000 000 population, respectively (P < .0001 for both), and recovered in subsequent weeks to near pre-March 2020 levels. Average weekly anti-HCV positivity rates decreased steadily pre-restrictions and this trend remained unchanged afterwards. CONCLUSIONS: Reductions in HCV testing and first-time HCV-positive diagnosis rates, key drivers of progression along the HCV care cascade, occurred following the introduction of COVID-19-related public health measures. Further assessment will be required to better understand the full impact of these service disruptions on the HCV care cascade and to inform strategies for the re-engagement of people who may have been lost to care because of these measures.


Subject(s)
COVID-19 , Hepatitis C , British Columbia/epidemiology , Hepatitis C/diagnosis , Hepatitis C/epidemiology , Humans , Interrupted Time Series Analysis , Public Health , SARS-CoV-2
5.
BMJ ; 374: n1943, 2021 08 20.
Article in English | MEDLINE | ID: covidwho-1367424

ABSTRACT

OBJECTIVE: To estimate the effectiveness of mRNA covid-19 vaccines against symptomatic infection and severe outcomes (hospital admission or death). DESIGN: Test negative design study. SETTING: Ontario, Canada between 14 December 2020 and 19 April 2021. PARTICIPANTS: 324 033 community dwelling people aged ≥16 years who had symptoms of covid-19 and were tested for SARS-CoV-2. INTERVENTIONS: BNT162b2 (Pfizer-BioNTech) or mRNA-1273 (Moderna) vaccine. MAIN OUTCOME MEASURES: Laboratory confirmed SARS-CoV-2 by reverse transcription polymerase chain reaction (RT-PCR) and hospital admissions and deaths associated with SARS-CoV-2 infection. Multivariable logistic regression was adjusted for personal and clinical characteristics associated with SARS-CoV-2 and vaccine receipt to estimate vaccine effectiveness against symptomatic infection and severe outcomes. RESULTS: Of 324 033 people with symptoms, 53 270 (16.4%) were positive for SARS-CoV-2 and 21 272 (6.6%) received at least one dose of vaccine. Among participants who tested positive, 2479 (4.7%) were admitted to hospital or died. Vaccine effectiveness against symptomatic infection observed ≥14 days after one dose was 60% (95% confidence interval 57% to 64%), increasing from 48% (41% to 54%) at 14-20 days after one dose to 71% (63% to 78%) at 35-41 days. Vaccine effectiveness observed ≥7 days after two doses was 91% (89% to 93%). Vaccine effectiveness against hospital admission or death observed ≥14 days after one dose was 70% (60% to 77%), increasing from 62% (44% to 75%) at 14-20 days to 91% (73% to 97%) at ≥35 days, whereas vaccine effectiveness observed ≥7 days after two doses was 98% (88% to 100%). For adults aged ≥70 years, vaccine effectiveness estimates were observed to be lower for intervals shortly after one dose but were comparable to those for younger people for all intervals after 28 days. After two doses, high vaccine effectiveness was observed against variants with the E484K mutation. CONCLUSIONS: Two doses of mRNA covid-19 vaccines were observed to be highly effective against symptomatic infection and severe outcomes. Vaccine effectiveness of one dose was observed to be lower, particularly for older adults shortly after the first dose.


Subject(s)
COVID-19 Nucleic Acid Testing/statistics & numerical data , COVID-19 Vaccines/therapeutic use , COVID-19/mortality , Patient Admission/statistics & numerical data , Adolescent , Adult , Aged , COVID-19/diagnosis , COVID-19/prevention & control , Female , Humans , Male , Middle Aged , Ontario/epidemiology , SARS-CoV-2 , Treatment Outcome , Young Adult
6.
Epidemics ; 35: 100453, 2021 06.
Article in English | MEDLINE | ID: covidwho-1220842

ABSTRACT

Following successful non-pharmaceutical interventions (NPI) aiming to control COVID-19, many jurisdictions reopened their economies and borders. As little immunity had developed in most populations, re-establishing higher contact carried substantial risks, and therefore many locations began to see resurgence in COVID-19 cases. We present a Bayesian method to estimate the leeway to reopen, or alternatively the strength of change required to re-establish COVID-19 control, in a range of jurisdictions experiencing different COVID-19 epidemics. We estimated the timing and strength of initial control measures such as widespread distancing and compared the leeway jurisdictions had to reopen immediately after NPI measures to later estimates of leeway. Finally, we quantified risks associated with reopening and the likely burden of new cases due to introductions from other jurisdictions. We found widely varying leeway to reopen. After initial NPI measures took effect, some jurisdictions had substantial leeway (e.g., Japan, New Zealand, Germany) with > 0.99 probability that contact rates were below 80% of the threshold for epidemic growth. Others had little leeway (e.g., the United Kingdom, Washington State) and some had none (e.g., Sweden, California). For most such regions, increases in contact rate of 1.5-2 fold would have had high (> 0.7) probability of exceeding past peak sizes. Most jurisdictions experienced June-August trajectories consistent with our projections of contact rate increases of 1-2-fold. Under such relaxation scenarios for some regions, we projected up to ∼100 additional cases if just one case were imported per week over six weeks, even between jurisdictions with comparable COVID-19 risk. We provide an R package covidseir to enable jurisdictions to estimate leeway and forecast cases under different future contact patterns. Estimates of leeway can establish a quantitative basis for decisions about reopening. We recommend a cautious approach to reopening economies and borders, coupled with strong monitoring for changes in transmission.


Subject(s)
COVID-19/prevention & control , Bayes Theorem , COVID-19/epidemiology , COVID-19/transmission , Communicable Disease Control , Forecasting , Humans , Risk , SARS-CoV-2
7.
J Med Virol ; 93(2): 1078-1098, 2021 02.
Article in English | MEDLINE | ID: covidwho-1196446

ABSTRACT

BACKGROUND: To determine the utility of admission laboratory markers in the assessment and prognostication of coronavirus disease-2019 (COVID-19), a systematic review and meta-analysis were conducted on the association between admission laboratory values in hospitalized COVID-19 patients and subsequent disease severity and mortality. MATERIAL AND METHODS: Searches were conducted in MEDLINE, Pubmed, Embase, and the WHO Global Research Database from December 1,2019 to May 1, 2020 for relevant articles. A random effects meta-analysis was used to calculate the weighted mean difference (WMD) and 95% confidence interval (95% CI) for each of 27 laboratory markers. The impact of age and sex on WMDs was estimated using meta-regression techniques for 11 markers. RESULTS: In total, 64 studies met the inclusion criteria. The most marked WMDs were for neutrophils (ANC) at 3.82 × 109 /L (2.76, 4.87), lymphocytes (ALC) at -0.34 × 109 /L (-0.45, -0.23), interleukin-6 (IL-6) at 32.59 pg/mL (23.99, 41.19), ferritin at 814.14 ng/mL (551.48, 1076.81), C-reactive protein (CRP) at 66.11 mg/L (52.16, 80.06), D-dimer at 5.74 mg/L (3.91, 7.58), LDH at 232.41 U/L (178.31, 286.52), and high sensitivity troponin I at 90.47 pg/mL (47.79, 133.14) when comparing fatal to nonfatal cases. Similar trends were observed comparing severe to non-severe groups. There were no statistically significant associations between age or sex and WMD for any of the markers included in the meta-regression. CONCLUSION: The results highlight that hyper inflammation, blunted adaptive immune response, and intravascular coagulation play key roles in the pathogenesis of COVID-19. Markers of these processes are good candidates to identify patients for early intervention and, importantly, are likely reliable regardless of age or sex in adult patients.


Subject(s)
Adaptive Immunity , COVID-19/immunology , COVID-19/mortality , Hospitalization/statistics & numerical data , Inflammation , Biomarkers/blood , C-Reactive Protein/analysis , COVID-19/physiopathology , Female , Fibrin Fibrinogen Degradation Products/analysis , Humans , Lymphocytes , Male , Neutrophils , Regression Analysis , Severity of Illness Index
8.
PLoS Comput Biol ; 16(12): e1008274, 2020 12.
Article in English | MEDLINE | ID: covidwho-1004402

ABSTRACT

Extensive non-pharmaceutical and physical distancing measures are currently the primary interventions against coronavirus disease 2019 (COVID-19) worldwide. It is therefore urgent to estimate the impact such measures are having. We introduce a Bayesian epidemiological model in which a proportion of individuals are willing and able to participate in distancing, with the timing of distancing measures informed by survey data on attitudes to distancing and COVID-19. We fit our model to reported COVID-19 cases in British Columbia (BC), Canada, and five other jurisdictions, using an observation model that accounts for both underestimation and the delay between symptom onset and reporting. We estimated the impact that physical distancing (social distancing) has had on the contact rate and examined the projected impact of relaxing distancing measures. We found that, as of April 11 2020, distancing had a strong impact in BC, consistent with declines in reported cases and in hospitalization and intensive care unit numbers; individuals practising physical distancing experienced approximately 0.22 (0.11-0.34 90% CI [credible interval]) of their normal contact rate. The threshold above which prevalence was expected to grow was 0.55. We define the "contact ratio" to be the ratio of the estimated contact rate to the threshold rate at which cases are expected to grow; we estimated this contact ratio to be 0.40 (0.19-0.60) in BC. We developed an R package 'covidseir' to make our model available, and used it to quantify the impact of distancing in five additional jurisdictions. As of May 7, 2020, we estimated that New Zealand was well below its threshold value (contact ratio of 0.22 [0.11-0.34]), New York (0.60 [0.43-0.74]), Washington (0.84 [0.79-0.90]) and Florida (0.86 [0.76-0.96]) were progressively closer to theirs yet still below, but California (1.15 [1.07-1.23]) was above its threshold overall, with cases still rising. Accordingly, we found that BC, New Zealand, and New York may have had more room to relax distancing measures than the other jurisdictions, though this would need to be done cautiously and with total case volumes in mind. Our projections indicate that intermittent distancing measures-if sufficiently strong and robustly followed-could control COVID-19 transmission. This approach provides a useful tool for jurisdictions to monitor and assess current levels of distancing relative to their threshold, which will continue to be essential through subsequent waves of this pandemic.


Subject(s)
COVID-19/prevention & control , Models, Biological , Physical Distancing , Bayes Theorem , British Columbia/epidemiology , COVID-19/epidemiology , COVID-19/transmission , Humans
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