Seasonal Variation in Respiratory Syncytial Virus Epidemiology Over Nine Consecutive Years of Surveillance

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Methods An observational study using data from the World Health Organization (WHO) Global Influenza Programme, sourced from Canada, which has completed and long-term records of 5,666,687 RSV surveillance uninterruptedly from the first week of 2016 to the 15th week of 2025. Results Of the 5,666,687 tests, 245,828 were RSV-positive, yielding an overall positivity rate of 4.34%. During nine seasonal years of continuous observation, RSV-positive rate varied year-round, suggesting a feature of seasonality, meaning that RSV epidemics always occurred in winter and spring. The RSV-positive rate also varied year by year in terms of the onset week, offset week, peak week, RSV positivity rate in the peak week, and the number of epidemic weeks. The impact of the COVID-19 pandemic on RSV seasonality in Canada is significant. Most notably, the RSV prevalence in the 2020–2021 season was largely suppressed. Furthermore, in the subsequent three years, variations persisted in the onset week, offset week, peak week, RSV positivity rate in the peak week, and the number of epidemic weeks. Conclusion This report, with a sample size in the millions and covering the pre-, mid-, and post-COVID-19 pandemic periods over nine consecutive years, not only demonstrates the seasonal characteristics of RSV prevalence but also reveals the interaction between the two dominant respiratory viruses. respiratory syncytial virus epidemiology surveillance seasonality Figures Figure 1 Introduction Respiratory syncytial virus (RSV) is a leading cause of acute lower respiratory infections (ALRIs), contributing to a substantial global disease burden [ 1 , 2 ]. Infants, young children, and older adults are particularly vulnerable, with an estimated 33 million cases annually in children under five, resulting in over 3 million hospitalizations and 100,000 deaths worldwide [ 1 , 2 ]. Despite its significant impact, effective antiviral treatments remain limited, and prevention strategies primarily rely on passive immunization (e.g., palivizumab) for high-risk infants, though recent advances in maternal vaccines and monoclonal antibodies show promise [ 3 ]. A critical aspect of RSV control is understanding its seasonal epidemic patterns (i.e., seasonality), which influence the timing of interventions such as immunoprophylaxis and public health preparedness [ 4 – 8 ]. There has been an increasing amount of research focused on the seasonality of RSV epidemics [ 4 – 8 ]. In temperate regions, RSV epidemics typically peak in winter, coinciding with colder temperatures and increased indoor crowding [ 9 ]; whereas in tropical and subtropical regions, transmission occurs year-round, often with peaks during rainy seasons, though patterns are less predictable [ 5 , 9 ]. Available studies show that environmental factors such as temperature, humidity, and air pollution influence RSV spread, but the mechanisms remain debated; human behavior, such as school terms, travel, and social gatherings contribute to seasonal surges, and viral competition (interactions with other respiratory viruses, such as influenza and SARS-CoV-2) may alter RSV dynamics [ 9 – 12 ]. Increasingly clear evidence shows that the COVID-19 pandemic suppressed RSV circulation in 2020, followed by delayed or off-season outbreaks in 2021–2022 [ 6 , 7 ]. While progress has been made in describing RSV seasonality, variability during and after the COVID-19 pandemic remains poorly understood, as only certain countries have systematically recorded RSV incidence through their own surveillance systems until recent years [ 5 ]. Although the World Health Organization (WHO) RSV surveillance project has been implemented in 25 countries across all six WHO regions from 2016 to 2023 [ 13 ], we still have not seen a conclusive study focused on RSV seasonality, risk factors, spread, and evolution from this project. The progress of the RSV surveillance project depends on another dominant project, the Global Influenza Surveillance and Response System (GISRS), which might partially explain the delay in expectations [ 13 , 14 ]. On the other hand, many existing conclusions on the disease burden and seasonality of RSV are not derived from original-source, high-quality surveillance data but from the synthesis of data from scattered studies [ 1 , 2 , 5 ]. It is commendable that a few studies using the National Respiratory and Enteric Virus Surveillance System data have described RSV seasonality during the prepandemic and pandemic periods in the United States of America (USA) [ 6 , 7 ]. Although Canada and the USA are two large neighbors in North America, these two countries differ in landforms, topography, and climate. Thus, the RSV seasonality should also be differ. In this report, we are fortunate to have access to Global Influenza Programme data from the WHO [ 15 ] to examine RSV seasonality and assess how the COVID-19 pandemic altered its transmission dynamics. In this database, data from Canada has the highest quality, as it contains complete and long-term RSV surveillance records uninterruptedly from the first week of 2016 to the 15th week of 2025. By analyzing as many as 5,666,687 tests, the RSV seasonality and the impact of COVID-19 were presented. Methods Data Source The WHO Global Influenza Programme provides a global platform for reporting and analyzing influenza and RSV surveillance data [ 15 ]. The dataset is shared through FluNet and fluID by the Global Influenza Surveillance and Response System (GISRS) [ 15 ]. Data regarding ISO year, ISO week, countries and territories, the number of specimens processed, and the number of RSV-positive cases were downloaded on April 4, 2025. We did not find age aggregation in the original dataset, so we had to forgo age stratification in subsequent analyses. Although the WHO implemented an RSV surveillance project in 25 countries across all six WHO regions from 2016 to 2023 [ 13 – 15 ], it is challenging to find detailed and reliable RSV surveillance data in this publicly available dataset. We are grateful that the data quality from Canada in this database is very high, with the following advantages: 1) it has long-term data records spanning from the first week of 2016 until the data was downloaded in 2025 (15th ISO week); 2) the data covers seasons before, during, and after the COVID-19 pandemic; 3) a sample size of 5,666,687 ensures accurate analysis. Thus, this study focused on Canadian data to identify the epidemiological characteristics of RSV over nine consecutive years. According to the records and definitions of the WHO, the original data source consisted of three parts: non-sentinel: data obtained from non-sentinel systems; data reported in this category may include outbreak investigations, universal testing, testing at the point of care, or other systems apart from sentinel surveillance; sentinel: data obtained from sentinel surveillance, which collects high-quality data systematically and routinely from sentinel surveillance sites that are representative of the population under surveillance; type not defined: the source of data may include sentinel or non-sentinel surveillance sources or both [ 15 ]. Ethical Issues The WHO Global Influenza Programme adhered to the "WHO Guidelines on Ethical Issues in Public Health Surveillance" The analysis and publication of data comply with the "Sharing and Reuse of Health - Related Data for Research Purposes: WHO Policy and Implementation Guidance" This report analyzed de-identified data that contains information licensed under the Open Government Licence - Canada [ 16 , 17 ]. Definitions According to the guideline "Improving RSV Molecular Detection, Typing, and Sequencing Capacity in Participating Laboratories of the WHO Global RSV Surveillance - Phase 2" [ 18 ], most of the RSV is detected by reverse transcription polymerase chain reaction. In this context, we have decided to follow the definitions of RSV seasonality recommended by Midgley CM et al [ 4 ]. The epidemic onset and offset weeks were defined, respectively, as the first and last of two consecutive weeks when the percentage of PCR tests positive for RSV was ≥ 3%. The epidemic duration was defined as the inclusive number of weeks between onset and offset. The peak was defined as the week with the highest percentage of PCR tests positive for RSV [ 4 ]. The RSV season year was defined as the period from the 26th ISO week of the previous year to the 25th ISO week of the next year (2025 data includes up to the 15th ISO week). Data Analyses The weekly percentage of tests positive is the primary indicator used to assess RSV seasonality over time. It is calculated by (Number of Respiratory Syncytial Virus (RSV) detections / Number of specimens processed for RSV using any method) * 100. Results Nine consecutive seasons of RSV surveillance yielded a total sample size of 5,666,687. As shown in Table 1 , within these nine consecutive seasons, the number of specimens processed increased from 245,001 in 2016–2017 to 1,133,858 in 2024–2025; the total sample size reached 5,666,687. The current population of Canada is 40,053,631 as of April 20, 2025, making such a large sample size highly representative. The RSV positive rate varied between season years, with the highest rate of 9.37% emerging in the 2016–2017 season and the lowest rate of 0.08% occurring in the 2020–2021 season. Table 1 Number of specimens processed and RSV-positive detections by year. RSV season year Number of specimens processed Number of RSV-positive RSV positivity rate (%) 2016–2017 245,001 22,964 9.37 2017–2018 286,836 14,664 5.11 2018–2019 284,997 17,581 6.17 2019–2020 474,063 18,725 3.95 2020–2021 422,966 348 0.08 2021–2022 626,434 31,954 5.10 2022–2023 1,003,731 47,086 4.69 2023–2024 1,188,801 42,743 3.60 2024–2025* 1,133,858 49,763 3.46 Total 5,666,687 245,828 4.34 RSV seasons: spans from the 26th ISO week of the previous year to the 25th ISO week of the next year (2025 data includes up to the 15th ISO week). RSV, respiratory syncytial virus. Seasonal diversity in RSV prevalence The RSV-positive rates of 52 ISO weeks (excluding 2019, which ended at week 53) were plotted against ISO weeks. To visualize the seasonality of RSV infection clearly, the seasonal years span from the 26th ISO week of the previous year to the 25th ISO week of the next year (2025 data includes up to the 15th ISO week) artificially. As shown in Fig. 1 , the RSV-positive rate curves exhibit some commonality; specifically, most of them peak in late winter and early spring, dropping to their lowest point in summer. RSV seasonality also demonstrated significant diversity in RSV-positive rates year by year. The highest peak (16.87%) occurred in the 2016–2017 season (Fig. 1 and Table 2 ). The onset week varied each season, with 7 out of 9 occurring between weeks 41 and 48 (Fig. 1 and Table 2 ). The offset week also differed by season; the latest week is 20 (2018), and the earliest week is 9 (2023) (Fig. 1 and Table 2 ). Peak weeks also differed from season to season, ranging from week 49 to week 7 (Fig. 1 and Table 2 ). The epidemic weeks varied within a range of 17 to 29 weeks (Fig. 1 and Table 2 ). Table 2 Seasonal diversity in RSV epidemiology. RSV season year Onset week Peak week RSV positivity rate in peak week (%) Offset week Epidemic weeks 2016–2017 44 52 16.87 17 26 2017–2018 47 5 7.17 20 26 2018–2019 48 7 12.63 17 22 2019–2020 47 1 9.27 12 18 2020–2021 - - - - - 2021-2022a 33 50 11.21 3 23 2021-2022b 10 14 4.05 15 6 2022–2023 41 1 10.34 9 21 2023–2024 43 49 7.37 11 21 2024–2025 46 52 11.72 10 17 RSV season: Spans from the 26th ISO week of the previous year to the 25th ISO week of the next year (2025 data includes up to the 15th ISO week). Threshold: The epidemic onset and offset weeks were defined, respectively, as the first and last of 2 consecutive weeks when the percentage of PCR tests positive for RSV was ≥ 3%. The epidemic duration was the inclusive number of weeks between onset and offset. The peak was defined as the week with the highest percentage of PCR tests positive for RSV —, indicate RSV-positive rate of 2020–2021 season < 3%. Split season (2021–2022): Labeled "a" and "b" for distinct epidemic periods. The impact of the COVID-19 pandemic on RSV seasonality As shown in Fig. 1 and Table 2 , the impact of the COVID-19 pandemic on RSV seasonality is significant. Firstly, the previously observed seasonal epidemic characteristics of RSV completely disappeared during the 2020–2021 season, with an overall RSV-positive rate of 0.08% and no onset or offset week (Table 1 , Table 2 , and Fig. 1 ). Secondly, during the 2021–2022 season, there were two waves with RSV-positive rates exceeding 3%, beginning in week 33 (2021) and week 10 (2022), and ending in week 3 (2022) and week 15 (2022), respectively (Table 2 ); the two epidemic waves comprised a total of 29 epidemic weeks (23 + 6). Thirdly, the onset week was particularly early, occurring as early as week 33 in the 2021–2022 season. Finally, although the curves for the 2022–2023 season and beyond suggest that seasonal patterns are returning to those observed in prepandemic years, the peak RSV-positive rate still remains far below 16.87%. Discussion This study, leveraging a comprehensive dataset of over 5.6 million tests from Canada spanning nine consecutive years (2016–2025), provides critical insights into the seasonal dynamics of RSV and the profound disruptions caused by the COVID-19 pandemic. Our analysis confirms that the winter-spring seasonality of RSV unfolds yearly in Canada, but also reveals striking variability in epidemic timing, intensity, and duration-particularly during the pandemic and post-pandemic periods. These findings underscore the complexity of RSV epidemiology and highlight the interplay between viral competition, public health interventions, and environmental factors. Consistent with prior studies in temperate regions, RSV epidemics in Canada predominantly occurred between late autumn and early spring (weeks 41 − 20), with peak positivity rates ranging from 4.05–16.87%. The observed variability in onset and offset weeks (e.g., onset as early as week 33 in 2021–2022) aligns with reports from the United States and Europe, where RSV seasonality is influenced by climatic factors such as temperature and humidity [ 13 , 19 ]. However, our data suggest that Canada's RSV epidemics may peak earlier (e.g., week 49 in 2016–2017) compared to the U.S., possibly reflecting differences in latitude, population density, or surveillance methodologies. The sharp decline in RSV activity during the summer months further supports the hypothesis that environmental conditions modulate transmission [ 20 ]. The COVID-19 pandemic dramatically altered RSV epidemiology. The near-absence of RSV detections in the 2020–2021 season (positivity rate: 0.08%) mirrors global trends and likely resulted from non-pharmaceutical interventions (e.g., masking, school closures) [ 21 ]. Notably, the subsequent 2021–2022 season exhibited a bifurcated epidemic pattern, with an unusually early summer resurgence (week 33) and a second winter wave-a phenomenon observed in Australia and South Africa [ 22 ]. This deviation from typical seasonality may reflect waning population immunity due to prior RSV suppression, as proposed by the "immunity debt" hypothesis [ 23 ]. The gradual return to pre-pandemic timing by 2023–2024 suggests that RSV seasonality is resilient but may require several years to stabilize post-disruption. The significantly reduced peak positivity rates post-2020 (e.g., 7.37% in 2023–2024 vs. 16.87% in 2016–2017) raise questions about persistent changes in RSV transmission dynamics. Potential explanations include: 1) viral competition; the co-circulation of SARS-CoV-2 and influenza may have competitively suppressed RSV, as seen in other studies [ 24 ]; 2) behavioral shifts; altered healthcare-seeking behavior or testing priorities during and after the pandemic; and 3) immunization effects. While maternal vaccines and monoclonal antibodies were not widely deployed in Canada during the study period [ 25 ], their future use could further modify seasonality. This study's strengths include its large sample size, longitudinal observation over 9 seasons, and alignment with WHO surveillance standards. However, limitations include: 1) lack of age-stratified data, which prevents analysis of age-specific patterns despite known disparities in RSV burden [ 1 – 5 ]; 2) geographic granularity; Canada's climatic diversity (e.g., coastal vs. prairie regions) could mask subnational variations. In conclusion, this study not only delineates the seasonal architecture of RSV in Canada but also provides a template for understanding how emerging pathogens can reshape endemic viral ecosystems. As RSV prevention enters a new era of vaccines and monoclonal antibodies, sustained high-quality surveillance will be essential to monitor the long-term effects of these interventions. Declarations Conflict of interest The authors declare no conflict of interest. Acknowledgements The authors thank WHO Global Influenza Programme provided RSV surveillance data. Author contributions QW and XD conceived the idea for this study, gathered and analyzed data and wrote the manuscript; JH and QW gathered data, analyzed data and reviewed the manuscript; XD analyzed data and reviewed the manuscript. Funding The authors did not receive support from any organization for the submitted work. Data availability All data can be download from the WHO Global Influenza Programme website. 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Cycles of susceptibility: Immunity debt explains altered infectious disease dynamics post -pandemic. Clin Infect Dis 2024 Oct 11:ciae493. 10.1093/cid/ciae493 Teluguakula N, Chow VTK, Pandareesh MD, Dasegowda V, Kurrapotula V, Gopegowda SM, Radic M. SARS-CoV-2 and Influenza Co-Infection: Fair Competition or Sinister Combination? Viruses. 2024;16(5):793. 10.3390/v16050793 . Nourbakhsh S, Shoukat A, Zhang K, Poliquin G, Halperin D, Sheffield H, Halperin SA, Langley JM, Moghadas SM. Effectiveness and cost-effectiveness of RSV infant and maternal immunization programs: A case study of Nunavik. Can EClinicalMedicine. 2021;41:101141. 10.1016/j.eclinm.2021.101141 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6997148","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":481496703,"identity":"c5dde74c-db28-4205-8d9e-7b363b6fd248","order_by":0,"name":"Qin Wang","email":"","orcid":"","institution":"Shanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Qin","middleName":"","lastName":"Wang","suffix":""},{"id":481496704,"identity":"76f48aed-6eab-4cf7-b796-37b0c72ca7a5","order_by":1,"name":"Jingxian Hong","email":"","orcid":"","institution":"Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Jingxian","middleName":"","lastName":"Hong","suffix":""},{"id":481496712,"identity":"3f92a83e-e23a-4d69-9d03-b2b284097502","order_by":2,"name":"Xiaoyan Dong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYDCCA1Cajb+x/cMHAxs54rXwSxw+xjijIM2YeC2SDWlpzDwfDicS1MF3vPfwC8Y2hsQNB86YPbYxYE5gYD98dAM+LZJnzqVZMJwBajncY26cY8CWx8CTlnYDnxaDGzlmBgwVYFsMpHMMeIoZJHjMiNBiANKSYyBtYSCR2ECEFuMHIFtmAr0vzWBgQFiL5JkzZgxAvxj3Sxw+bNhjkGDMRsgvfMd7jD8AQ0y2jb+x8cGPP//l+NkPH8OrBQjYpP8w/HdsgHMJKAcB5g9Awp4IhaNgFIyCUTBSAQB9L02lfFRjpgAAAABJRU5ErkJggg==","orcid":"","institution":"Shanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University","correspondingAuthor":true,"prefix":"","firstName":"Xiaoyan","middleName":"","lastName":"Dong","suffix":""}],"badges":[],"createdAt":"2025-06-28 10:23:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6997148/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6997148/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86316807,"identity":"e7c8e65e-05ac-4919-92ee-f8a0760f4a85","added_by":"auto","created_at":"2025-07-09 09:05:44","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":620485,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVariation in RSV epidemiology over nine consecutive years.\u003c/strong\u003e The RSV-positive rates of 52 ISO weeks (excluding 2019, which ended at week 53, therefore, there are gaps in the data curves other than for 2019) were plotted against ISO week which spans from the 26th ISO week of the previous year to the 25th ISO week of the next year.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6997148/v1/672efb3a1bc7e5a5d0455870.jpeg"},{"id":101752322,"identity":"1ffd661e-ba9a-4b19-9e0e-0a72ed6e2540","added_by":"auto","created_at":"2026-02-03 10:26:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1244483,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6997148/v1/9c57d3c6-8696-4663-868e-6a7430eb33fa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Seasonal Variation in Respiratory Syncytial Virus Epidemiology Over Nine Consecutive Years of Surveillance","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRespiratory syncytial virus (RSV) is a leading cause of acute lower respiratory infections (ALRIs), contributing to a substantial global disease burden [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Infants, young children, and older adults are particularly vulnerable, with an estimated 33\u0026nbsp;million cases annually in children under five, resulting in over 3\u0026nbsp;million hospitalizations and 100,000 deaths worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite its significant impact, effective antiviral treatments remain limited, and prevention strategies primarily rely on passive immunization (e.g., palivizumab) for high-risk infants, though recent advances in maternal vaccines and monoclonal antibodies show promise [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA critical aspect of RSV control is understanding its seasonal epidemic patterns (i.e., seasonality), which influence the timing of interventions such as immunoprophylaxis and public health preparedness [\u003cspan additionalcitationids=\"CR5 CR6 CR7\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. There has been an increasing amount of research focused on the seasonality of RSV epidemics [\u003cspan additionalcitationids=\"CR5 CR6 CR7\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In temperate regions, RSV epidemics typically peak in winter, coinciding with colder temperatures and increased indoor crowding [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]; whereas in tropical and subtropical regions, transmission occurs year-round, often with peaks during rainy seasons, though patterns are less predictable [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Available studies show that environmental factors such as temperature, humidity, and air pollution influence RSV spread, but the mechanisms remain debated; human behavior, such as school terms, travel, and social gatherings contribute to seasonal surges, and viral competition (interactions with other respiratory viruses, such as influenza and SARS-CoV-2) may alter RSV dynamics [\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Increasingly clear evidence shows that the COVID-19 pandemic suppressed RSV circulation in 2020, followed by delayed or off-season outbreaks in 2021\u0026ndash;2022 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhile progress has been made in describing RSV seasonality, variability during and after the COVID-19 pandemic remains poorly understood, as only certain countries have systematically recorded RSV incidence through their own surveillance systems until recent years [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Although the World Health Organization (WHO) RSV surveillance project has been implemented in 25 countries across all six WHO regions from 2016 to 2023 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], we still have not seen a conclusive study focused on RSV seasonality, risk factors, spread, and evolution from this project. The progress of the RSV surveillance project depends on another dominant project, the Global Influenza Surveillance and Response System (GISRS), which might partially explain the delay in expectations [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. On the other hand, many existing conclusions on the disease burden and seasonality of RSV are not derived from original-source, high-quality surveillance data but from the synthesis of data from scattered studies [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. It is commendable that a few studies using the National Respiratory and Enteric Virus Surveillance System data have described RSV seasonality during the prepandemic and pandemic periods in the United States of America (USA) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlthough Canada and the USA are two large neighbors in North America, these two countries differ in landforms, topography, and climate. Thus, the RSV seasonality should also be differ. In this report, we are fortunate to have access to Global Influenza Programme data from the WHO [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] to examine RSV seasonality and assess how the COVID-19 pandemic altered its transmission dynamics. In this database, data from Canada has the highest quality, as it contains complete and long-term RSV surveillance records uninterruptedly from the first week of 2016 to the 15th week of 2025. By analyzing as many as 5,666,687 tests, the RSV seasonality and the impact of COVID-19 were presented.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData Source\u003c/h2\u003e\u003cp\u003eThe WHO Global Influenza Programme provides a global platform for reporting and analyzing influenza and RSV surveillance data [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The dataset is shared through FluNet and fluID by the Global Influenza Surveillance and Response System (GISRS) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Data regarding ISO year, ISO week, countries and territories, the number of specimens processed, and the number of RSV-positive cases were downloaded on April 4, 2025.\u003c/p\u003e\u003cp\u003eWe did not find age aggregation in the original dataset, so we had to forgo age stratification in subsequent analyses. Although the WHO implemented an RSV surveillance project in 25 countries across all six WHO regions from 2016 to 2023 [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], it is challenging to find detailed and reliable RSV surveillance data in this publicly available dataset.\u003c/p\u003e\u003cp\u003eWe are grateful that the data quality from Canada in this database is very high, with the following advantages: 1) it has long-term data records spanning from the first week of 2016 until the data was downloaded in 2025 (15th ISO week); 2) the data covers seasons before, during, and after the COVID-19 pandemic; 3) a sample size of 5,666,687 ensures accurate analysis. Thus, this study focused on Canadian data to identify the epidemiological characteristics of RSV over nine consecutive years.\u003c/p\u003e\u003cp\u003eAccording to the records and definitions of the WHO, the original data source consisted of three parts: non-sentinel: data obtained from non-sentinel systems; data reported in this category may include outbreak investigations, universal testing, testing at the point of care, or other systems apart from sentinel surveillance; sentinel: data obtained from sentinel surveillance, which collects high-quality data systematically and routinely from sentinel surveillance sites that are representative of the population under surveillance; type not defined: the source of data may include sentinel or non-sentinel surveillance sources or both [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEthical Issues\u003c/h3\u003e\n\u003cp\u003eThe WHO Global Influenza Programme adhered to the \"WHO Guidelines on Ethical Issues in Public Health Surveillance\" The analysis and publication of data comply with the \"Sharing and Reuse of Health - Related Data for Research Purposes: WHO Policy and Implementation Guidance\" This report analyzed de-identified data that contains information licensed under the Open Government Licence - Canada [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eDefinitions\u003c/h3\u003e\n\u003cp\u003eAccording to the guideline \"Improving RSV Molecular Detection, Typing, and Sequencing Capacity in Participating Laboratories of the WHO Global RSV Surveillance - Phase 2\" [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], most of the RSV is detected by reverse transcription polymerase chain reaction. In this context, we have decided to follow the definitions of RSV seasonality recommended by Midgley CM et al [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The epidemic onset and offset weeks were defined, respectively, as the first and last of two consecutive weeks when the percentage of PCR tests positive for RSV was \u0026ge;\u0026thinsp;3%. The epidemic duration was defined as the inclusive number of weeks between onset and offset. The peak was defined as the week with the highest percentage of PCR tests positive for RSV [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The RSV season year was defined as the period from the 26th ISO week of the previous year to the 25th ISO week of the next year (2025 data includes up to the 15th ISO week).\u003c/p\u003e\n\u003ch3\u003eData Analyses\u003c/h3\u003e\n\u003cp\u003eThe weekly percentage of tests positive is the primary indicator used to assess RSV seasonality over time. It is calculated by (Number of Respiratory Syncytial Virus (RSV) detections / Number of specimens processed for RSV using any method) * 100.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eNine consecutive seasons of RSV surveillance yielded a total sample size of 5,666,687.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, within these nine consecutive seasons, the number of specimens processed increased from 245,001 in 2016\u0026ndash;2017 to 1,133,858 in 2024\u0026ndash;2025; the total sample size reached 5,666,687. The current population of Canada is 40,053,631 as of April 20, 2025, making such a large sample size highly representative. The RSV positive rate varied between season years, with the highest rate of 9.37% emerging in the 2016\u0026ndash;2017 season and the lowest rate of 0.08% occurring in the 2020\u0026ndash;2021 season.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eNumber of specimens processed and RSV-positive detections by year.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRSV season year\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of specimens processed\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber of RSV-positive\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRSV positivity rate (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2016\u0026ndash;2017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e245,001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22,964\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2017\u0026ndash;2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e286,836\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14,664\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2018\u0026ndash;2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e284,997\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17,581\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2019\u0026ndash;2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e474,063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18,725\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2020\u0026ndash;2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e422,966\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e348\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2021\u0026ndash;2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e626,434\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31,954\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2022\u0026ndash;2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,003,731\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47,086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2023\u0026ndash;2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,188,801\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42,743\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2024\u0026ndash;2025*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,133,858\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49,763\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5,666,687\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e245,828\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eRSV seasons: spans from the 26th ISO week of the previous year to the 25th ISO week of the next year (2025 data includes up to the 15th ISO week).\u003c/p\u003e\u003cp\u003eRSV, respiratory syncytial virus.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eSeasonal diversity in RSV prevalence\u003c/h2\u003e\u003cp\u003eThe RSV-positive rates of 52 ISO weeks (excluding 2019, which ended at week 53) were plotted against ISO weeks. To visualize the seasonality of RSV infection clearly, the seasonal years span from the 26th ISO week of the previous year to the 25th ISO week of the next year (2025 data includes up to the 15th ISO week) artificially. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the RSV-positive rate curves exhibit some commonality; specifically, most of them peak in late winter and early spring, dropping to their lowest point in summer. RSV seasonality also demonstrated significant diversity in RSV-positive rates year by year. The highest peak (16.87%) occurred in the 2016\u0026ndash;2017 season (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The onset week varied each season, with 7 out of 9 occurring between weeks 41 and 48 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The offset week also differed by season; the latest week is 20 (2018), and the earliest week is 9 (2023) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Peak weeks also differed from season to season, ranging from week 49 to week 7 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The epidemic weeks varied within a range of 17 to 29 weeks (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSeasonal diversity in RSV epidemiology.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRSV season year\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOnset week\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePeak week\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRSV positivity rate in peak week (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOffset week\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eEpidemic weeks\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2016\u0026ndash;2017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2017\u0026ndash;2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2018\u0026ndash;2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2019\u0026ndash;2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2020\u0026ndash;2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2021-2022a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2021-2022b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2022\u0026ndash;2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2023\u0026ndash;2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2024\u0026ndash;2025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eRSV season: Spans from the 26th ISO week of the previous year to the 25th ISO week of the next year (2025 data includes up to the 15th ISO week).\u003c/p\u003e\u003cp\u003eThreshold: The epidemic onset and offset weeks were defined, respectively, as the first and last of 2 consecutive weeks when the percentage of PCR tests positive for RSV was \u0026ge;\u0026thinsp;3%. The epidemic duration was the inclusive number of weeks between onset and offset. The peak was defined as the week with the highest percentage of PCR tests positive for RSV\u003c/p\u003e\u003cp\u003e\u0026mdash;, indicate RSV-positive rate of 2020\u0026ndash;2021 season\u0026thinsp;\u0026lt;\u0026thinsp;3%.\u003c/p\u003e\u003cp\u003eSplit season (2021\u0026ndash;2022): Labeled \"a\" and \"b\" for distinct epidemic periods.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eThe impact of the COVID-19 pandemic on RSV seasonality\u003c/h3\u003e\n\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the impact of the COVID-19 pandemic on RSV seasonality is significant. Firstly, the previously observed seasonal epidemic characteristics of RSV completely disappeared during the 2020\u0026ndash;2021 season, with an overall RSV-positive rate of 0.08% and no onset or offset week (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Secondly, during the 2021\u0026ndash;2022 season, there were two waves with RSV-positive rates exceeding 3%, beginning in week 33 (2021) and week 10 (2022), and ending in week 3 (2022) and week 15 (2022), respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e); the two epidemic waves comprised a total of 29 epidemic weeks (23\u0026thinsp;+\u0026thinsp;6). Thirdly, the onset week was particularly early, occurring as early as week 33 in the 2021\u0026ndash;2022 season. Finally, although the curves for the 2022\u0026ndash;2023 season and beyond suggest that seasonal patterns are returning to those observed in prepandemic years, the peak RSV-positive rate still remains far below 16.87%.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study, leveraging a comprehensive dataset of over 5.6\u0026nbsp;million tests from Canada spanning nine consecutive years (2016\u0026ndash;2025), provides critical insights into the seasonal dynamics of RSV and the profound disruptions caused by the COVID-19 pandemic. Our analysis confirms that the winter-spring seasonality of RSV unfolds yearly in Canada, but also reveals striking variability in epidemic timing, intensity, and duration-particularly during the pandemic and post-pandemic periods. These findings underscore the complexity of RSV epidemiology and highlight the interplay between viral competition, public health interventions, and environmental factors.\u003c/p\u003e\u003cp\u003eConsistent with prior studies in temperate regions, RSV epidemics in Canada predominantly occurred between late autumn and early spring (weeks 41\u0026thinsp;\u0026minus;\u0026thinsp;20), with peak positivity rates ranging from 4.05\u0026ndash;16.87%. The observed variability in onset and offset weeks (e.g., onset as early as week 33 in 2021\u0026ndash;2022) aligns with reports from the United States and Europe, where RSV seasonality is influenced by climatic factors such as temperature and humidity [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, our data suggest that Canada's RSV epidemics may peak earlier (e.g., week 49 in 2016\u0026ndash;2017) compared to the U.S., possibly reflecting differences in latitude, population density, or surveillance methodologies. The sharp decline in RSV activity during the summer months further supports the hypothesis that environmental conditions modulate transmission [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe COVID-19 pandemic dramatically altered RSV epidemiology. The near-absence of RSV detections in the 2020\u0026ndash;2021 season (positivity rate: 0.08%) mirrors global trends and likely resulted from non-pharmaceutical interventions (e.g., masking, school closures) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Notably, the subsequent 2021\u0026ndash;2022 season exhibited a bifurcated epidemic pattern, with an unusually early summer resurgence (week 33) and a second winter wave-a phenomenon observed in Australia and South Africa [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This deviation from typical seasonality may reflect waning population immunity due to prior RSV suppression, as proposed by the \"immunity debt\" hypothesis [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The gradual return to pre-pandemic timing by 2023\u0026ndash;2024 suggests that RSV seasonality is resilient but may require several years to stabilize post-disruption.\u003c/p\u003e\u003cp\u003eThe significantly reduced peak positivity rates post-2020 (e.g., 7.37% in 2023\u0026ndash;2024 vs. 16.87% in 2016\u0026ndash;2017) raise questions about persistent changes in RSV transmission dynamics. Potential explanations include: 1) viral competition; the co-circulation of SARS-CoV-2 and influenza may have competitively suppressed RSV, as seen in other studies [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]; 2) behavioral shifts; altered healthcare-seeking behavior or testing priorities during and after the pandemic; and 3) immunization effects. While maternal vaccines and monoclonal antibodies were not widely deployed in Canada during the study period [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], their future use could further modify seasonality.\u003c/p\u003e\u003cp\u003eThis study's strengths include its large sample size, longitudinal observation over 9 seasons, and alignment with WHO surveillance standards. However, limitations include: 1) lack of age-stratified data, which prevents analysis of age-specific patterns despite known disparities in RSV burden [\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]; 2) geographic granularity; Canada's climatic diversity (e.g., coastal vs. prairie regions) could mask subnational variations.\u003c/p\u003e\u003cp\u003eIn conclusion, this study not only delineates the seasonal architecture of RSV in Canada but also provides a template for understanding how emerging pathogens can reshape endemic viral ecosystems. As RSV prevention enters a new era of vaccines and monoclonal antibodies, sustained high-quality surveillance will be essential to monitor the long-term effects of these interventions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003eThe authors thank WHO Global Influenza Programme provided RSV surveillance data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003eQW and XD conceived the idea for this study, gathered and analyzed data and wrote the manuscript; JH and QW gathered data, analyzed data and reviewed the manuscript; XD analyzed data and reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThe authors did not receive support from any organization for the submitted work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e All data can be download from the WHO Global Influenza Programme website.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLi Y, Wang X, Blau DM, Caballero MT, Feikin DR, Gill CJ, Madhi SA, Omer SB, Sim\u0026otilde;es EAF, Campbell H, Pariente AB, Bardach D, Bassat Q, Casalegno JS, Chakhunashvili G, Crawford N, Danilenko D, Do LAH, Echavarria M, Gentile A, Gordon A, Heikkinen T, Huang QS, Jullien S, Krishnan A, Lopez EL, Markić J, Mira-Iglesias A, Moore HC, Moyes J, Mwananyanda L, Nokes DJ, Noordeen F, Obodai E, Palani N, Romero C, Salimi V, Satav A, Seo E, Shchomak Z, Singleton R, Stolyarov K, Stoszek SK, von Gottberg A, Wurzel D, Yoshida LM, Yung CF, Zar HJ, Respiratory Virus Global Epidemiology Network, Nair H. 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Can EClinicalMedicine. 2021;41:101141. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.eclinm.2021.101141\u003c/span\u003e\u003cspan address=\"10.1016/j.eclinm.2021.101141\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"respiratory syncytial virus, epidemiology, surveillance, seasonality","lastPublishedDoi":"10.21203/rs.3.rs-6997148/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6997148/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eUnderstanding the epidemiological characteristics of respiratory syncytial virus (RSV) prevalence is essential for effective prevention and control measures.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eAn observational study using data from the World Health Organization (WHO) Global Influenza Programme, sourced from Canada, which has completed and long-term records of 5,666,687 RSV surveillance uninterruptedly from the first week of 2016 to the 15th week of 2025.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eOf the 5,666,687 tests, 245,828 were RSV-positive, yielding an overall positivity rate of 4.34%. During nine seasonal years of continuous observation, RSV-positive rate varied year-round, suggesting a feature of seasonality, meaning that RSV epidemics always occurred in winter and spring. The RSV-positive rate also varied year by year in terms of the onset week, offset week, peak week, RSV positivity rate in the peak week, and the number of epidemic weeks. The impact of the COVID-19 pandemic on RSV seasonality in Canada is significant. Most notably, the RSV prevalence in the 2020\u0026ndash;2021 season was largely suppressed. Furthermore, in the subsequent three years, variations persisted in the onset week, offset week, peak week, RSV positivity rate in the peak week, and the number of epidemic weeks.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis report, with a sample size in the millions and covering the pre-, mid-, and post-COVID-19 pandemic periods over nine consecutive years, not only demonstrates the seasonal characteristics of RSV prevalence but also reveals the interaction between the two dominant respiratory viruses.\u003c/p\u003e","manuscriptTitle":"Seasonal Variation in Respiratory Syncytial Virus Epidemiology Over Nine Consecutive Years of Surveillance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-09 08:57:39","doi":"10.21203/rs.3.rs-6997148/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"984c5a7a-42bf-4171-8e21-4ea33b7e4522","owner":[],"postedDate":"July 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-30T15:25:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-09 08:57:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6997148","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6997148","identity":"rs-6997148","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}