Since the COVID-19 pandemic, many jurisdictions have adopted wastewater-based surveillance for various pathogens. Indeed, monitoring pathogen concentration in wastewater, usually measured in RNA or DNA copies per milliliter, can efficiently assess the prevalence of infections in entire communities. However, wastewater-based surveillance does not provide a directly interpretable and actionable metric for public health. Here, we propose a statistical framework that assesses the relationship between COVID-19 hospital admissions and SARS-CoV-2 concentrations in wastewater for several large urban centres in Canada between 2021 and 2024. We also use this analysis to categorize early into an infection wave the clinical severity of future SARS-CoV-2 epidemics.

Background:The COVID-19 pandemic has stimulated the use of wastewater surveillance (WWS) in Canada. Objective:To inform continued investment, the study assessed the cost-utility of WWS, alongside conventional surveillance, compared to conventional surveillance alone, using COVID-19 in Ontario as an example. Methods:This model-based cost-utility analysis measured WWS effectiveness by increased lead time of 1-10 days for public health response using the Ontario health system perspective. The model integrated SARS-CoV-2 transmission dynamics, SARS-CoV-2 RNA concentration in the sewage system, and disease progression. The analysis considered year-round surveillance with an outbreak occurring once in a decade, assuming WWS benefits accrue only in the outbreak year. At the individual-level, a lifetime time horizon was used and future health outcomes (quality-adjusted life years [QALYs]) and cost were discounted at 1.5%. The model was informed by population-based administrative data and was calibrated to real-world Ontario surveillance data. Results:For Omicron/BA.1-like outbreaks, a WWS program with a $15 million CAD/year budget maintained over 10 years would be cost-effective at a $50,000 CAD/QALY threshold if it detects an outbreak three or more days earlier, and cost-saving if it detects an outbreak 10 or more days earlier than conventional surveillance. For a less severe outbreak with lower transmission rates, e.g., XBB-like, the WWS program would be cost-effective if at least six outbreaks occur in a decade. Conclusion:The study findings suggest that long-term investments in WWS are likely cost-effective for low-frequency but high-impact outbreaks. Maintaining WWS infrastructure will enhance Canada's emergency preparedness for emerging and re-emerging pathogens.

Wastewater-based surveillance is a valuable tool for monitoring community-level SARS-CoV-2 transmission, but in-sewer physical and biochemical processes can attenuate and distort viral signals before they reach sampling points, complicating interpretation. We developed a stochastic, mechanistic fate and transport model to quantify viral losses between shedding locations and wastewater treatment plants under dry-weather conditions in Winnipeg, Canada. We explicitly included sedimentation and resuspension of virus-associated solids, biofilm adsorption, and biodegradation. The simulation results suggest sedimentation as the dominant loss pathway, reducing viral concentrations by a mean of 11-33%, compared with 4.1-5% for biofilm adsorption and 4.6-6.5% for biodegradation. Total viral loss across the network ranged from 0% to 80%, corresponding to a population-equivalent loss per neighborhood of up to 8,000 individuals. This value represents the equivalent number of individuals whose viral signals may go undetected due to transport-related losses in the sewer network, reducing the effective population coverage of wastewater-based surveillance to approximately 80% citywide and provide an incomplete picture of the infection risk. Furthermore, the minimum detectable prevalence varied across wastewater treatment plants catchments, ranging from 0.055% to over 0.08%, highlighting spatial differences in surveillance sensitivity. By quantifying the spatial heterogeneity of in-sewer signal attenuation, this study highlights the importance of incorporating fate and transport processes when estimating process limits of detection, designing sampling strategies, and interpreting wastewater-based surveillance data. These findings provide essential guidance for improving the accuracy, sensitivity, and public health utility of wastewater surveillance.

Background The spread of SARS-CoV-2 has been studied at unprecedented levels worldwide. In jurisdictions where molecular analysis was performed on large scales, the emergence and competition of numerous SARS-CoV-2lineages have been observed in near real-time. Lineage identification, traditionally performed from clinical samples, can also be determined by sampling wastewater from sewersheds serving populations of interest. Variants of concern (VOCs) and SARS-CoV-2 lineages associated with increased transmissibility and/or severity are of particular interest. Method Here, we consider clinical and wastewater data sources to assess the emergence and spread of VOCs in Canada retrospectively. Results We show that, overall, wastewater-based VOC identification provides similar insights to the surveillance based on clinical samples. Based on clinical data, we observed synchrony in VOC introduction as well as similar emergence speeds across most Canadian provinces despite the large geographical size of the country and differences in provincial public health measures. Conclusion In particular, it took approximately four months for VOC Alpha and Delta to contribute to half of the incidence. In contrast, VOC Omicron achieved the same contribution in less than one month. This study provides significant benchmarks to enhance planning for future VOCs, and to some extent for future pandemics caused by other pathogens, by quantifying the rate of SARS-CoV-2 VOCs invasion in Canada.

The effective reproduction number, R t, is an important epidemiological metric used to assess the state of an epidemic, as well as the effectiveness of public health interventions undertaken in response. When R t is above one, it indicates that new infections are increasing, and thus the epidemic is growing, while an R t is below one indicates that new infections are decreasing, and so the epidemic is under control. There are several established software packages that are readily available to statistically estimate R t using clinical surveillance data. However, there are comparatively few accessible tools for estimating R t from pathogen wastewater concentration, a surveillance data stream that cemented its utility during the COVID-19 pandemic. We present the R package ern that aims to perform the estimation of the effective reproduction number from real-world wastewater or aggregated clinical surveillance data in a user-friendly way.

Background:The COVID-19 pandemic underlined the need for pandemic planning but also brought into focus the use of mathematical modelling to support public health decisions. The types of models needed (compartment, agent-based, importation) are described. Best practices regarding biological realism (including the need for multidisciplinary expert advisors to modellers), model complexity, consideration of uncertainty and communications to decision-makers and the public are outlined. Methods:A narrative review was developed from the experiences of COVID-19 by members of the Public Health Agency of Canada External Modelling Network for Infectious Diseases (PHAC EMN-ID), a national community of practice on mathematical modelling of infectious diseases for public health. Results:Modelling can best support pandemic preparedness in two ways: 1) by modelling to support decisions on resource needs for likely future pandemics by estimating numbers of infections, hospitalized cases and cases needing intensive care, associated with epidemics of "hypothetical-yet-plausible" pandemic pathogens in Canada; and 2) by having ready-to-go modelling methods that can be readily adapted to the features of an emerging pandemic pathogen and used for long-range forecasting of the epidemic in Canada, as well as to explore scenarios to support public health decisions on the use of interventions. Conclusion:There is a need for modelling expertise within public health organizations in Canada, linked to modellers in academia in a community of practice, within which relationships built outside of times of crisis can be applied to enhance modelling during public health emergencies. Key challenges to modelling for pandemic preparedness include the availability of linked public health, hospital and genomic data in Canada.

The search for better tools for interpreting and understanding wastewater surveillance has continued since the beginning of the coronavirus disease 2019 (COVID-19) pandemic. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has continued to mutate, thus complicating the interpretation of surveillance results. We assessed the Omicron variants (BA.1, BA.2, and BA.5) associated with wastewater-derived SARS-CoV-2 RNA trends by estimating the effective reproduction number (Reff) using an epidemic model that integrates explicitly the SARS-CoV-2 N2 gene concentration detected in wastewater through rt-qPCR quantitative analysis. The model inferred COVID-19 cases based on wastewater data and compared them with the ones reported by clinical surveillance. The variant of the SARS-CoV-2 associated with the wastewater-derived viral RNA was monitored through wastewater whole-genome sequencing. Three major waves between January and September 2022 were associated with the Omicron subvariants (BA.1, BA.2, and BA.5). This work showed that disease trends can be monitored using estimates of the effective reproduction number which is simple and easy to understand.

ObjectivesTo identify COVID-19 infectious disease models that accounted for social determinants of health (SDH).MethodsWe searched MEDLINE, EMBASE, Cochrane Library, medRxiv, and the Web of Science from December 2019 to August 2020. We included mathematical modelling studies focused on humans investigating COVID-19 impact and including at least one SDH. We abstracted study characteristics (e.g., country, model type, social determinants of health) and appraised study quality using best practices guidelines.Results83 studies were included. Most pertained to multiple countries (n = 15), the United States (n = 12), or China (n = 7). Most models were compartmental (n = 45) and agent-based (n = 7). Age was the most incorporated SDH (n = 74), followed by gender (n = 15), race/ethnicity (n = 7) and remote/rural location (n = 6). Most models reflected the dynamic nature of infectious disease spread (n = 51, 61%) but few reported on internal (n = 10, 12%) or external (n = 31, 37%) model validation.ConclusionFew models published early in the pandemic accounted for SDH other than age. Neglect of SDH in mathematical models of disease spread may result in foregone opportunities to understand differential impacts of the pandemic and to assess targeted interventions.Systematic Review Registration:[https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020207706], PROSPERO, CRD42020207706.

BACKGROUND:Infectious disease (ID) models have been the backbone of policy decisions during the COVID-19 pandemic. However, models often overlook variation in disease risk, health burden, and policy impact across social groups. Nonetheless, social determinants are becoming increasingly recognized as fundamental to the success of control strategies overall and to the mitigation of disparities. METHODS:To underscore the importance of considering social heterogeneity in epidemiological modeling, we systematically reviewed ID modeling guidelines to identify reasons and recommendations for incorporating social determinants of health into models in relation to the conceptualization, implementation, and interpretations of models. RESULTS:After identifying 1,372 citations, we found 19 guidelines, of which 14 directly referenced at least 1 social determinant. Age (n = 11), sex and gender (n = 5), and socioeconomic status (n = 5) were the most commonly discussed social determinants. Specific recommendations were identified to consider social determinants to 1) improve the predictive accuracy of models, 2) understand heterogeneity of disease burden and policy impact, 3) contextualize decision making, 4) address inequalities, and 5) assess implementation challenges. CONCLUSION:This study can support modelers and policy makers in taking into account social heterogeneity, to consider the distributional impact of infectious disease outbreaks across social groups as well as to tailor approaches to improve equitable access to prevention, diagnostics, and therapeutics. HIGHLIGHTS:Infectious disease (ID) models often overlook the role of social determinants of health (SDH) in understanding variation in disease risk, health burden, and policy impact across social groups.In this study, we systematically review ID guidelines and identify key areas to consider SDH in relation to the conceptualization, implementation, and interpretations of models.We identify specific recommendations to consider SDH to improve model accuracy, understand heterogeneity, estimate policy impact, address inequalities, and assess implementation challenges.

Molecular epidemiology is the integration of epidemiology, the study of the distribution and risk factors of disease, and molecular biology, the study of biological diversity and processes at the molecular level. In the context of viral infections, applications of molecular epidemiology predominantly focus on characterizing variation in the nucleic acid molecule(s) that comprise the virus genome. This chapter, will review the fundamental concepts and recent progress in the molecular epidemiology of viral infections. It will start with the use of genetic sequences to catalog the diversity of viruses, how we can map this diversity to the global distribution of the virus, and identify statistical associations between genetic variation and clinical variables. Next, it will explore topics on emerging viral infections, including the discovery of new viruses, and the use of probabilistic models to reconstruct the origin and spread of a virus in recent history. This chapter will examine how similar methods are used to reconstruct the transmission history of viral infections and identify risk factors associated with transmission risk. Finally, it will discuss how the analysis of viral sequences sampled from a given patient is used to study the pathogenesis and host-specific adaptation of the virus.

In Canada, the national number of hospital admissions associated with influenza and respiratory syncytial virus (RSV) is usually available about three months after admission. This delay hampers real-time analysis involving hospitalization data, like, for example, epidemic forecasting. Here, using a mixed-effects model on 15 years of data covering about 70% of the Canadian population, we show that these hospitalizations can be approximated using the number of laboratory tests positive for influenza A and RSV, a data stream publicly reported much more rapidly, about two weeks after symptoms onset.### Competing Interest StatementThe authors have declared no competing interest.### Funding StatementThis study did not receive any funding.### Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:The Health Canada-Public Health Agency of Canada Research Ethics Board considered this study to be exempt from the requirement for research ethics review pursuant to article 2.2 of the Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans.I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.YesThe laboratory test data is publicly available on the Public Health Agency of Canada website (https://www.canada.ca/en/public-health/services/surveillance/respiratory-virus-detections-canada.html) The hospital admission data can be obtained by sending a request to the Canadian Institute for Health Information (CIHI).

Wastewater-based surveillance (WBS) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) offers a complementary tool for clinical surveillance to detect and monitor coronavirus disease 2019 (COVID-19). Since both symptomatic and asymptomatic individuals infected with SARS-CoV-2 can shed the virus through the fecal route, WBS has the potential to measure community prevalence of COVID-19 without restrictions from healthcare-seeking behaviours and clinical testing capacity. During the Omicron wave, the limited capacity of clinical testing to identify COVID-19 cases in many jurisdictions highlighted the utility of WBS to estimate disease prevalence and inform public health strategies; however, there is a plethora of in-sewage, environmental and laboratory factors that can influence WBS outcomes. The implementation of WBS, therefore, requires a comprehensive framework to outline a pipeline that accounts for these complex and nuanced factors. This article reviews the framework of the national WBS conducted at the Public Health Agency of Canada to present WBS methods used in Canada to track and monitor SARS-CoV-2. In particular, we focus on five Canadian cities-Vancouver, Edmonton, Toronto, Montréal and Halifax-whose wastewater signals are analyzed by a mathematical model to provide case forecasts and reproduction number estimates. The goal of this work is to share our insights on approaches to implement WBS. Importantly, the national WBS system has implications beyond COVID-19, as a similar framework can be applied to monitor other infectious disease pathogens or antimicrobial resistance in the community.

EDITORIAL article Front. Public Health, 16 November 2023Sec. Infectious Diseases: Epidemiology and Prevention Volume 11 - 2023 | https://doi.org/10.3389/fpubh.2023.1328452

Genetic sequencing is subject to many different types of errors, but most analyses treat the resultant sequences as if they are known without error. Next generation sequencing methods rely on significantly larger numbers of reads than previous sequencing methods in exchange for a loss of accuracy in each individual read. Still, the coverage of such machines is imperfect and leaves uncertainty in many of the base calls. In this work, we demonstrate that the uncertainty in sequencing techniques will affect downstream analysis and propose a straightforward method to propagate the uncertainty. Our method (which we have dubbed Sequence Uncertainty Propagation, or SUP) uses a probabilistic matrix representation of individual sequences which incorporates base quality scores as a measure of uncertainty that naturally lead to resampling and replication as a framework for uncertainty propagation. With the matrix representation, resampling possible base calls according to quality scores provides a bootstrap- or prior distribution-like first step towards genetic analysis. Analyses based on these re-sampled sequences will include a more complete evaluation of the error involved in such analyses. We demonstrate our resampling method on SARS-CoV-2 data. The resampling procedures add a linear computational cost to the analyses, but the large impact on the variance in downstream estimates makes it clear that ignoring this uncertainty may lead to overly confident conclusions. We show that SARS-CoV-2 lineage designations via Pangolin are much less certain than the bootstrap support reported by Pangolin would imply and the clock rate estimates for SARS-CoV-2 are much more variable than reported.

Wastewater surveillance (WWS) is useful to better understand the spreading of coronavirus disease 2019 (COVID-19) in communities, which can help design and implement suitable mitigation measures. The main objective of this study was to develop the Wastewater Viral Load Risk Index (WWVLRI) for three Saskatchewan cities to offer a simple metric to interpret WWS. The index was developed by considering relationships between reproduction number, clinical data, daily per capita concentrations of virus particles in wastewater, and weekly viral load change rate. Trends of daily per capita concentrations of SARS-CoV-2 in wastewater for Saskatoon, Prince Albert, and North Battleford were similar during the pandemic, suggesting that per capita viral load can be useful to quantitatively compare wastewater signals among cities and develop an effective and comprehensible WWVLRI. The effective reproduction number (Rt) and the daily per capita efficiency adjusted viral load thresholds of 85 × 106 and 200 × 106 N2 gene counts (gc)/population day (pd) were determined. These values with rates of change were used to categorize the potential for COVID-19 outbreaks and subsequent declines. The weekly average was considered 'low risk' when the per capita viral load was 85 × 106 N2 gc/pd. A 'medium risk' occurs when the per capita copies were between 85 × 106 and 200 × 106 N2 gc/pd. with a rate of change <100 %. The start of an outbreak is indicated by a 'medium-high' risk classification when the week-over-week rate of change was >100 %, and the absolute magnitude of concentrations of viral particles was >85 × 106 N2 gc/pd. Lastly, a 'high risk' occurs when the viral load exceeds 200 × 106 N2 gc/pd. This methodology provides a valuable resource for decision-makers and health authorities, specifically given the limitation of COVID-19 surveillance based on clinical data.