Doxycycline post-exposure prophylaxis (doxy-PEP) represents major advancements in sexually transmitted infection prevention for men who have sex with men (MSM), yet the joint long-term population-level impact on Neisseria gonorrhoeae transmission dynamics and antimicrobial resistance (AMR) together with a moderately effective vaccine remains uncertain. Here, we developed a deterministic transmission model calibrated to empirical surveillance data from England to evaluate the 15-year epidemiological interactions of implementing doxy-PEP and vaccination at the population level. The model incorporates the tetracycline- and ceftriaxone-susceptible, tetracycline-resistant (Tet-R), ceftriaxone-resistant (Cef-R), and dual-resistant (Dual-R) strains. Our simulations suggest that the unmitigated deployment of doxy-PEP provides only modest reductions in overall gonorrhoea burden, yielding a net programmatic efficiency of 0.072 (95% CrI: 0.018 - 0.23) averted infections per enrolment over the 15-year horizon. Although doxy-PEP reduces tetracycline- and ceftriaxone-susceptible infections, it consistently selects for Tet-R lineages. Conversely, standalone vaccination yields substantially greater epidemiological benefit, averting 0.73 (95% CrI: 0.17 - 1.63) overall infections per enrolment. When deployed in tandem, the dual intervention strategy achieves the greatest overall effectiveness to 0.83 (95% CrI: 0.22 - 1.71) averted infections per enrolment while mitigating the Tet-R selection pressure observed under standalone doxy-PEP. Long-term strain dynamics were found to be governed by background frontline ceftriaxone treatment failure rates rather than intervention uptake. Under a scenario of compromised ceftriaxone efficacy (20% treatment failure), doxy-PEP is projected to favour expansion of Dual-R strain, yielding 510 (95% CrI: 38 -258600) excess Dual-R infections across the 15-year horizon (146.18% [95% CrI: 31.30 - 920.86%] cumulative increase) and triggering sustained transmission exceeding the no-intervention baseline. Conversely, vaccination standalone or combined strategies consistently suppress overall transmission. By year 15, annual Cef-R and Dual-R infections are restricted to less than or equal to 19 infections under both vaccine-inclusive approaches. These findings suggest that the public health utility of antibiotic prophylaxis is heavily contingent upon the preserved efficacy of frontline therapeutics. Our work demonstrates that single-agent prophylaxis risks localized containment of some pathogens at the cost of driving multidrug-resistant selection in others and underscores the necessity of integrating non-selective tools like vaccines to manage N. gonorrhoeae. ### Competing Interest Statement The authors have declared no competing interest. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes 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. Yes I 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). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data used to parameterize the model, run the simulations, and generate the findings are included in the Supplementary Information and are publicly available with the model code at the GitHub repository: https://github.com/killingbear999/amr_gonorrhoea. National Medical Research Council, https://ror.org/04x3cxs03, MOH-002048

Background:Highly Pathogenic Avian Influenza A (subtype H5N1) poses a threat to human health, and its pandemic potential emphasizes the need to better understand detailed reported transmission pathways to humans. Existing literature is outdated or lacks detailed, comprehensive analysis of the range of transmission routes and how the virus may enter the human body. Objective:To comprehensively map all reported H5N1 transmission pathways to humans, as well as viral entry routes. Methods:CINAHL, Embase, MEDLINE, Scopus, PubMed, grey literature, and reference lists (of included studies) were searched up to October 29th, 2025, with no language restrictions. Observational studies and grey literature reporting H5N1 transmission evidence to humans were included. Two reviewers conducted duplicate screening independently (two of three reviewers per record). One reviewer completed data extraction, which was cross-verified for accuracy by a second. Findings were summarized narratively. Results:120 sources met inclusion criteria (70 studies, 50 grey literature). Reported H5N1 transmission pathways were classified into animal-to-human (109 of 120 sources, 90.8%; including poultry-to-human in 100 sources [83.3%] and cattle-to-human in nine sources [7.5%]), environment-to-human (32 of 120 sources, 26.7%), and human-to-human (14 of 120 sources, 11.7%). Reported transmission pathways were further classified as direct or indirect contact, synthesized, and linked to suspected routes of human entry, including mucosal entry (eyes, nose, mouth), inhalation of aerosols or droplets, ingestion, and percutaneous exposure. Entry routes are biologically plausible and do not imply relative likelihood or causal attribution. Conclusions:There are multiple reported pathways of H5N1 exposure, and a single pathway may involve multiple ways to infect humans. Further research is needed to determine causal mechanisms, identify specific risk factors and measures of association, and strengthen evidence-based prevention strategies.

BACKGROUND:Socioeconomic disparities in COVID-19 outcomes have been widely documented, but evidence regarding inequities in SARS-CoV-2 infection risk remains mixed. In Canada, infection-induced seroprevalence appeared to converge across socioeconomic strata by late 2022, raising questions about whether inequities in infection risk diminished during the Omicron period. AIM:To assess whether apparent convergence in SARS-CoV-2 seroprevalence reflects true equity in infection risk or masks persistent socioeconomic disparities in force of infection. METHODS:We analysed serial cross-sectional SARS-CoV-2 seroprevalence data (anti-nucleocapsid antibodies) from Canadian Blood Services donors collected between April 2021 and April 2023 and stratified by area-level material deprivation quintile (Q1 = least deprived; Q5 = most deprived). We fitted a dynamic seronegative-seropositive model with sero-reversion to the full seroprevalence time series, estimating quintile-specific forces of infection before and after the emergence of the Omicron variant (January 2022). Models allowing differential Omicron-related amplification by material deprivation were compared using likelihood-based criteria. RESULTS:During the pre-Omicron period, the most deprived quintile (Q5) experienced a 71% higher force of infection than the least deprived (Q1; incidence rate ratio (IRR): 1.71; 95% CI: 1.60-1.83). Following Omicron emergence, force of infection rose markedly in all quintiles. Because the pre-Omicron baseline was lowest in the least-deprived quintile, the relative increase during Omicron was largest there (48.5-fold) and smallest in the most-deprived quintile (31.8-fold), compressing the relative socioeconomic gradient (Q5 vs Q1 IRR: 1.12; 95% CI: 1.11-1.14). Despite this compression in relative terms, the most deprived populations continued to experience higher absolute force of infection throughout the Omicron period. CONCLUSION:Convergence in SARS-CoV-2 seroprevalence across socioeconomic strata masked persistent inequalities in force of infection. Dynamic modelling demonstrates that apparent equity is attributable, in our modelling framework, to differential amplification of force of infection during the Omicron period rather than from elimination of underlying socioeconomic disparities.

ABSTRACT Background Case-based infectious disease surveillance is subject to ascertainment bias when testing intensity varies across time and population subgroups. We previously developed a regression-based test adjustment methodology using Standardized Testing Ratios (STRs) to correct for differential testing patterns in COVID-19 surveillance data. Wastewater-based surveillance (WWS) measures viral burden in the community independently of diagnostic testing behavior, making it a valuable external validation tool for test-adjusted case estimates. Methods We analyzed 111 weeks of paired wastewater and case surveillance data from Ontario, Canada (July 19, 2020 to August 28, 2022). Wastewater SARS-CoV-2 signals from 107 sewersheds across 34 public health units were normalized within sewersheds and aggregated using population-weighted averages. We compared wastewater correlations with crude reported and test-adjusted case counts using Spearman rank correlations, linear regression, and negative binomial distributed lag nonlinear models (DLNM), stratified by epidemic period. Results Test-adjusted cases correlated substantially more strongly with wastewater signals than crude reported cases overall (Spearman ρ = 0.849 vs. 0.679; linear R² = 0.609 vs. 0.191). The advantage of test adjustment was greatest during the Omicron wave, when population-level diagnostic testing contracted sharply following PCR eligibility restrictions (ρ = 0.924 vs. 0.604; R² = 0.815 vs. 0.470). DLNM incorporating the wastewater signal explained substantially more variance in test-adjusted than crude reported cases (McFadden pseudo-R² 0.898 vs. 0.776), despite similar lag-response structure for both outcomes. Conclusions Wastewater surveillance provides compelling independent validation of a previously described test adjustment methodology for COVID-19 case surveillance. The agreement between wastewater signals and test-adjusted cases was strongest precisely when testing scarcity was most severe, supporting the use of test adjustment to recover accurate infection dynamics from case surveillance data during periods of changing testing access and policy.

Long-standing structural inequities have shaped distinct social contexts across Toronto’s neighbourhoods. This study aims to capture how these localized contexts may be associated with diverse COVID-19 risk across the city. A spatio-temporal Localized Conditional Autoregressive Model (ST-LCAR) was used to assess associations between population factors (age, sex, income, visible minority status, and education) and COVID-19 relative risk, accounting for spatial and temporal autocorrelation and local context through spatio-temporal random effects and piecewise intercepts. This study focuses on the first four complete waves of the COVID-19 pandemic across Forward Sortation Areas (FSAs) in the City of Toronto. A 10-percentage-point increase in the proportion of residents who identify as visible minorities in an FSA was associated with a 3

BACKGROUND: In central Ontario, influenza, respiratory syncytial virus (RSV), and invasive pneumococcal disease (IPD) follow similar seasonal patterns, peaking in winter. We aimed to quantify the independent and joint impact of influenza A, influenza B, and RSV on IPD risk at the population level. METHODS: We used a 2:1 self-matched case-crossover study design to evaluate acute effects of respiratory virus activity on IPD risk. This design ensures that effects are not confounded by within-individual characteristics that remain constant over short periods of time. We included 3,892 IPD cases occurring between January 2000 and June 2009. Effects were measured using univariable and multivariable conditional logistic regression. Multivariable models included environmental covariates (e.g., temperature, absolute humidity, and UV index) and interaction terms between viruses. RESULTS: Influenza A activity and influenza B activity were both independently associated with increased IPD risk; however, co-circulation of influenza A and B reduced the impact of both viruses. RSV activity was positively associated with increased IPD risk only in the presence of increased influenza A or influenza B activity. CONCLUSIONS: To our knowledge this represents the first study to consider the impact of interactions between these viruses on IPD risk in Canada. Our findings suggest that the prevention of IPD should be considered as a potential health benefit of influenza and RSV vaccination programs.

As Canada contends with rising healthcare costs and health system strain, vaccination programs stand out as stabilizing health infrastructure. Vaccination can reduce the burden of avoidable illness and dampen volatility arising from seasonal disease surges and outbreaks. Together, these effects can preserve clinical and public health capacity, improve fiscal predictability, and support health system resilience. These system-level benefits are generally not fully captured by conventional cost-utility analyses. Drawing on Canadian evidence, we outline how vaccination programs stabilize healthcare use and spending, and how a stabilization lens complements conventional economic evaluation. Recognizing vaccines as cost-stabilizing infrastructure may influence how vaccination programs are prioritized, budgeted, and evaluated in health system planning.

Pertussis remains a major public health concern, particularly affecting young children. While most identified cases occur in this group, the burden among older children and adults who undergo less frequent testing is not well characterized. We analyzed pertussis testing and case data in the Greater Toronto Area from 1993 to 2006. We applied a meta-regression-based method for test adjustment by age and sex, estimating case counts in each demographic group as if they were tested at the same rate as the most tested group (< 1-year males). Before adjustment, incidence was highest in the < 1-year group and declined with age, with the ≥ 80-year group having an incidence rate ratio (IRR) of 0.011 (95

COVID-19 infection risk has not been evenly distributed across racial groups, with exposure being shaped by social and structural factors. The emergence of highly transmissible variants (i.e., Omicron) dramatically increased infection rates. However, it remains unclear whether racial disparities in infection risk disappeared or persisted over the course of the pandemic. To understand how SARS-CoV-2 infection risk differed between racial groups in Canada and whether those disparities changed with the Omicron variant. We analyzed cross-sectional SARS-CoV-2 seroprevalence data from the Canadian Blood Services serosurveillance program (June 2020 to April 2023) using a previously described dynamic susceptible-infection model, while accounting for seroreversion. Race-specific force of infection was estimated for the pre-Omicron and Omicron periods (with the emergence of Omicron defined as beginning December 26, 2021). Prior to Omicron, racialized individuals had a 121

The COVID-19 experience in Toronto, Canada, varied over time. However, the focus on spatial and temporal patterns of COVID-19 in the city and associated population factors has mainly been concentrated on case patterns with less exploration of testing. As testing is the first indicator of disease burden and a means to identify at-risk populations, we sought to address this research gap by exploring the spatial and temporal patterns of COVID-19 testing rates in the City of Toronto while assessing population factors associated with varied testing rate distribution. This study uses spatial-temporal Bayesian hierarchical models with conditional autoregressive priors to visually present the changing trends of COVID-19 testing rates over space and time while quantifying the potential relationship between socio-economic and sociodemographic characteristics and COVID-19 testing rates. This study focuses on the first four waves of COVID-19 using Forward Sortation Areas (FSAs) as the spatial unit of analysis. Across the first four waves of the COVID-19 pandemic, the maps generated from our Bayesian models showed heterogeneity of relative testing rates across FSA and over time. Quantitatively, a 10-percentage point increase in visible minorities in an FSA was associated with up to 8

Most spatio-temporal models identify COVID-19 sociodemographic and socioeconomic risk factors using methods that assume a single spatial dependency pattern across the city, which may not reflect reality. The purpose of this study is to apply a spatially and temporally localized Bayesian model to identify COVID-19 risk factors that account for localized context. For this study, a spatio-temporal localized Bayesian Hierarchical Model (ST-LCAR) was used to assess the relationships between population factors (age, sex, income, visible minority status, and education) and COVID-19 relative risk. The ST-LCAR model accounts for spatial and temporal autocorrelation through spatio-temporal random effects along with piecewise intercepts to capture step changes in relative risk patterns that might be reflective of underlying local contexts. This study focuses on the first four complete waves of the COVID-19 pandemic across Forward Sortation Areas (FSAs) in the City of Toronto. A 10-percentage-point increase in the proportion of residents who identify as visible minorities was associated with a 3% increase in COVID-19 relative risk; however, this association varied across different social contexts. On the other hand, a 10-percentage-point increase in the proportion of residents with post-secondary education was associated with a 22% decrease in relative risk. Beyond quantitative relationships, our model identified 3 times higher COVID-19 relative risk in the northwestern portion of the city, with patterns varying over time. The different COVID-19 patterns in the city of Toronto may have been shaped by the complex and diverse social contexts, products of ingrained systems of structural inequities that influence the living, working, and economic conditions of city residents. Public health interventions and pandemic preparedness should integrate an equity-focused lens that considers the diverse social contexts across the city and how it shapes health outcomes.

OBJECTIVES:SARS-CoV-2 infection is an established prothrombotic trigger, yet the population-level temporal relationship between circulating viral activity and pulmonary embolism (PE) remains poorly characterized. We aimed to evaluate the short-term association between respiratory viral activity and PE, accounting for specific temporal lags. STUDY DESIGN:Ecological time-series study. METHODS:We conducted a population-level time-series analysis of incident PE hospitalizations in Ontario, Canada, from 2011 to 2024. Using distributed lag non-linear models, we assessed associations between standardized weekly activity of SARS-CoV-2, influenza A/B, and respiratory syncytial virus (RSV) and PE risk over a 5-week lag. RR per SD increase in viral activity were estimated via negative binomial regression with cross-basis terms, capturing exposure-response and lag-response non-linearities, and adjusted for Fourier seasonal terms and secular trends. A single-virus model assessed SARS-CoV-2 alone, while a multi-pathogen model additionally adjusted for influenza A and RSV. RESULTS:Among 70,599 PE cases, SARS-CoV-2 activity demonstrated a significant temporal association with PE. Cumulative RR increased 12% per SD over five weeks (RR 1.12; 95% CI 1.00-1.25). The risk followed a distinct delay trajectory, with weekly cumulative RRs peaking at week 3 (RR 1.22; 95% CI 1.04-1.43). The multi-pathogen model, SARS-CoV-2 showed a higher cumulative RR of 1.26 (95% CI 1.11-1.44), with a lower, non-significant peak at week 3 (RR 1.13; 95% CI 0.96-1.32). CONCLUSIONS:Increased population-level SARS-CoV-2 activity is associated with a heightened risk of PE, peaking at approximately the third week. This delayed peak suggests a protracted thrombo-inflammatory window, likely driven by sustained endothelial injury. These findings highlight the vascular burden of COVID-19 and suggest that infection prevention measures, including vaccination, may provide significant downstream protection against thromboembolic disease.

Syphilis remains a major global public health challenge, particularly among men who have sex with men (MSM). Although penicillin is effective for treatment, primary prevention options are limited. Recent trials show that doxycycline post-exposure prophylaxis (doxy-PEP) can substantially reduce syphilis incidence, but its long-term population-level impact, effects on transmission dynamics and effective programme enrolment strategies remain unclear, especially when accounting real-world behavioural factors such as screening frequency, uptake, adherence and discontinuation. Here we develop a behavioural transmission-dynamic model calibrated using Bayesian methods with epidemiological and behavioural data from Singapore and England to characterize transmission dynamics in MSM, quantify the potential long-run public health impact, efficiency and robustness of alternative doxy-PEP programme enrolment strategies across different settings (for example, schools, sexual health clinics and risk groups) under varying behavioural patterns and epidemiological settings. Over 15 years, targeting MSM at high-risk (>5 partners per year) at diagnosis is most efficient (averting 2.50 (95% credible interval 0.68–5.94) and 4.60 (2.12–7.79) cases per enrolment in Singapore and England, respectively). Broader strategies to enrol all MSM attending sexual health clinics into a doxy-PEP programme achieved larger total reductions but with much lower efficiency and can avert as few as 0.02 (0.00–0.23) and 0.04 (0.01–0.46) cases per enrolment, respectively. Findings were robust across different behavioural and future scenarios. In summary, doxy-PEP programmes should prioritize enrolling MSM at high-risk diagnosed with syphilis, while maintaining frequent STI screening every 3–4 months, and monitoring the emergence or amplification of tetracycline-resistant Neisseria gonorrhoeae. A transmission model, integrating trial-derived prophylaxis efficacy and behavioural factors such as screening frequency and intervention uptake and adherence, evaluates different enrolment programmes for doxycycline post-exposure prophylaxis in terms of their potential effects on syphilis incidence, using data and indications from Singapore and England.

The COVID-19 pandemic placed immense strain on Canada’s healthcare system and nosocomial infections increased risk for both healthcare workers and high-risk patients. Nosocomial COVID-19 surveillance typically relies on time-based case definitions that aim to balance sensitivity against specificity, yielding inconsistent estimates of relative mortality risk. We developed a latent class analysis approach to probabilistically classify healthcare-associated infections using multiple indicator variables and evaluated mortality risk in hospitalized COVID-19 patients. We analyzed COVID-19 surveillance data from Ontario’s Case Contact and Management System and COVaxON vaccine registry (March 17, 2020 to September 4, 2022). Latent class analysis (LCA) integrated five binary indicators (nosocomial flag, outbreak linkage, and three time intervals from admission to positive test) to classify 53,191 hospitalized cases by likelihood of healthcare-acquired infection. We estimated mortality odds and survival by classification using multivariable logistic regression and time-varying Cox models, adjusting for age, comorbidities, immunocompromised status, long-term care residence, vaccination, and pandemic wave. LCA identified three classes: unlikely (n = 46,819), moderately likely (n = 2,687), and likely (n = 3,685) healthcare-acquired infection. Compared to unlikely, moderately likely cases had elevated mortality (OR: 1.26, 95

Background The requirement for critical care in even a modest fraction of SARS-CoV-2 infected individuals made ICU resources an important societal chokepoint during the recent pandemic. We developed a simple regression-based point score in 2020 based on an objective of forecasting critical care occupancy in the Canadian province of Ontario based on mean age of cases, case numbers, and testing volume. Evolution of the pandemic (variants of concern, vaccination) led us to re-assess and re-calibrate our earlier work, with inclusion of information vaccination which became widespread in 2021. Methods We obtained complete provincial SARS-CoV-2 case, testing, and vaccination data for the period from March 2020 to September 2022, with data subdivided into 6 major “waves”, following the approach applied by other Canadian investigators. Our initial model was fit only using the first two “wild type” SARS-CoV-2 waves; an updated model included wave 3 (N501Y+ variants). Our model was validated by comparing model projections to waves not used for model fitting; validation model fits were evaluated with Spearman’s rho; counterfactuals without vaccination were modeled to impute fraction of critical care admissions prevented with vaccination. Costing was based on published economic estimates. Results Our initial model (fit to waves 1 and 2) was well calibrated (rho 0.85) but predictive validity was modest (rho 0.46). Predictive validity improved in models fit to the first 3 pandemic waves without vaccination (rho 0.60) or with vaccination (rho 0.68) (P for inclusion of vaccination 0.013 by Likelihood Ratio Test). Prevented fraction of ICU admissions attributable to vaccination was 144% (22017 admissions expected vs. 9020 observed); based on published estimates of ICU admission cost for SARS-CoV-2 the 12977 admissions averted $2.9 (CDN) billion in economic costs, in contrast to the $3 billion total cost of the vaccination program. Conclusions Simple time series regression incorporating case and testing characteristics continues to be useful as a tool for forecasting critical care occupancy due to SARS-CoV-2 but early pandemic models need to be updated to capture the preventive effects of widespread vaccination. The economic benefit of vaccination for prevention of critical care resource consumption during the pandemic is substantial, achieving near cost neutrality with the province’s entire vaccination program. ### Competing Interest Statement DNF has served on advisory boards related to influenza and SARS-CoV-2 vaccines for Seqirus, Pfizer, Astrazeneca and Sanofi-Pasteur Vaccines, and has served as a legal expert on issues related to COVID-19 epidemiology for the Elementary Teachers Federation of Ontario and the Registered Nurses Association of Ontario. NJW worked for Johnson and Johnson Innovative Medicine in 2022. ART and AS were employed by the Public Health Agency of Canada when the research was conducted. The work does not represent the views of the Public Health Agency of Canada. Other authors: no competing interests. ### Funding Statement DNF received a grant from the Canadians Institutes for Health Research (2019 COVID-19 rapid researching funding OV4-170360), and was supported by a grant to the University of Toronto Institute for Pandemics from the R. Howard Webster Foundation. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Approved by the University of Toronto Research Ethics Board, Protocols #39239, #39253 and #44787. Consent to participate: not applicable. 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. Yes I 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). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Aggregate data and guidance on methods for reproduction of analyses are available from Dr. Fisman (david.fisman@utoronto.ca).

Communicable disease surveillance typically relies on case counts for estimates of risk, and counts can be strongly influenced by testing rates. In the Canadian province of Ontario, testing rates varied markedly by age, sex, geography and time over the course of the SARS-CoV-2 pandemic. We applied a standardization-based approach to test-adjustment to better understand pandemic dynamics from 2020 to 2022, and to better understand when test-adjustment is necessary for accurate estimation of risk. Case counts were adjusted for under-testing using a previously published standardization-based approach that estimates case numbers that would have been expected if the entire population was tested at the same rate as most-tested age and sex groups. After adjustment for under-testing, estimated case counts increased threefold and test-adjusted cases correlated better with SARS-CoV-2-attributed death than crude reported cases. Test-adjusted epidemic curves suggested, in contrast to reported case counts, that the first two pandemic waves were equivalent in size, and identified three distinct pandemic waves in 2022, due to the emergence of Omicron variants. Under-reporting was greatest in younger individuals, with variation explained partly by testing rates and prevalence of multigenerational households; test-adjustment resulted in little change in the epidemic curve during time periods when per capita testing rates exceeded 5.5%. We conclude that standardization-based adjustment for differential testing by age and sex results in a different understanding of the epidemiology of SARS-CoV-2 in Ontario. This methodology may offer a means of deriving adjusted estimates of infection incidence from surveillance data, accounting for fluctuations due to changing test practices.

Respiratory viruses are major contributors to population mortality, but cause-of-death coding undercounts their impact. Ecological regression models linking viral circulation to mortality fluctuations can address this limitation. To estimate the population attributable fraction (PAF) of mortality associated with influenza A and B, respiratory syncytial virus (RSV), and SARS-CoV-2 in Ontario, Canada (1993–2024). We analysed monthly all-cause mortality data with laboratory surveillance indicators for influenza A, B, RSV, and SARS-CoV-2. Negative binomial models with secular trends, Fourier seasonal terms, and population offsets were stratified into pre-pandemic (1993-February 2020) and pandemic (March 2020-March 2024) periods. PAFs were derived from counterfactual predictions setting viral coefficients to zero. Sensitivity analyses excluded seasonal terms; Wald tests compared coefficients across model specifications. Pre-pandemic, influenza A accounted for 1.8% (95% CI 1.4–2.3%) of mortality; influenza B showed no detectable impact. RSV demonstrated inverse associations in seasonally adjusted models but positive associations (PAF 1.9%, 95% CI 1.3–2.4%) without seasonal adjustment. During 2020–2024, amid elevated baseline mortality (IRR 1.050, P=0.027), SARS-CoV-2 dominated, accounting for 6.5% (95% CI 4.5–8.4%) of deaths, 3.6-times the pre-pandemic influenza A burden, despite widespread vaccination and antiviral availability. Model-estimated SARS-CoV-2 deaths (18,052) matched reported COVID-19 deaths (18,603). Meta-analyses showed substantial heterogeneity for influenza A (I²=93.7%) and RSV (I²=88.5%) across periods and modeling approaches, but minimal heterogeneity for SARS-CoV-2 (I²=2.5%). SARS-CoV-2 demonstrated 3–4-fold higher mortality burden than seasonal influenza A despite available countermeasures. Estimates for influenza A and RSV were sensitive to seasonal adjustment, highlighting the importance of modelling choices when quantifying virus-attributable mortality.

[This corrects the article DOI: 10.1371/journal.pone.0297093.].

Background:The requirement for critical care in even a modest fraction of SARS-CoV-2-infected individuals made critical care resources a key societal chokepoint during the COVID-19 pandemic. We previously developed a simple regression-based point score to forecast critical care occupancy in Ontario, Canada, using case numbers, mean age of cases, and testing volume. In this study, we aimed to validate and update this forecasting model to account for evolving population immunity, including the effects of widespread vaccination. Methods:We obtained complete provincial SARS-CoV-2 case, testing, and vaccination data from March 2020 to September 2022, subdividing the pandemic into six waves. Our initial model was fitted using data from the first two waves; an updated model included wave 3, which was dominated by N501Y+ variants. We validated the models by comparing projections to waves not used for fitting. Predictive validity was assessed using Spearman's rho. Counterfactual scenarios without vaccination were modeled to estimate vaccine-attributable reductions in critical care admissions. Findings:The initial model (waves 1-2) was well calibrated (rho = 0.85) but had modest predictive validity (rho = 0.46). Predictive validity improved with models fitted to waves 1-3, both without (rho = 0.60) and with vaccination (rho = 0.68); model fit improved significantly with vaccination (p = 0.013). Averted admissions attributable to vaccination were estimated at 144% (22,017 expected vs. 9020 observed). Interpretation:Simple regression-based forecasting tools remain valuable for predicting SARS-CoV-2 critical care occupancy. However, models developed early in the pandemic should be recalibrated to account for evolving immunity, including widespread vaccination. Funding:Canadian Institutes of Health Research (OV4-170360); R. Howard Webster Foundation (via the University of Toronto Institute for Pandemics).