A methodology for estimating SARS-CoV-2 importation risk by air travel into Canada between July and November 2021.

BACKGROUND: Estimating rates of disease importation by travellers is a key activity to assess both the risk to a country from an infectious disease emerging elsewhere in the world and the effectiveness of border measures. We describe a model used to estimate the number of travellers infected with SARS-CoV-2 into Canadian airports in 2021, and assess the impact of pre-departure testing requirements on importation risk. METHODS: A mathematical model estimated the number of essential and non-essential air travellers infected with SARS-CoV-2, with the latter requiring a negative pre-departure test result. The number of travellers arriving infected (i.e. imported cases) depended on air travel volumes, SARS-CoV-2 exposure risk in the departure country, prior infection or vaccine acquired immunity, and, for non-essential travellers, screening from pre-departure molecular testing. Importation risk was estimated weekly from July to November 2021 as the number of imported cases and percent positivity (PP; i.e. imported cases normalised by travel volume). The impact of pre-departure testing was assessed by comparing three scenarios: baseline (pre-departure testing of all non-essential travellers; most probable importation risk given the pre-departure testing requirements), counterfactual scenario 1 (no pre-departure testing of fully vaccinated non-essential travellers), and counterfactual scenario 2 (no pre-departure testing of non-essential travellers). RESULTS: In the baseline scenario, weekly imported cases and PP varied over time, ranging from 145 to 539 cases and 0.15 to 0.28%, respectively. Most cases arrived from the USA, Mexico, the United Kingdom, and France. While modelling suggested that essential travellers had a higher weekly PP (0.37 - 0.65%) than non-essential travellers (0.12 - 0.24%), they contributed fewer weekly cases (62 - 154) than non-essential travellers (84 - 398 per week) given their lower travel volume. Pre-departure testing was estimated to reduce imported cases by one third (counterfactual scenario 1) to one half (counterfactual scenario 2). CONCLUSIONS: The model results highlighted the weekly variation in importation by traveller group (e.g., reason for travel and country of departure) and enabled a framework for measuring the impact of pre-departure testing requirements. Quantifying the contributors of importation risk through mathematical simulation can support the design of appropriate public health policy on border measures.

Exploring the dynamics of the 2022 mpox outbreak in Canada.

The 2022 mpox outbreak predominantly impacted gay, bisexual, and other men who have sex with men (gbMSM). Two models were developed to support situational awareness and management decisions in Canada. A compartmental model characterized epidemic drivers at national/provincial levels, while an agent-based model (ABM) assessed municipal-level impacts of vaccination. The models were parameterized and calibrated using empirical case and vaccination data between 2022 and 2023. The compartmental model explored: (1) the epidemic trajectory through community transmission, (2) the potential for transmission among non-gbMSM, and (3) impacts of vaccination and the proportion of gbMSM contributing to disease transmission. The ABM incorporated sexual-contact data and modeled: (1) effects of vaccine uptake on disease dynamics, and (2) impacts of case importation on outbreak resurgence. The calibrated, compartmental model followed the trajectory of the epidemic, which peaked in July 2022, and died out in December 2022. Most cases occurred among gbMSM, and epidemic trajectories were not consistent with sustained transmission among non-gbMSM. The ABM suggested that unprioritized vaccination strategies could increase the outbreak size by 47%, and that consistent importation (≥5 cases per 10 000) is necessary for outbreak resurgence. These models can inform time-sensitive situational awareness and policy decisions for similar future outbreaks.

Projected effects of nonpharmaceutical public health interventions to prevent resurgence of SARS-CoV-2 transmission in Canada.

BACKGROUND: Continual efforts to eliminate community transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) will be needed to prevent additional waves of infection. We explored the impact of nonpharmaceutical interventions on projected SARS-CoV-2 transmission in Canada. METHODS: We developed an age-structured agent-based model of the Canadian population simulating the impact of current and projected levels of public health interventions on SARS-CoV-2 transmission. Interventions included case detection and isolation, contact tracing and quarantine, physical distancing and community closures, evaluated alone and in combination. RESULTS: Without any interventions, 64.6% (95% credible interval [CrI] 63.9%-65.0%) of Canadians will be infected with SARS-CoV-2 (total attack rate) and 3.6% (95% CrI 2.4%-3.8%) of those infected and symptomatic will die. If case detection and contact tracing continued at baseline levels without maintained physical distancing and reimplementation of restrictive measures, this combination brought the total attack rate to 56.1% (95% CrI 0.05%-57.1%), but it dropped to 0.4% (95% CrI 0.03%-23.5%) with enhanced case detection and contact tracing. Combining the latter scenario with maintained physical distancing reduced the total attack rate to 0.2% (95% CrI 0.03%-1.7%) and was the only scenario that consistently kept hospital and intensive care unit bed use under capacity, prevented nearly all deaths and eliminated the epidemic. Extending school closures had minimal effects but did reduce transmission in schools; however, extending closures of workplaces and mixed-age venues markedly reduced attack rates and usually or always eliminated the epidemic under any scenario. INTERPRETATION: Controlling SARS-CoV-2 transmission will depend on enhancing and maintaining interventions at both the community and individual levels. Without such interventions, a resurgent epidemic will occur, with the risk of overwhelming our health care systems.

With-in host dynamics of L. monocytogenes and thresholds for distinct infection scenarios.

The case fatality and illness rates associated with L. monocytogenes continue to pose a serious public health burden despite the significant efforts and control protocol administered by private and public sectors. Due to the advance in surveillance and improvement in detection methodology, the knowledge of sources, transmission routes, growth potential in food process units and storage, effect of pH and temperature are well understood. However, the with-in host growth and transmission mechanisms of L. monocytogenes, particularly within the human host, remain unclear, largely due to the limited access to scientific experimentation on the human population. In order to provide insight towards the human immune response to the infection caused by L. monocytogenes, we develop a with-in host mathematical model. The model explains, in terms of biological parameters, the states of asymptomatic infection, mild infection and systemic infection leading to listeriosis. The activation and proliferation of T-cells are found to be critical for the susceptibility of the infection. Utilizing stability analysis and numerical simulation, the ranges of the critical parameters relative to infection states are established. Bifurcation analysis shows the impact of the differences of these parameters on the dynamics of the model. Finally, we present model applications in regards to predicting the risk potential of listeriosis relative to the susceptible human population.

World Health Organization Estimates of the Global and Regional Disease Burden of 22 Foodborne Bacterial, Protozoal, and Viral Diseases, 2010: A Data Synthesis.

BACKGROUND: Foodborne diseases are important worldwide, resulting in considerable morbidity and mortality. To our knowledge, we present the first global and regional estimates of the disease burden of the most important foodborne bacterial, protozoal, and viral diseases. METHODS AND FINDINGS: We synthesized data on the number of foodborne illnesses, sequelae, deaths, and Disability Adjusted Life Years (DALYs), for all diseases with sufficient data to support global and regional estimates, by age and region. The data sources included varied by pathogen and included systematic reviews, cohort studies, surveillance studies and other burden of disease assessments. We sought relevant data circa 2010, and included sources from 1990-2012. The number of studies per pathogen ranged from as few as 5 studies for bacterial intoxications through to 494 studies for diarrheal pathogens. To estimate mortality for Mycobacterium bovis infections and morbidity and mortality for invasive non-typhoidal Salmonella enterica infections, we excluded cases attributed to HIV infection. We excluded stillbirths in our estimates. We estimate that the 22 diseases included in our study resulted in two billion (95% uncertainty interval [UI] 1.5-2.9 billion) cases, over one million (95% UI 0.89-1.4 million) deaths, and 78.7 million (95% UI 65.0-97.7 million) DALYs in 2010. To estimate the burden due to contaminated food, we then applied proportions of infections that were estimated to be foodborne from a global expert elicitation. Waterborne transmission of disease was not included. We estimate that 29% (95% UI 23-36%) of cases caused by diseases in our study, or 582 million (95% UI 401-922 million), were transmitted by contaminated food, resulting in 25.2 million (95% UI 17.5-37.0 million) DALYs. Norovirus was the leading cause of foodborne illness causing 125 million (95% UI 70-251 million) cases, while Campylobacter spp. caused 96 million (95% UI 52-177 million) foodborne illnesses. Of all foodborne diseases, diarrheal and invasive infections due to non-typhoidal S. enterica infections resulted in the highest burden, causing 4.07 million (95% UI 2.49-6.27 million) DALYs. Regionally, DALYs per 100,000 population were highest in the African region followed by the South East Asian region. Considerable burden of foodborne disease is borne by children less than five years of age. Major limitations of our study include data gaps, particularly in middle- and high-mortality countries, and uncertainty around the proportion of diseases that were foodborne. CONCLUSIONS: Foodborne diseases result in a large disease burden, particularly in children. Although it is known that diarrheal diseases are a major burden in children, we have demonstrated for the first time the importance of contaminated food as a cause. There is a need to focus food safety interventions on preventing foodborne diseases, particularly in low- and middle-income settings.

Effets projetés des mesures de santé publique non pharmacologiques visant à prévenir la recrudescence de la transmission du SRAS-CoV-2 au Canada.

CONTEXTE: Il faudra prendre des mesures continues contre la transmission communautaire du coronavirus du syndrome respiratoire aigu sévère 2 (SRAS-CoV-2) pour prévenir d'autres vagues d'infection. Nous avons exploré les effets des interventions non pharmacologiques sur la transmission projetée du SRAS-CoV-2 au Canada. MÉTHODES: Nous avons créé un modèle de la population canadienne à base d'agents intégrant l'âge qui simule les effets des mesures de santé publique, selon leur intensité actuelle et projetée, sur la transmission du SRAS-CoV-2. Les mesures étudiées sont le dépistage et l'isolement des cas, la recherche de contacts et la mise en quarantaine, l'éloignement sanitaire et la fermeture des espaces partagés. Nous avons évalué l'effet des mesures prises individuellement et celui des mesures combinées. RÉSULTATS: En l'absence de mesures, 64,6 % (intervalle de crédibilité [ICr] à 95 % : 63,9 %­65,0 %) des Canadiens contracteraient le SRAS-CoV-2 (taux d'attaque global), et 3,6 % (ICr à 95 % 2,4 %­3,8 %) des personnes infectées en mourraient. En poursuivant le dépistage et la recherche de contacts à la même intensité que pendant la période de référence, sans maintenir l'éloignement sanitaire ou refermer certains endroits, le pays connaîtrait un taux d'attaque global de 56,1 % (ICr à 95 % 0,05 %­57,1 %); si ces mesures étaient accrues, le taux d'attaque chuterait à 0,4 % (ICr à 95 % 0,03 %­23,5 %). En combinant ce dernier scénario et le maintien de l'éloignement sanitaire, le taux tomberait à 0,2 % (ICr à 95 % 0,03 %­1,7 %). Ce scénario est le seul qui garderait la demande en soins hospitaliers et intensifs sous la capacité, qui préviendrait presque tous les décès et qui mettrait fin à l'épidémie. La prolongation de la fermeture des écoles aurait un effet minime, mais réduirait la transmission en milieu scolaire. Par contre, la prolongation de la fermeture des lieux de travail et des lieux publics réduirait de manière marquée le taux d'attaque et mettait habituellement ou toujours fin à l'épidémie, selon les différents scénarios simulés. INTERPRÉTATION: Le contrôle de la transmission du SRAS-CoV-2 passera par l'amélioration et le maintien des mesures, tant communautaires qu'individuelles. Autrement, il y aura une recrudescence de l'épidémie, et un risque de surcharger le système de santé.

A systematic review and meta-analysis of the effects of extreme weather events and other weather-related variables on Cryptosporidium and Giardia in fresh surface waters.

Global climate change is expected to impact drinking water quality through multiple weather-related phenomena. We conducted a systematic review and meta-analysis of the relationship between various weather-related variables and the occurrence and concentration of Cryptosporidium and Giardia in fresh surface waters. We implemented a comprehensive search in four databases, screened 1,228 unique citations for relevance, extracted data from 107 relevant articles, and conducted random-effects meta-analysis on 16 key relationships. The average odds of identifying Cryptosporidium oocysts and Giardia cysts in fresh surface waters was 2.61 (95% CI = 1.63-4.21; I² = 16%) and 2.87 (95% CI = 1.76-4.67; I² = 0%) times higher, respectively, during and after extreme weather events compared to baseline conditions. Similarly, the average concentration of Cryptosporidium and Giardia identified under these conditions was also higher, by approximately 4.38 oocysts/100 L (95% CI = 2.01-9.54; I(2) = 0%) and 2.68 cysts/100 L (95% CI = 1.08-6.55; I² = 48%). Correlation relationships between other weather-related parameters and the density of these pathogens were frequently heterogeneous and indicated low to moderate effects. Meta-regression analyses identified different study-level factors that influenced the variability in these relationships. The results can be used as direct inputs for quantitative microbial risk assessment. Future research is warranted to investigate these effects and potential mitigation strategies in different settings and contexts.

Economic Cost of a Listeria monocytogenes Outbreak in Canada, 2008.

Estimates of the economic costs associated with foodborne disease are important to inform public health decision-making. In 2008, 57 cases of listeriosis and 24 deaths in Canada were linked to contaminated delicatessen meat from one meat processing plant. Costs associated with the cases (including medical costs, nonmedical costs, and productivity losses) and those incurred by the implicated plant and federal agencies responding to the outbreak were estimated to be nearly $242 million Canadian dollars (CAD, 2008). Case costs alone were estimated at approximately $2.8 million (CAD, 2008) including loss of life. This demonstrates the considerable economic burden at both the individual and population levels associated with foodborne disease and foodborne outbreaks in particular. Foodborne outbreaks due to severe pathogens, such as Listeria monocytogenes and those that result in product recalls, are typically the most costly from the individual and/or societal perspective. Additional economic estimates of foodborne disease would contribute to our understanding of the burden of foodborne disease in Canada and would support the need for ongoing prevention and control activities.

Estimates of foodborne illness-related hospitalizations and deaths in Canada for 30 specified pathogens and unspecified agents.

Foodborne illness estimates help to set food safety priorities and create public health policies. The Public Health Agency of Canada estimates that 4 million episodes of foodborne illness occur each year in Canada due to 30 known pathogens and unspecified agents. The main objective of this study was to estimate the number of domestically acquired foodborne illness-related hospitalizations and deaths. Using the estimates of foodborne illness for Canada along with data from the Canadian Hospitalization Morbidity Database (for years 2000-2010) and relevant international literature, the number of hospitalizations and deaths for 30 pathogens and unspecified agents were calculated. Analysis accounted for under-reporting and underdiagnosis. Estimates of the proportion foodborne and the proportion travel-related were incorporated for each pathogen. Monte Carlo simulations were performed to account for uncertainty generating mean estimates and 90% probability intervals. It is estimated that each year there are 4000 hospitalizations (range 3200-4800) and 105 (range 75-139) deaths associated with domestically acquired foodborne illness related to 30 known pathogens and 7600 (range 5900-9650) hospitalizations and 133 (range 77-192) deaths associated with unspecified agents, for a total estimate of 11,600 (range 9250-14,150) hospitalizations and 238 (range 155-323) deaths associated with domestically acquired foodborne illness in Canada. Key pathogens associated with these hospitalizations or deaths include norovirus, nontyphoidal Salmonella spp., Campylobacter spp., VTEC O157 and Listeria monocytogenes. This is the first time Canada has established pathogen-specific estimates of domestically acquired foodborne illness-related hospitalizations and deaths. This information illustrates the substantial burden of foodborne illness in Canada.

Systematic review and meta-analysis of the proportion of Campylobacter cases that develop chronic sequelae.

BACKGROUND: Understanding of chronic sequelae development after Campylobacter infection is limited. The objective of the study was to determine via systematic review and meta-analysis the proportion of Campylobacter cases that develop chronic sequelae. METHODS: A systematic review of English language articles published prior to July 2011 located using Pubmed, Agricola, CabDirect, and Food Safety and Technology Abstracts. Observational studies reporting the number of Campylobacter cases that developed reactive arthritis (ReA), Reiter's syndrome (RS), haemolytic uraemic syndrome (HUS), irritable bowel syndrome (IBS), inflammatory bowel disease (IBD) ,Guillain Barré syndrome (GBS) or Miller Fisher syndrome (MFS) were included. Data extraction through independent extraction of articles by four reviewers (two per article). Random effects meta-analysis was performed and heterogeneity was assessed using the I(2) value. Meta-regression was used to explore the influence of study level variables on heterogeneity. RESULTS: A total of 31 studies were identified; 20 reported on ReA, 2 reported on RS, 9 reported on IBS, 3 studies reported on IBD, 8 reported on GBS, 1 reported on MFS and 3 reported on HUS. The proportion of Campylobacter cases that developed ReA was 2.86% (95% CI 1.40% - 5.61%, I(2) = 97.7%), irritable bowel syndrome was 4.01% (95% CI 1.41% - 10.88%, I(2) = 99.2%). Guillain Barré syndrome was 0.07% (95% CI 0.03% - 0.15%, I(2) = 72.7%). CONCLUSIONS: A significant number of Campylobacter cases develop a chronic sequela. However, results should be interpreted with caution due to the high heterogeneity.

Component costs of foodborne illness: a scoping review.

BACKGROUND: Governments require high-quality scientific evidence to prioritize resource allocation and the cost-of-illness (COI) methodology is one technique used to estimate the economic burden of a disease. However, variable cost inventories make it difficult to interpret and compare costs across multiple studies. METHODS: A scoping review was conducted to identify the component costs and the respective data sources used for estimating the cost of foodborne illnesses in a population. This review was accomplished by: (1) identifying the research question and relevant literature, (2) selecting the literature, (3) charting, collating, and summarizing the results. All pertinent data were extracted at the level of detail reported in a study, and the component cost and source data were subsequently grouped into themes. RESULTS: Eighty-four studies were identified that described the cost of foodborne illness in humans. Most studies (80%) were published in the last two decades (1992-2012) in North America and Europe. The 10 most frequently estimated costs were due to illnesses caused by bacterial foodborne pathogens, with non-typhoidal Salmonella spp. being the most commonly studied. Forty studies described both individual (direct and indirect) and societal level costs. The direct individual level component costs most often included were hospital services, physician personnel, and drug costs. The most commonly reported indirect individual level component cost was productivity losses due to sick leave from work. Prior estimates published in the literature were the most commonly used source of component cost data. Data sources were not provided or specifically linked to component costs in several studies. CONCLUSIONS: The results illustrated a highly variable depth and breadth of individual and societal level component costs, and a wide range of data sources being used. This scoping review can be used as evidence that there is a lack of standardization in cost inventories in the cost of foodborne illness literature, and to promote greater transparency and detail of data source reporting. By conforming to a more standardized cost inventory, and by reporting data sources in more detail, there will be an increase in cost of foodborne illness research that can be interpreted and compared in a meaningful way.

Chronic sequelae of E. coli O157: systematic review and meta-analysis of the proportion of E. coli O157 cases that develop chronic sequelae.

OBJECTIVE: This was a systematic review and meta-analysis to determine the proportion of Escherichia coli O157 cases that develop chronic sequelae. DATA SOURCES: We conducted a systematic review of articles published prior to July 2011 in Pubmed, Agricola, CabDirect, or Food Safety and Technology Abstracts. STUDY SELECTION: Studies were selected that reported the number of E. coli O157 cases that developed reactive arthritis (ReA), hemolytic uremic syndrome (HUS), irritable bowel syndrome, inflammatory bowel disease, or Guillain Barré syndrome. METHODS: Three levels of screening and data extraction of articles were conducted using predefined data fields. Meta-analysis was performed on unique outcome measures using a random-effects model, and heterogeneity was assessed using the I² value. Meta-regression was used to explore the influence of nine study-level variables on heterogeneity. RESULTS: A total of 82 studies were identified reporting 141 different outcome measures; 81 reported on HUS and one reported on ReA. Depending on the number of cases of E. coli O157, the estimate for the proportion of E. coli O157 cases that develop HUS ranged from 17.2% in extra-small studies (<50 cases) to 4.2% in extra-large studies (>1000 cases). Heterogeneity was significantly associated with group size (p<0.0001); however, the majority of the heterogeneity was unexplained. CONCLUSIONS: High unexplained heterogeneity indicated that the study-level factors examined had a minimal influence on the variation of estimates reported.

Association between component costs, study methodologies, and foodborne illness-related factors with the cost of nontyphoidal Salmonella illness.

Nontyphoidal Salmonella spp. are one of the most common causes of bacterial foodborne illness. Variability in cost inventories and study methodologies limits the possibility of meaningfully interpreting and comparing cost-of-illness (COI) estimates, reducing their usefulness. However, little is known about the relative effect these factors have on a cost-of-illness estimate. This is important for comparing existing estimates and when designing new cost-of-illness studies. Cost-of-illness estimates, identified through a scoping review, were used to investigate the association between descriptive, component cost, methodological, and foodborne illness-related factors such as chronic sequelae and under-reporting with the cost of nontyphoidal Salmonella spp. illness. The standardized cost of nontyphoidal Salmonella spp. illness from 30 estimates reported in 29 studies ranged from $0.01568 to $41.22 United States dollars (USD)/person/year (2012). The mean cost of nontyphoidal Salmonella spp. illness was $10.37 USD/person/year (2012). The following factors were found to be significant in multiple linear regression (p≤0.05): the number of direct component cost categories included in an estimate (0-4, particularly long-term care costs) and chronic sequelae costs (inclusion/exclusion), which had positive associations with the cost of nontyphoidal Salmonella spp. illness. Factors related to study methodology were not significant. Our findings indicated that study methodology may not be as influential as other factors, such as the number of direct component cost categories included in an estimate and costs incurred due to chronic sequelae. Therefore, these may be the most important factors to consider when designing, interpreting, and comparing cost of foodborne illness studies.

Harnessing the theoretical foundations of the exponential and beta-Poisson dose-response models to quantify parameter uncertainty using Markov Chain Monte Carlo.

Dose-response models are the essential link between exposure assessment and computed risk values in quantitative microbial risk assessment, yet the uncertainty that is inherent to computed risks because the dose-response model parameters are estimated using limited epidemiological data is rarely quantified. Second-order risk characterization approaches incorporating uncertainty in dose-response model parameters can provide more complete information to decisionmakers by separating variability and uncertainty to quantify the uncertainty in computed risks. Therefore, the objective of this work is to develop procedures to sample from posterior distributions describing uncertainty in the parameters of exponential and beta-Poisson dose-response models using Bayes's theorem and Markov Chain Monte Carlo (in OpenBUGS). The theoretical origins of the beta-Poisson dose-response model are used to identify a decomposed version of the model that enables Bayesian analysis without the need to evaluate Kummer confluent hypergeometric functions. Herein, it is also established that the beta distribution in the beta-Poisson dose-response model cannot address variation among individual pathogens, criteria to validate use of the conventional approximation to the beta-Poisson model are proposed, and simple algorithms to evaluate actual beta-Poisson probabilities of infection are investigated. The developed MCMC procedures are applied to analysis of a case study data set, and it is demonstrated that an important region of the posterior distribution of the beta-Poisson dose-response model parameters is attributable to the absence of low-dose data. This region includes beta-Poisson models for which the conventional approximation is especially invalid and in which many beta distributions have an extreme shape with questionable plausibility.

Estimates of the burden of foodborne illness in Canada for 30 specified pathogens and unspecified agents, circa 2006.

Estimates of foodborne illness are important for setting food safety priorities and making public health policies. The objective of this analysis is to estimate domestically acquired, foodborne illness in Canada, while identifying data gaps and areas for further research. Estimates of illness due to 30 pathogens and unspecified agents were based on data from the 2000-2010 time period from Canadian surveillance systems, relevant international literature, and the Canadian census population for 2006. The modeling approach required accounting for under-reporting and underdiagnosis and to estimate the proportion of illness domestically acquired and through foodborne transmission. To account for uncertainty, Monte Carlo simulations were performed to generate a mean estimate and 90% credible interval. It is estimated that each year there are 1.6 million (1.2-2.0 million) and 2.4 million (1.8-3.0 million) episodes of domestically acquired foodborne illness related to 30 known pathogens and unspecified agents, respectively, for a total estimate of 4.0 million (3.1-5.0 million) episodes of domestically acquired foodborne illness in Canada. Norovirus, Clostridium perfringens, Campylobacter spp., and nontyphoidal Salmonella spp. are the leading pathogens and account for approximately 90% of the pathogen-specific total. Approximately one in eight Canadians experience an episode of domestically acquired foodborne illness each year in Canada. These estimates cannot be compared with prior crude estimates in Canada to assess illness trends as different methodologies were used.

Counterfactuals of effects of vaccination and public health measures on COVID-19 cases in Canada: What could have happened?

This study illustrates what may have happened, in terms of coronavirus disease 2019 (COVID-19) infections, hospitalizations and deaths in Canada, had public health measures not been used to control the COVID-19 epidemic, and had restrictions been lifted with low levels of vaccination, or no vaccination, of the Canadian population. The timeline of the epidemic in Canada, and the public health interventions used to control the epidemic, are reviewed. Comparisons against outcomes in other countries and counterfactual modelling illustrate the relative success of control of the epidemic in Canada. Together, these observations show that without the use of restrictive measures and without high levels of vaccination, Canada could have experienced substantially higher numbers of infections and hospitalizations and almost a million deaths.

Projections of the transmission of the Omicron variant for Toronto, Ontario, and Canada using surveillance data following recent changes in testing policies.

At the end of 2021, with the rapid escalation of COVID19 cases due to the Omicron variant, testing centers in Canada were overwhelmed. To alleviate the pressure on the PCR testing capacity, many provinces implemented new strategies that promote self testing and adjust the eligibility for PCR tests, making the count of new cases underreported. We designed a novel compartmental model which captures the new testing guidelines, social behaviours, booster vaccines campaign and features of the newest variant Omicron. To better describe the testing eligibility, we considered the population divided into high risk and non-high-risk settings. The model is calibrated using data from January 1 to February 9, 2022, on cases and severe outcomes in Canada, the province of Ontario and City of Toronto. We conduct analyses on the impact of PCR testing capacity, self testing, different levels of reopening and vaccination coverage on cases and severe outcomes. Our results show that the total number of cases in Canada, Ontario and Toronto are 2.34 (95%CI: 1.22-3.38), 2.20 (95%CI: 1.15-3.72), and 1.97(95%CI: 1.13-3.41), times larger than reported cases, respectively. The current testing strategy is efficient if partial restrictions, such as limited capacity in public spaces, are implemented. Allowing more people to have access to PCR reduces the daily cases and severe outcomes; however, if PCR test capacity is insufficient, then it is important to promote self testing. Also, we found that reopening to a pre-pandemic level will lead to a resurgence of the infections, peaking in late March or April 2022. Vaccination and adherence to isolation protocols are important supports to the testing policies to mitigate any possible spread of the virus.

A Rapid Literature Review of Multi-Criteria Decision Support Methods in the Context of One Health for All-Hazards Threat Prioritization.

Background: Multi-Criteria Decision Analysis (MCDA) is a decision support tool that can be used in public health emergency management. The use of a One Health lens in MCDA can support the prioritization of threats and interventions which cut across the human, animal, and environmental domains. Previous literature reviews have focused on creating a snapshot of MCDA methodological trends. Our study provides an update to the MCDA methods literature with key considerations from a One Health perspective and addresses the application of MCDA in an all-hazards decision-making context. Methods: We conducted a literature search on MEDLINE, EMBASE, SCOPUS, the CAB database, and a limited online gray literature search in partnership with a librarian from Health Canada. Articles were limited to those published in the year 2010 or later in a high-income setting (OECD member countries). Results: Sixty-two articles were included for synthesis. Of these articles, most were Canadian studies (20%); and prioritized health risks, threats, and interventions in the human domain (69%). Six commonly used prioritization criteria were identified: threat, health, intervention, strategic, social, and economic impact. Stakeholders were engaged in 85% of studies and commonly consisted of government groups, non-governmental groups, subject matter experts, and the public. While most articles (65%) included elements of One Health based on our definition, only 5 studies (9%) explicitly acknowledged One Health as a guiding principle for the study. Forty seven percentage of studies noted that MCDA was beneficial in supporting the decision-making process. Conclusion: Current literature on health prioritization presents some variability in the depth of integration of the One Health framework and on the use of various MCDA methodologies given prioritization objectives. Studies which applied a comprehensive One Health approach, prioritized disparate threats, or conducted cyclical prioritizations for governing bodies were broad in scope, but sparse. The results of our review indicate the need for better guidance on the integration of a One Health approach and the use of various MCDA methods given the main prioritization objectives.

Statistical framework to support the epidemiological interpretation of SARS-CoV-2 concentration in municipal wastewater.

The ribonucleic acid (RNA) of the severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) is detectable in municipal wastewater as infected individuals can shed the virus in their feces. Viral concentration in wastewater can inform the severity of the COVID-19 pandemic but observations can be noisy and sparse and hence hamper the epidemiological interpretation. Motivated by a Canadian nationwide wastewater surveillance data set, unlike previous studies, we propose a novel Bayesian statistical framework based on the theories of functional data analysis to tackle the challenges embedded in the longitudinal wastewater monitoring data. By employing this framework to analyze the large-scale data set from the nationwide wastewater surveillance program covering 15 sampling sites across Canada, we successfully detect the true trends of viral concentration out of noisy and sparsely observed viral concentrations, and accurately forecast the future trajectory of viral concentrations in wastewater. Along with the excellent performance assessment using simulated data, this study shows that the proposed novel framework is a useful statistical tool and has a significant potential in supporting the epidemiological interpretation of noisy viral concentration measurements from wastewater samples in a real-life setting.

A wastewater-based epidemic model for SARS-CoV-2 with application to three Canadian cities.

The COVID-19 pandemic has stimulated wastewater-based surveillance, allowing public health to track the epidemic by monitoring the concentration of the genetic fingerprints of SARS-CoV-2 shed in wastewater by infected individuals. Wastewater-based surveillance for COVID-19 is still in its infancy. In particular, the quantitative link between clinical cases observed through traditional surveillance and the signals from viral concentrations in wastewater is still developing and hampers interpretation of the data and actionable public-health decisions. We present a modelling framework that includes both SARS-CoV-2 transmission at the population level and the fate of SARS-CoV-2 RNA particles in the sewage system after faecal shedding by infected persons in the population. Using our mechanistic representation of the combined clinical/wastewater system, we perform exploratory simulations to quantify the effect of surveillance effectiveness, public-health interventions and vaccination on the discordance between clinical and wastewater signals. We also apply our model to surveillance data from three Canadian cities to provide wastewater-informed estimates for the actual prevalence, the effective reproduction number and incidence forecasts. We find that wastewater-based surveillance, paired with this model, can complement clinical surveillance by supporting the estimation of key epidemiological metrics and hence better triangulate the state of an epidemic using this alternative data source.