Estimating the Under-ascertainment of COVID-19 cases in Toronto, Ontario, March to May 2020.

Background: Public health surveillance data do not always capture all cases, due in part to test availability and health care seeking behaviour. Our study aimed to estimate under-ascertainment multipliers for each step in the reporting chain for COVID-19 in Toronto, Canada. Design and methods: We applied stochastic modeling to estimate these proportions for the period from March 2020 (the beginning of the pandemic) through to May 23, 2020, and for three distinct windows with different laboratory testing criteria within this period. Results: For each laboratory-confirmed symptomatic case reported to Toronto Public Health during the entire period, the estimated number of COVID-19 infections in the community was 18 (5th and 95th percentile: 12, 29). The factor most associated with under-reporting was the proportion of those who sought care that received a test. Conclusions: Public health officials should use improved estimates to better understand the burden of COVID-19 and other similar infections.

Psychology Meets Biology in COVID-19: What We Know and Why It Matters for Public Health

Psychosocial factors are related to immune, viral, and vaccination outcomes. Yet, this knowledge has been poorly represented in public health initiatives during the COVID-19 pandemic. This review provides an overview of biopsychosocial links relevant to COVID-19 outcomes by describing seminal evidence about these associations known prepandemic as well as contemporary research conducted during the pandemic. This focuses on the negative impact of the pandemic on psychosocial health and how this in turn has likely consequences for critically relevant viral and vaccination outcomes. We end by looking forward, highlighting the potential of psychosocial interventions that could be leveraged to support all people in navigating a postpandemic world and how a biopsychosocial approach to health could be incorporated into public health responses to future pandemics.

Pandemic fatigue or enduring precautionary behaviours? Canadians' long-term response to COVID-19 public health measures.

The long-term dynamics of COVID-19 disease incidence and public health measures may impact individuals' precautionary behaviours as well as support for measures. The objectives of this study were to assess longitudinal changes in precautionary behaviours and support for public health measures. Survey data were collected online from 1030 Canadians in each of 5 cycles in 2020: June 15-July 13; July 22-Aug 8; Sept 7-15; Oct 14-21; and Nov 12-17. Precautionary behaviour increased over the study period in the context of increasing disease incidence. When controlling for the stringency of public health measures and disease incidence, mixed effects logistic regression models showed these behaviours did not significantly change over time. Odds ratios for avoiding contact with family and friends ranged from 0.84 (95% CI 0.59-1.20) in September to 1.25 (95% CI 0.66-2.37) in November compared with July 2020. Odds ratios for attending an indoor gathering ranged from 0.86 (95% CI 0.62-1.20) in August to 1.71 (95% CI 0.95-3.09) in October compared with July 2020. Support for non-essential business closures increased over time with 2.33 (95% CI 1.14-4.75) times higher odds of support in November compared to July 2020. Support for school closures declined over time with lower odds of support in September (OR 0.66 [95% CI 0.45-0.96]), October (OR 0.48 [95% CI 0.26-0.87]), and November (OR 0.39 [95% CI 0.19-0.81]) compared with July 2020. In summary, respondents' behaviour mirrored government guidance between July and November 2020 and supported individual precautionary behaviour and limitations on non-essential businesses over school closures.

Modelling scenarios of the epidemic of COVID-19 in Canada.

BACKGROUND: Severe acute respiratory syndrome virus 2 (SARS-CoV-2), likely a bat-origin coronavirus, spilled over from wildlife to humans in China in late 2019, manifesting as a respiratory disease. Coronavirus disease 2019 (COVID-19) spread initially within China and then globally, resulting in a pandemic. OBJECTIVE: This article describes predictive modelling of COVID-19 in general, and efforts within the Public Health Agency of Canada to model the effects of non-pharmaceutical interventions (NPIs) on transmission of SARS-CoV-2 in the Canadian population to support public health decisions. METHODS: The broad objectives of two modelling approaches, 1) an agent-based model and 2) a deterministic compartmental model, are described and a synopsis of studies is illustrated using a model developed in Analytica 5.3 software. RESULTS: Without intervention, more than 70% of the Canadian population may become infected. Non-pharmaceutical interventions, applied with an intensity insufficient to cause the epidemic to die out, reduce the attack rate to 50% or less, and the epidemic is longer with a lower peak. If NPIs are lifted early, the epidemic may rebound, resulting in high percentages (more than 70%) of the population affected. If NPIs are applied with intensity high enough to cause the epidemic to die out, the attack rate can be reduced to between 1% and 25% of the population. CONCLUSION: Applying NPIs with intensity high enough to cause the epidemic to die out would seem to be the preferred choice. Lifting disruptive NPIs such as shut-downs must be accompanied by enhancements to other NPIs to prevent new introductions and to identify and control any new transmission chains.

Sporadic SARS-CoV-2 cases at the neighbourhood level in Toronto, Ontario, 2020: a spatial analysis of the early pandemic period.

BACKGROUND: As the largest city in Canada, Toronto has played an important role in the dynamics of SARS-CoV-2 transmission in Ontario, and the burden of disease across Toronto neighbourhoods has shown considerable heterogeneity. The purpose of this study was to investigate the spatial variation of sporadic SARS-CoV-2 cases in Toronto neighbourhoods by detecting clusters of increased risk and investigating effects of neighbourhood-level risk factors on rates. METHODS: Data on sporadic SARS-CoV-2 cases, at the neighbourhood level, for Jan. 25 to Nov. 26, 2020, were obtained from the City of Toronto COVID-19 dashboard. We used a flexibly shaped spatial scan to detect clusters of increased risk of sporadic COVID-19. We then used a generalized linear geostatistical model to investigate whether average household size, population density, dependency ratio and prevalence of low-income households were associated with sporadic SARS-CoV-2 rates. RESULTS: We identified 3 clusters of elevated risk of SARS-CoV-2 infection, with standardized morbidity ratios ranging from 1.59 to 2.43. The generalized linear geostatistical model found that average household size (relative risk [RR] 2.17, 95% confidence interval [CI] 1.80-2.61) and percentage of low-income households (RR 1.03, 95% CI 1.02-1.04) were significant predictors of sporadic SARS-CoV-2 cases at the neighbourhood level. INTERPRETATION: During the study period, 3 clusters of increased risk of sporadic SARS-CoV-2 infection were identified, and average household size and percentage of low-income households were found to be associated with sporadic SARS-CoV-2 rates at the neighbourhood level. The findings of this study can be used to target resources and create policy to address inequities that are shown through heterogeneity of SARS-CoV-2 cases at the neighbourhood level in Toronto, Ontario.

Quantifying contact patterns in response to COVID-19 public health measures in Canada.

BACKGROUND: A variety of public health measures have been implemented during the COVID-19 pandemic in Canada to reduce contact between individuals. The objective of this study was to provide empirical contact pattern data to evaluate the impact of public health measures, the degree to which social contacts rebounded to normal levels, as well as direct public health efforts toward age- and location-specific settings. METHODS: Four population-based cross-sectional surveys were administered to members of a paid panel representative of Canadian adults by age, gender, official language, and region of residence during May (Survey 1), July (Survey 2), September (Survey 3), and December (Survey 4) 2020. A total of 4981 (Survey 1), 2493 (Survey 2), 2495 (Survey 3), and 2491 (Survey 4) respondents provided information about the age and setting for each direct contact made in a 24-h period. Contact matrices were constructed and contacts for those under the age of 18 years imputed. The next generation matrix approach was used to estimate the reproduction number (Rt) for each survey. Respondents with children under 18 years estimated the number of contacts their children made in school and extracurricular settings. RESULTS: Estimated Rt values were 0.49 (95% CI: 0.29-0.69) for May, 0.48 (95% CI: 0.29-0.68) for July, 1.06 (95% CI: 0.63-1.52) for September, and 0.81 (0.47-1.17) for December. The highest proportion of reported contacts occurred within the home (51.3% in May), in 'other' locations (49.2% in July) and at work (66.3 and 65.4% in September and December). Respondents with children reported an average of 22.7 (95% CI: 21.1-24.3) (September) and 19.0 (95% CI 17.7-20.4) (December) contacts at school per day per child in attendance. CONCLUSION: The skewed distribution of reported contacts toward workplace settings in September and December combined with the number of reported school-related contacts suggest that these settings represent important opportunities for transmission emphasizing the need to support and ensure infection control procedures in both workplaces and schools.

COVID-19 Case Age Distribution: Correction for Differential Testing by Age.

BACKGROUND: Despite expected initial universal susceptibility to a novel pandemic pathogen like SARS-CoV-2, the pandemic has been characterized by higher observed incidence in older persons and lower incidence in children and adolescents. OBJECTIVE: To determine whether differential testing by age group explains observed variation in incidence. DESIGN: Population-based cohort study. SETTING: Ontario, Canada. PARTICIPANTS: Persons diagnosed with SARS-CoV-2 and those tested for SARS-CoV-2. MEASUREMENTS: Test volumes from the Ontario Laboratories Information System, number of laboratory-confirmed SARS-CoV-2 cases from the Integrated Public Health Information System, and population figures from Statistics Canada. Demographic and temporal patterns in incidence, testing rates, and test positivity were explored using negative binomial regression models and standardization. Sources of variation in standardized ratios were identified and test-adjusted standardized infection ratios (SIRs) were estimated by metaregression. RESULTS: Observed disease incidence and testing rates were highest in the oldest age group and markedly lower in those younger than 20 years; no differences in incidence were seen by sex. After adjustment for testing frequency, SIRs were lowest in children and in adults aged 70 years or older and markedly higher in adolescents and in males aged 20 to 49 years compared with the overall population. Test-adjusted SIRs were highly correlated with standardized positivity ratios (Pearson correlation coefficient, 0.87 [95% CI, 0.68 to 0.95]; P < 0.001) and provided a case identification fraction similar to that estimated with serologic testing (26.7% vs. 17.2%). LIMITATIONS: The novel methodology requires external validation. Case and testing data were not linkable at the individual level. CONCLUSION: Adjustment for testing frequency provides a different picture of SARS-CoV-2 infection risk by age, suggesting that younger males are an underrecognized group at high risk for SARS-CoV-2 infection. PRIMARY FUNDING SOURCE: Canadian Institutes of Health Research.

A sub-national real-time epidemiological and vaccination database for the COVID-19 pandemic in Canada.

The COVID-19 pandemic has demonstrated the need for real-time, open-access epidemiological information to inform public health decision-making and outbreak control efforts. In Canada, authority for healthcare delivery primarily lies at the provincial and territorial level; however, at the outset of the pandemic no definitive pan-Canadian COVID-19 datasets were available. The COVID-19 Canada Open Data Working Group was created to fill this crucial data gap. As a team of volunteer contributors, we collect daily COVID-19 data from a variety of governmental and non-governmental sources and curate a line-list of cases and mortality for all provinces and territories of Canada, including information on location, age, sex, travel history, and exposure, where available. We also curate time series of COVID-19 recoveries, testing, and vaccine doses administered and distributed. Data are recorded systematically at a fine sub-national scale, which can be used to support robust understanding of COVID-19 hotspots. We continue to maintain this dataset, and an accompanying online dashboard, to provide a reliable pan-Canadian COVID-19 resource to researchers, journalists, and the general public.

Socio-demographic disparities in knowledge, practices, and ability to comply with COVID-19 public health measures in Canada.

OBJECTIVES: The effectiveness of public health interventions for mitigation of the COVID-19 pandemic depends on individual attitudes, compliance, and the level of support available to allow for compliance with these measures. The aim of this study was to describe attitudes and behaviours towards the Canadian COVID-19 public health response, and identify risk-modifying behaviours based on socio-demographic characteristics. METHODS: A cross-sectional online survey was administered in May 2020 to members of a paid panel representative of the Canadian population by age, gender, official language, and region of residence. A total of 4981 respondents provided responses for indicators of self-reported risk perceptions, attitudes, and behaviours towards COVID-19 public health measures. RESULTS: More than 90% of respondents reported confidence in the ability to comply with a variety of public health measures. However, only 51% reported preparedness for illness in terms of expectation to work if sick or access to paid sick days. Risk perceptions, attitudes, and behaviours varied by demographic variables. Men, younger age groups, and those in the paid workforce were less likely to consider public health measures to be effective, and had less confidence in their ability to comply. Approximately 80% of respondents reported that parents provided childcare and 52% reported that parents in the workforce provided childcare while schools were closed. CONCLUSION: Policies to help address issues of public adherence include targeted messaging for men and younger age groups, social supports for those who need to self-isolate, changes in workplace policies to discourage presenteeism, and provincially co-ordinated masking and safe school policies.

RéSUMé: OBJECTIFS: L'efficacité des mesures d'intervention en santé publique pour atténuer la pandémie de COVID-19 dépend des attitudes individuelles, de la conformité, ainsi que du niveau d'aide disponible pour que les mesures soient respectées. Notre étude visait à décrire les attitudes et les comportements à l'égard de la riposte de la santé publique canadienne à la COVID-19 et à cerner les comportements modificateurs du risque d'après les caractéristiques sociodémographiques. MéTHODE: Un sondage en ligne transversal a été administré en mai 2020 aux membres d'un comité rémunéré représentatif de l'âge, du sexe, des langues officielles et des régions de résidence de la population canadienne. En tout, 4 981 personnes ont fourni des réponses à des questions indicatrices de leurs perceptions du risque, de leurs attitudes et de leurs comportements autodéclarés à l'égard des mesures de santé publique liées à la COVID-19. RéSULTATS: Plus de 90 % des répondants se sont dits certains de leur capacité de respecter de nombreuses mesures de santé publique. Par contre, 51 % seulement ont dit être préparés à respecter ces mesures s'ils attrapaient la maladie, c'est-à-dire pouvoir s'absenter du travail ou avoir droit à des congés de maladie payés. Les perceptions du risque, les attitudes et les comportements variaient selon les caractéristiques démographiques. Les hommes, les jeunes et les personnes ayant un emploi rémunéré étaient moins susceptibles de trouver les mesures de santé publique efficaces et moins sûrs de leur capacité de les respecter. Environ 80 % des répondants ont indiqué que la garde des enfants était assurée par les parents, et 52 % ont indiqué que la garde des enfants quand les écoles étaient fermées était assurée par des parents ayant un emploi. CONCLUSION: Des messages ciblant les hommes et les jeunes, des soutiens sociaux aux personnes ayant besoin de s'isoler, des changements dans les politiques en milieu de travail pour dissuader le présentéisme, ainsi que des politiques de port du masque et de sécurité à l'école coordonnées à l'échelle provinciale sont des mesures susceptibles d'atténuer les problèmes d'adhésion du public.

Modélisation mathématique de la transmission de la COVID-19 et stratégies d'atténuation des risques dans la population ontarienne au Canada.

CONTEXTE: Au Canada, on utilise des interventions d'éloignement physique pour ralentir la propagation du SRAS-CoV-2 (coronavirus du syndrome respiratoire aigu sévère 2), mais on ignore au juste quelle en sera l'efficacité. Nous avons évalué comment différentes interventions non pharmacologiques pouvaient être utilisées pour maîtriser la pandémie de COVID-19 (maladie à coronavirus 2019) et alléger le fardeau qu'elle impose au système de santé. MÉTHODES: Nous avons utilisé un modèle à compartiments structuré selon l'âge pour faire une analyse de la transmission de la COVID-19 dans la population de l'Ontario, au Canada. Nous avons comparé un scénario de référence, soit dépistage limité, isolement et quarantaine, à des scénarios incluant dépistage accru, mesures strictes d'éloignement physique, ou combinaison de dépistage accru et d'éloignement physique moins strict. Les interventions étaient appliquées soit pendant des durées fixes, soit selon un cycle dynamique en fonction de l'occupation projetée des lits dans les unités de soins intensifs (USI). Nous présentons les médianes et les intervalles de crédibilité tirés de 100 expériences répliquées par scénario sur un horizon temporel de 2 ans. RÉSULTATS: Selon le scénario de référence, nous avons estimé que 56 % (intervalle de crédibilité de 95 %, 42 %­63 %) de la population ontarienne contractait l'infection pendant l'épidémie. Au moment du sommet épidémique, nous avons projeté 107 000 (intervalle de crédibilité de 95 %, 60 760­149 000) hospitalisations (soins standards) et 55 500 (intervalle de crédibilité de 95 %, 32 700­75 200) hospitalisations dans les USI. Pour les scénarios à durée fixe, selon les projections, toutes les interventions retardaient et réduisaient la hauteur du sommet épidémique par rapport au scénario de référence, et ce sont les mesures d'éloignement physique strict qui exerçaient le plus d'effet; de même, les interventions de durée plus longue étaient plus efficaces. Selon les projections, les interventions dynamiques réduisaient la proportion de la population atteinte à la fin de la période de 2 ans et pouvaient ramener le nombre médian de cas dans les USI en deçà des estimations actuelles de leur capacité en Ontario. INTERPRÉTATION: Sans éloignement physique substantiel ou une combinaison d'éloignement physique modéré et de dépistage accru, nous projetons que les ressources des USI pourraient être insuffisantes. L'éloignement physique dynamique maintiendrait la capacité du système de santé en plus de donner un répit psychologique et économique périodique aux populations.

Derivation and Validation of Clinical Prediction Rules for COVID-19 Mortality in Ontario, Canada.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is currently causing a high-mortality global pandemic. The clinical spectrum of disease caused by this virus is broad, ranging from asymptomatic infection to organ failure and death. Risk stratification of individuals with coronavirus disease 2019 (COVID-19) is desirable for management, and prioritization for trial enrollment. We developed a prediction rule for COVID-19 mortality in a population-based cohort in Ontario, Canada. METHODS: Data from Ontario's provincial iPHIS system were extracted for the period from January 23 to May 15, 2020. Logistic regression-based prediction rules and a rule derived using a Cox proportional hazards model were developed and validated using split-halves validation. Sensitivity analyses were performed, with varying approaches to missing data. RESULTS: Of 21 922 COVID-19 cases, 1734 with complete data were included in the derivation set; 1796 were included in the validation set. Age and comorbidities (notably diabetes, renal disease, and immune compromise) were strong predictors of mortality. Four point-based prediction rules were derived (base case, smoking excluded, long-term care excluded, and Cox model-based). All displayed excellent discrimination (area under the curve for all rules > 0.92) and calibration (P > .50 by Hosmer-Lemeshow test) in the derivation set. All performed well in the validation set and were robust to varying approaches to replacement of missing variables. CONCLUSIONS: We used a public health case management data system to build and validate 4 accurate, well-calibrated, robust clinical prediction rules for COVID-19 mortality in Ontario, Canada. While these rules need external validation, they may be useful tools for management, risk stratification, and clinical trials.

Bidirectional impact of imperfect mask use on reproduction number of COVID-19: A next generation matrix approach.

The use of masks as a means of reducing transmission of COVID-19 outside healthcare settings has proved controversial. Masks are thought to have two modes of effect: they prevent infection with COVID-19 in wearers; and prevent transmission by individuals with subclinical infection. We used a simple next-generation matrix approach to estimate the conditions under which masks would reduce the reproduction number of COVID-19 under a threshold of 1. Our model takes into account the possibility of assortative mixing, where mask users interact preferentially with other mask users. We make 3 key observations: 1. Masks, even with suboptimal efficacy in both prevention of acquisition and transmission of infection, could substantially decrease the reproduction number for COVID-19 if widely used. 2. Widespread masking may be sufficient to suppress epidemics where R has been brought close to 1 via other measures (e.g., distancing). 3. "Assortment" within populations (the tendency for interactions between masked individuals to be more likely than interactions between masked and unmasked individuals) would rapidly erode the impact of masks. As such, mask uptake needs to be fairly universal to have an effect. This simple model suggests that widespread uptake of masking could be determinative in suppressing COVID-19 epidemics in regions with R(t) at or near 1.

Mathematical modelling of COVID-19 transmission and mitigation strategies in the population of Ontario, Canada.

BACKGROUND: Physical-distancing interventions are being used in Canada to slow the spread of severe acute respiratory syndrome coronavirus 2, but it is not clear how effective they will be. We evaluated how different nonpharmaceutical interventions could be used to control the coronavirus disease 2019 (COVID-19) pandemic and reduce the burden on the health care system. METHODS: We used an age-structured compartmental model of COVID-19 transmission in the population of Ontario, Canada. We compared a base case with limited testing, isolation and quarantine to scenarios with the following: enhanced case finding, restrictive physical-distancing measures, or a combination of enhanced case finding and less restrictive physical distancing. Interventions were either implemented for fixed durations or dynamically cycled on and off, based on projected occupancy of intensive care unit (ICU) beds. We present medians and credible intervals from 100 replicates per scenario using a 2-year time horizon. RESULTS: We estimated that 56% (95% credible interval 42%-63%) of the Ontario population would be infected over the course of the epidemic in the base case. At the epidemic peak, we projected 107 000 (95% credible interval 60 760-149 000) cases in hospital (non-ICU) and 55 500 (95% credible interval 32 700-75 200) cases in ICU. For fixed-duration scenarios, all interventions were projected to delay and reduce the height of the epidemic peak relative to the base case, with restrictive physical distancing estimated to have the greatest effect. Longer duration interventions were more effective. Dynamic interventions were projected to reduce the proportion of the population infected at the end of the 2-year period and could reduce the median number of cases in ICU below current estimates of Ontario's ICU capacity. INTERPRETATION: Without substantial physical distancing or a combination of moderate physical distancing with enhanced case finding, we project that ICU resources would be overwhelmed. Dynamic physical distancing could maintain health-system capacity and also allow periodic psychological and economic respite for populations.