Time-Series Analysis of Coxsackievirus B Serotype Surveillance Data in Japan

Time-Series Analysis of Coxsackievirus B Serotype Surveillance Data in Japan | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research note Time-Series Analysis of Coxsackievirus B Serotype Surveillance Data in Japan Ayako Sumi, Keiji Mise This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-286112/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Objective Studies have identified serotypes of Coxsackievirus B (CVB) enterovirus as a cause of type 1 diabetes. Studies have also identified cyclical variations in type 1 diabetes incidence—peak incidences occurring in 4- to 6-years periods in two regions in England, a 5-year period in Western Australia, and 5.33-year period in Poland. To date, no studies have investigated whether CVB infection rates demonstrate similar cyclical variation characteristics. The purpose of this study was to characterize periodicity in CVB surveillance data. Results Maximum entropy spectral analysis was performed on monthly CVB surveillance data. In addition to demonstrating a 1-year cycle for all serotypes, spectral peaks demonstrated dominant cycles—6.9-, 3.8-, 4.3-, 9.5-, and 7.8- year periods for CVB1, CVB2, CVB3, CVB4, and CVB5, respectively. Pearson correlation was used to compare the least-squares fit curves based on periods estimated from the analysis with the original data. The results for all five serotypes—CVB1, CVB2, CVB3, CVB4, and CVB5—demonstrated good correlation—ρ = 0.96, ρ = 0.60, ρ = 0.90, ρ = 0.88, and ρ = 0.67, respectively. This method could be a useful tool for the efficient investigation of CVB as a pathogen of type 1 diabetes. Biophysics Type 1 diabetes Coxsackievirus B time-series analysis periodicity surveillance Japan Figures Figure 1 Figure 2 Introduction Coxsackie B (CVB) enterovirus serotypes have recently attracted attention as a cause of type 1 diabetes, which has a high incidence high among children in European countries [1,2]. The estimated increase in annual incidence of type 1 diabetes in Europe was 3.9% (95% CI 3.6%, 4.2%) from 1989 to 2003; worldwide, the estimated annual increase was 2.8% (95% CI 2.4%, 3.2%) from 1990 to 1999 [3]. Examining the periodic structure of CVB serotype surveillance data is essential for predicting the epidemic of type 1 diabetes. Some researchers have reported cyclical variations in yearly incidence rates of type 1 diabetes—4-year intervals in the Yorkshire region in England from 1978 to1990 [4], a 6-year cyclical pattern in a neighboring area of northeast England from 1990 to 2007 [5], a sinusoidal cycle with peaks every 5 years in Western Australia from 1985 to 2010 [6], and a 5.33-year periodicity in Poland during the period 1989-2012 [7]. More recently, to help clarify recent trends in European incidence rates, European Diabetes registry data were analyzed from over 84,000 children in 26 European centers representing 22 countries from 1989-2013, with separate estimates of incidence rate increases derived in five subperiods [3]. To date, no studies have examined whether surveillance data for CVB serotypes show similar cycles as those in type 1 diabetes incidence data, likely because studies investigating publicly available CVB serotype surveillance data for Europe are lacking. On the other hand, in Japan, CVB serotype surveillance data has been collected for 20 years [8]. The purpose of this study was to investigate the periodic structure of Japanese CVB serotype surveillance data of using time-series analysis based on the maximum entropy method (MEM) in the frequency domain and the least squares method (LSM) in the time domain [9, 10]. CVB Serotype Surveillance Monthly surveillance data of CVB serotypes (CVB1, CVB2, CVB3, CVB4, and CVB5) from January 2000 to December 2018 (228 data points) were analyzed. The number of specimens that test positive for pathogens and viruses, including CVB serotypes, are regularly reported to the National Institute of Infectious Disease Surveillance Center (Tokyo, Japan). These data are published in the monthly periodical Infectious Agents Surveillance Report [11]. Monthly surveillance data of CVB serotype from January 2000 to December 2018 are shown in Figure 1. Therein, all incidence data show a yearly cycle with large epidemics every few years, for example, CVB1 (Figure 1a) in 2004 and 2011 and CVB2 (Figure 1c) in 2005 and 2009. Periodicity of the Surveillance Data Power spectral densities (PSDs) obtained with the MEM spectral analysis (Additional file 1) for the data in Figure 1 are shown in Figure 2. In each plot— CVB1 (Figure 2a), CVB2 (Figure 2b), CVB3 (Figure 2c), CVB4 (Figure 2d), and CVB5 (Figure 2e)—prominent spectral peaks were observed at f = 1.0 [units (1/year)], corresponding to the 1-year cycle, that is, the seasonal cycle. In the low-frequency range, f < 1.0, reflecting oscillations longer than the 1-year cycle, several prominent spectral peaks were observed. In each power spectral density plot, the dominant spectral peak was observed during an approximately 3- to 5-year period. For each serotype, five dominant spectral frequency mode peaks with corresponding periods and powers are listed in Table 1. With the five periodic modes that were clearly observed in each PSD (Table 1), the least squares fitting (LSF) curve (Additional file 2) for each serotype was calculated. Each LSF curve thus obtained is presented in Figure 1 Each LSF curve reproduced the original data well (Figure 1), which confirmed the periods from MEM spectral analysis (Figure 2, Table 1) were accurate. Pearson correlations between the original data and the LSF curves—ρ = 0.96, ρ = 0.60, ρ = 0.90, ρ = 0.88, and ρ = 0.67 for CVB1, CVB2, CVB3, CVB4 and CVB5, respectively—further demonstrated a good fit. Discussion And Conclusions An important finding of this study was the 3- to 5-year period in enterovirus surveillance data in Japan (Figure 2 and Table 1). This period is similar to that observed in time-series data on the number of patients with type 1 diabetes in Europe [2]. Therefore, if periodicities in CVB infection rates similar to those identified in these surveillance data in Japan can be found in European data, it would support the association between CVB serotypes and type 1 diabetes. Countries with large numbers of patients with type 1 diabetes, such as Finland, have published surveillance data for enteroviruses but not for subtype-specific enterovirus. To resolve the high incidence of type 1 diabetes in Europe, access to subtype-specific enterovirus surveillance data is essential. We anticipate that this method of time-series analysis will be a useful tool for elucidating periodicity in subtype-specific enterovirus surveillance data. Limitation A limitation of this study was that a direct comparison between CVB infection rate and type 1 diabetes periodicities could not be performed since we did not have access to CVB epidemiological time-series data for European countries. Investigating the correlation of CVB infection rates with type 1 diabetes, for example in countries such as Finland, would allow efficient estimation CVB as pathogen of type 1 diabetes, to help reduce and prevent type 1 diabetes. Abbreviations LSF, least squares fitting; MEM, maximum entropy method; PSD, power spectral density. Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable. Availability of data and material The dataset of surveillance data analyzed during the current study are available from ref. [9]. Competing interests We declare that I have no competing interests. Funding This research was funded by the Japan Society for the Promotion of Science KAKENHI grant number 19K10666. Author’s contributions Both authors conceived the study and managed the data. KM conceived the study and drafted the manuscript. AS analyzed the data and wrote the final version of this paper, read the final manuscript and it. Acknowledgements We thank Coren Walters-Stewart, PhD from Edanz Group (https://en-author-services.edanzgroup.com/ac) for editing a draft of this manuscript and for helping to draft the abstract. References Rewers M, Ludvigsson J. Environmental risk factors for type 1 diabetes. Lancet 2016; 387 : 2340-2348. Patterson CC, Karuranga S, Salpea P, Saeedi P, Dahlquist G, Soltesz G, Ogle GD. Worldwide estimates of incidence, prevalence and mortality of type 1 diabetes in children and adolescents: Results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Res Clin Pract 2019; 157 : 107482-107490. Patterson CC, Harjutsalo V, Rosenbauer J, Neu A, Cinek O, Skrivarhaug T, et al. Trends and cyclical variation in the incidence of childhood type 1 diabetes in 26 European centres in the 25 year period 1989-2013: a multicentre prospective registration study. Diabetologia 2019; 62 : 408–417. Staines A, Bodansky HJ, Lilley HE, Stephenson C, McNally RJ, Cartwright RA. The epidemiology of diabetes mellitus in the United Kingdom: the Yorkshire Regional Childhood Diabetes Register. Diabetologia 1993; 36 : 1282–1287. McNally RJ, Court S, James PW, Pollock R, Blakey K, Begon M. Cyclical variation in type 1 childhood diabetes. Epidemiology 2010; 21 : 914–915. Haynes A, Bulsara MK, Bower C, Jones TW, Davis EA. Cyclical variation in the incidence of childhood type 1 diabetes in Western Australia (1985–2010). Diabetes Care 2012; 35 : 2300–2302. Chobot A, Polanska J, Brandt A, Deja G, Glowinska-Olszewska B, Pilecki O, et al. Updated 24-year trend of type 1 diabetes incidence in children in Poland reveals a sinusoidal pattern and sustained increase. Diabet Med 2017; 34 : 1252–1258. Pons-Salort M, Grassly Serotype-specific immunity explains the incidence of diseases caused by human enteroviruses. Science 2018; 361 : 800-803. National Institute of Infectious Diseases. Infectious Agents Surveillance Report (https://www.niid.go.jp/niid/en/iasr.html). Accessed 3, December 2020. Sumi A, Kobayashi N. Time- series analysis of geographically specific monthly number of newly registered cases of active tuberculosis in Japan. PLoS ONE 2019; 14 : e0213856. Sumi A, Toyoda S, Kanou K, Fujimoto T, Mise K, Kohei Y, et al . Association between meteorological factors and reported cases ofhand, foot, and mouth disease from 2000 to 2015 in Japan. Epidemiol Infect 2017; 145 : 2896-2911. Armitage P, Berry G, Matthews JNS. Statistical Method in Medical Research , 4th edn. Oxford: Blackwell Science, 2012. Ohtomo N, Terachi S, Tanaka Y, Tokiwano K, Kaneko N. New method of time series analysis and its application to Wolf’s sunspot number data. Jpn J Appl Phys 1994; 33 : 2321-2831. Finnish Institute for Health and Welfare. Statistical Databases (https://thl.fi/en/web/thlfi-en). Accessed 3, December 2020. Table Table 1. Characteristics of the five dominant spectral peaks shown in Figure 2. Frequency (1/year) Period (year) Power CoxB1 0.15 6.88 18.52 0.29 3.46 15.07 0.85 1.18 1.18 0.99 1.01 16.1 1.15 0.87 6.61 CoxB2 0.26 3.80 12.97 0.47 2.14 5.85 0.53 1.90 4.17 0.74 1.35 4.42 1.00 1.00 14.32 CoxB3 0.23 4.31 25.63 0.46 2.19 9.2 0.75 1.33 7.74 1.01 1.00 32.28 1.25 0.80 10.31 CoxB4 0.11 9.48 6.93 0.26 3.92 2.69 0.89 1.12 3.73 1.00 1.01 25.26 1.98 0.50 5.35 CoxB5 0.13 7.83 32.09 0.24 4.13 46.68 0.68 1.48 19.78 1.01 0.99 53.25 1.25 0.80 18.79 Supplementary Files Additionalfile1.pdf Additionalfile2.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Withdrawn by author 27 Aug, 2021 Reviewer # 2 agreed at journal 27 May, 2021 Reviewer # 1 agreed at journal 22 May, 2021 Reviewers invited by journal 14 Apr, 2021 Submission checks completed at journal 28 Feb, 2021 Editor invited by journal 23 Feb, 2021 First submitted to journal 15 Feb, 2021 Editor assigned by journal 15 Feb, 2021 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-286112","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research note","associatedPublications":[],"authors":[{"id":14132331,"identity":"93bfeae5-f1ef-4257-8856-674e83de26de","order_by":0,"name":"Ayako Sumi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYJCCAx8gtAGIYCZKy8EZJGth5kHSQhjIz8hOPGzzxy5Pt715A8OPGgZ2c0JaDG7kbjic25ZcbHbmWAFjzzEGZssGQlokQFoamBO33cgxYOBtYGA2OEDQYUAtFn/qE7fdf2PA+JcYLQwghzGwHQbawmPATJQtBmfebjjY23Yc6Je0gsMyxyQI+0W+PXfzhx9/qvPMjh/e+PBNjU0ywRCDgQQQAXSSRDKRsQPVAgJ2RGsZBaNgFIyCEQMAEwhEPfSS21wAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-5347-650X","institution":"Department of Hygiene, Sapporo Medical University School of Medicine","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Ayako","middleName":"","lastName":"Sumi","suffix":""},{"id":14132332,"identity":"612d57a7-3319-414e-8a05-5a095d18f9e3","order_by":1,"name":"Keiji Mise","email":"","orcid":"","institution":"Department of Admission and High school Liaison, Center for Medical Education, Sapporo Medical Univsesity","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Keiji","middleName":"","lastName":"Mise","suffix":""}],"badges":[],"createdAt":"2021-02-28 16:15:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-286112/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-286112/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":6733156,"identity":"e80d37d3-6c48-40a6-9eb8-a7725e3ccb23","added_by":"auto","created_at":"2021-03-09 00:33:02","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":63402,"visible":true,"origin":"","legend":"Comparison of the least-squares fitting curves calculated for long-term trends (solid line) in with original data (dotted line) for (a) CVB1, (b) CVB2, (c) CVB3, (d) CVB4, and (e) CVB5.","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-286112/v1/ebb57e6e1b6086c67cdb8503.jpg"},{"id":6733157,"identity":"c74ea1ff-e137-4247-ad90-d51bddb34e17","added_by":"auto","created_at":"2021-03-09 00:33:02","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":64225,"visible":true,"origin":"","legend":"Power spectral density plots of the original data for (a) CVB1, (b) CVB2, (c) CVB3, (d) CVB4, and (e) CVB5.","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-286112/v1/c5216409eb892f62c7daee11.jpg"},{"id":13675897,"identity":"48685580-3123-4a13-a7be-7b803ba63bc6","added_by":"auto","created_at":"2021-09-17 11:29:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":603259,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-286112/v1/8044b830-40e7-437a-ad72-8aa6b57533d4.pdf"},{"id":6732845,"identity":"5e56b468-6468-4a15-86bd-527481c64c62","added_by":"auto","created_at":"2021-03-09 00:30:02","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":378933,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-286112/v1/a27b35a1ba227863b8695da8.pdf"},{"id":6732847,"identity":"8cfa086a-8778-472b-a6ec-bbea93334936","added_by":"auto","created_at":"2021-03-09 00:30:02","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":265917,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-286112/v1/4a72f0c9b711c4ec22268a62.pdf"}],"financialInterests":"","formattedTitle":"Time-Series Analysis of Coxsackievirus B Serotype Surveillance Data in Japan","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCoxsackie B (CVB) enterovirus serotypes have recently attracted attention as a cause of type 1 diabetes, which has a high incidence high among children in European countries [1,2]. The estimated increase in annual incidence of type 1 diabetes in Europe was 3.9% (95% CI 3.6%, 4.2%) from 1989 to 2003; worldwide, the estimated annual increase was 2.8% (95% CI 2.4%, 3.2%) from 1990 to 1999 [3].\u003c/p\u003e\n\u003cp\u003eExamining the periodic structure of CVB serotype surveillance data is essential for predicting the epidemic of type 1 diabetes. Some researchers have reported cyclical variations in yearly incidence rates of type 1 diabetes\u0026mdash;4-year intervals in the Yorkshire region in England from 1978 to1990 [4], a 6-year cyclical pattern in a neighboring area of northeast England from 1990 to 2007 [5], a sinusoidal cycle with peaks every 5 years in Western Australia from 1985 to 2010 [6], and a 5.33-year periodicity in Poland during the period 1989-2012 [7]. More recently, to help clarify recent trends in European incidence rates, European Diabetes registry data were analyzed from over 84,000 children in 26 European centers representing 22 countries from 1989-2013, with separate estimates of incidence rate increases derived in five subperiods [3].\u003c/p\u003e\n\u003cp\u003eTo date, no studies have examined whether surveillance data for CVB serotypes show similar cycles as those in type 1 diabetes incidence data, likely because studies investigating publicly available CVB serotype surveillance data for Europe are lacking. On the other hand, in Japan, CVB serotype surveillance data has been collected for 20 years [8]. The purpose of this study was to investigate the periodic structure of Japanese CVB serotype surveillance data of using time-series analysis based on the maximum entropy method (MEM) in the frequency domain and the least squares method (LSM) in the time domain [9, 10].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCVB Serotype Surveillance\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMonthly surveillance data of CVB serotypes (CVB1, CVB2, CVB3, CVB4, and CVB5) from January 2000 to December 2018 (228 data points) were analyzed. The number of specimens that test positive for pathogens and viruses, including CVB serotypes, are regularly reported to the National Institute of Infectious Disease Surveillance Center (Tokyo, Japan). These data are published in the monthly periodical\u003cem\u003e Infectious Agents Surveillance Report \u003c/em\u003e[11].\u003c/p\u003e\n\u003cp\u003eMonthly surveillance data of CVB serotype from January 2000 to December 2018 are shown in Figure 1. Therein, all incidence data show a yearly cycle with large epidemics every few years, for example, CVB1 (Figure 1a) in 2004 and 2011 and CVB2 (Figure 1c) in 2005 and 2009.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePeriodicity of the Surveillance Data \u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePower spectral densities (PSDs) obtained with the MEM spectral analysis (Additional file 1) for the data in Figure 1 are shown in Figure 2. In each plot\u0026mdash; CVB1 (Figure 2a), CVB2 (Figure 2b), CVB3 (Figure 2c), CVB4 (Figure 2d), and CVB5 (Figure 2e)\u0026mdash;prominent spectral peaks were observed at \u003cem\u003ef \u003c/em\u003e= 1.0 [units (1/year)], corresponding to the 1-year cycle, that is, the seasonal cycle. In the low-frequency range, \u003cem\u003ef \u003c/em\u003e\u0026lt; 1.0, reflecting oscillations longer than the 1-year cycle, several prominent spectral peaks were observed. In each power spectral density plot, the dominant spectral peak was observed during an approximately 3- to 5-year period. For each serotype, five dominant spectral frequency mode peaks with corresponding periods and powers are listed in Table 1.\u003c/p\u003e\n\u003cp\u003eWith the five periodic modes that were clearly observed in each PSD (Table 1), the least squares fitting (LSF) curve (Additional file 2) for each serotype was calculated. Each LSF curve thus obtained is presented in Figure 1\u003c/p\u003e\n\u003cp\u003eEach LSF curve reproduced the original data well (Figure 1), which confirmed the periods from MEM spectral analysis (Figure 2, Table 1) were accurate. Pearson correlations between the original data and the LSF curves\u0026mdash;\u0026rho; = 0.96, \u0026rho; = 0.60, \u0026rho; = 0.90, \u0026rho; = 0.88, and \u0026rho; = 0.67 for CVB1, CVB2, CVB3, CVB4 and CVB5, respectively\u0026mdash;further demonstrated a good fit.\u003c/p\u003e"},{"header":"Discussion And Conclusions","content":"\u003cp\u003eAn important finding of this study was the 3- to 5-year period in enterovirus surveillance data in Japan (Figure 2 and Table 1). This period is similar to that observed in time-series data on the number of patients with type 1 diabetes in Europe [2]. Therefore, if periodicities in CVB infection rates similar to those identified in these surveillance data in Japan can be found in European data, it would support the association between CVB serotypes and type 1 diabetes. Countries with large numbers of patients with type 1 diabetes, such as Finland, have published surveillance data for enteroviruses but not for subtype-specific enterovirus. To resolve the high incidence of type 1 diabetes in Europe, access to subtype-specific enterovirus surveillance data is essential. We anticipate that this method of time-series analysis will be a useful tool for elucidating periodicity in subtype-specific enterovirus surveillance data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA limitation of this study was that a direct comparison between CVB infection rate and type 1 diabetes periodicities could not be performed since we did not have access to CVB epidemiological time-series data for European countries. Investigating the correlation of CVB infection rates with type 1 diabetes, for example in countries such as Finland, would allow efficient estimation CVB as pathogen of type 1 diabetes, to help reduce and prevent type 1 diabetes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eLSF, least squares fitting; MEM, maximum entropy method; PSD, power spectral density.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and material\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset of surveillance data analyzed during the current study are available from ref. [9].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe declare that I have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the Japan Society for the Promotion of Science KAKENHI grant number 19K10666.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthor\u0026rsquo;s contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBoth authors conceived the study and managed the data. KM conceived the study and drafted the manuscript. AS analyzed the data and wrote the final version of this paper, read the final manuscript and it.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Coren Walters-Stewart, PhD from Edanz Group (https://en-author-services.edanzgroup.com/ac) for editing a draft of this manuscript and for helping to draft the abstract.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRewers M, Ludvigsson J. \u003cstrong\u003eEnvironmental risk factors for type 1 diabetes.\u003c/strong\u003e\u003cem\u003eLancet\u003c/em\u003e 2016; \u003cstrong\u003e387\u003c/strong\u003e: 2340-2348.\u003c/li\u003e\n\u003cli\u003ePatterson CC, Karuranga S, Salpea P, Saeedi P, Dahlquist G, Soltesz G, Ogle GD. \u003cstrong\u003eWorldwide estimates of incidence, prevalence and mortality of type 1 diabetes in children and adolescents: Results from the International Diabetes Federation Diabetes Atlas, 9th edition.\u003c/strong\u003e\u003cem\u003eDiabetes Res Clin Pract\u003c/em\u003e 2019; \u003cstrong\u003e157\u003c/strong\u003e: 107482-107490.\u003c/li\u003e\n\u003cli\u003ePatterson CC, Harjutsalo V, Rosenbauer J, Neu A, Cinek O, Skrivarhaug T,\u003cem\u003e et al. \u003c/em\u003e\u003cstrong\u003eTrends and cyclical variation in the incidence of childhood type 1 diabetes in 26 European centres in the 25 year period 1989-2013: a multicentre prospective registration study. \u003c/strong\u003e\u003cem\u003eDiabetologia\u003c/em\u003e 2019; \u003cstrong\u003e62\u003c/strong\u003e: 408\u0026ndash;417.\u003c/li\u003e\n\u003cli\u003eStaines A, Bodansky HJ, Lilley HE, Stephenson C, McNally RJ, Cartwright RA. \u003cstrong\u003eThe epidemiology of diabetes mellitus in the United Kingdom: the Yorkshire Regional Childhood Diabetes Register.\u003c/strong\u003e\u003cem\u003eDiabetologia\u003c/em\u003e 1993; \u003cstrong\u003e36\u003c/strong\u003e: 1282\u0026ndash;1287.\u003c/li\u003e\n\u003cli\u003eMcNally RJ, Court S, James PW, Pollock R, Blakey K, Begon M. \u003cstrong\u003eCyclical variation in type 1 childhood diabetes. \u003c/strong\u003e\u003cem\u003eEpidemiology\u003c/em\u003e 2010; \u003cstrong\u003e21\u003c/strong\u003e: 914\u0026ndash;915.\u003c/li\u003e\n\u003cli\u003eHaynes A, Bulsara MK, Bower C, Jones TW, Davis EA. \u003cstrong\u003eCyclical variation in the incidence of childhood type 1 diabetes in Western Australia (1985\u0026ndash;2010).\u003c/strong\u003e\u003cem\u003eDiabetes Care \u003c/em\u003e2012; \u003cstrong\u003e35\u003c/strong\u003e: 2300\u0026ndash;2302.\u003c/li\u003e\n\u003cli\u003eChobot A, Polanska J, Brandt A, Deja G, Glowinska-Olszewska B, Pilecki O, \u003cem\u003eet al.\u003c/em\u003e\u003cstrong\u003eUpdated 24-year trend of type 1 diabetes incidence in children in Poland reveals a sinusoidal pattern and sustained increase. \u003c/strong\u003e\u003cem\u003eDiabet Med\u003c/em\u003e 2017; \u003cstrong\u003e34\u003c/strong\u003e: 1252\u0026ndash;1258.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pubmed.ncbi.nlm.nih.gov/?term=Pons-Salort+M\u0026amp;cauthor_id=30139872\"\u003ePons-Salort\u003c/a\u003e M,\u0026nbsp;\u003ca href=\"https://pubmed.ncbi.nlm.nih.gov/?term=Grassly+NC\u0026amp;cauthor_id=30139872\"\u003eGrassly\u003c/a\u003e\u003cstrong\u003eSerotype-specific immunity explains the incidence of diseases caused by human enteroviruses. \u003c/strong\u003e\u003cem\u003eScience\u003c/em\u003e 2018; \u003cstrong\u003e361\u003c/strong\u003e: 800-803.\u003c/li\u003e\n\u003cli\u003eNational Institute of Infectious Diseases. \u003cem\u003eInfectious Agents Surveillance Report \u003c/em\u003e(https://www.niid.go.jp/niid/en/iasr.html). Accessed 3, December 2020.\u003c/li\u003e\n\u003cli\u003eSumi A, Kobayashi N. \u003cstrong\u003eTime-\u003c/strong\u003e\u003cstrong\u003eseries analysis of geographically specific monthly number of newly registered cases of active tuberculosis in Japan. \u003c/strong\u003e\u003cem\u003ePLoS ONE\u003c/em\u003e 2019; \u003cstrong\u003e14\u003c/strong\u003e:\u0026nbsp;e0213856.\u003c/li\u003e\n\u003cli\u003eSumi A, Toyoda S, Kanou K, Fujimoto T, Mise K, Kohei Y, \u003cem\u003eet al\u003c/em\u003e. \u003cstrong\u003eAssociation between meteorological factors and reported cases ofhand, foot, and mouth disease from 2000 to 2015 in Japan. \u003c/strong\u003e\u003cem\u003eEpidemiol Infect\u003c/em\u003e 2017; \u003cstrong\u003e145\u003c/strong\u003e: 2896-2911.\u003c/li\u003e\n\u003cli\u003eArmitage P, Berry G, Matthews JNS. \u003cem\u003eStatistical Method in Medical Research\u003c/em\u003e, 4th edn. Oxford: Blackwell Science, 2012.\u003c/li\u003e\n\u003cli\u003eOhtomo N, Terachi S, Tanaka Y, Tokiwano K, Kaneko N. \u003cstrong\u003eNew method of time series analysis and its application to Wolf\u0026rsquo;s sunspot number data.\u003c/strong\u003e\u003cem\u003eJpn J Appl Phys\u003c/em\u003e 1994;\u003cstrong\u003e 33\u003c/strong\u003e: 2321-2831.\u003c/li\u003e\n\u003cli\u003eFinnish Institute for Health and Welfare. \u003cem\u003eStatistical Databases\u003c/em\u003e (https://thl.fi/en/web/thlfi-en). Accessed 3, December 2020.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Characteristics of the five dominant spectral peaks shown in Figure 2.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"57\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e\u003cstrong\u003eFrequency \u003c/strong\u003e\u003cstrong\u003e(1/year)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e\u003cstrong\u003ePeriod (year)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e\u003cstrong\u003ePower\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"5\" width=\"57\"\u003e\n\u003cp\u003e\u003cstrong\u003eCoxB1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e0.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e6.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e18.52\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e0.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e3.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e15.07\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e0.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e1.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e1.18\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e0.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e1.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e16.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e1.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e0.87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e6.61\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"5\" width=\"57\"\u003e\n\u003cp\u003eCoxB2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e0.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e3.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e12.97\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e0.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e2.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e5.85\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e0.53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e1.90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e4.17\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e0.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e1.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e4.42\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e14.32\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"5\" width=\"57\"\u003e\n\u003cp\u003eCoxB3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e0.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e4.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e25.63\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e0.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e2.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e9.2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e0.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e1.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e7.74\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e1.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e32.28\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e1.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e0.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e10.31\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"5\" width=\"57\"\u003e\n\u003cp\u003eCoxB4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e0.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e9.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e6.93\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e0.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e3.92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e2.69\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e0.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e1.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e3.73\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e1.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e25.26\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e1.98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e0.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e5.35\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"5\" width=\"57\"\u003e\n\u003cp\u003eCoxB5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e0.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e7.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e32.09\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e0.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e4.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e46.68\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e0.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e1.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e19.78\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e1.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e0.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e53.25\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e1.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e0.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e18.79\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-research-notes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"resn","sideBox":"Learn more about [BMC Research Notes](http://bmcresnotes.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/resn/default.aspx","title":"BMC Research Notes","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Type 1 diabetes, Coxsackievirus B, time-series analysis, periodicity, surveillance, Japan","lastPublishedDoi":"10.21203/rs.3.rs-286112/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-286112/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObjective\u003c/p\u003e\u003cp\u003eStudies have identified serotypes of Coxsackievirus B (CVB) enterovirus as a cause of type 1 diabetes. Studies have also identified cyclical variations in type 1 diabetes incidence—peak incidences occurring in 4- to 6-years periods in two regions in England, a 5-year period in Western Australia, and 5.33-year period in Poland. To date, no studies have investigated whether CVB infection rates demonstrate similar cyclical variation characteristics. The purpose of this study was to characterize periodicity in CVB surveillance data.\u003c/p\u003e\u003cp\u003eResults\u003c/p\u003e\u003cp\u003eMaximum entropy spectral analysis was performed on monthly CVB surveillance data. In addition to demonstrating a 1-year cycle for all serotypes, spectral peaks demonstrated dominant cycles—6.9-, 3.8-, 4.3-, 9.5-, and 7.8- year periods for CVB1, CVB2, CVB3, CVB4, and CVB5, respectively. Pearson correlation was used to compare the least-squares fit curves based on periods estimated from the analysis with the original data. The results for all five serotypes—CVB1, CVB2, CVB3, CVB4, and CVB5—demonstrated good correlation—ρ = 0.96, ρ = 0.60, ρ = 0.90, ρ = 0.88, and ρ = 0.67, respectively. This method could be a useful tool for the efficient investigation of CVB as a pathogen of type 1 diabetes.\u003c/p\u003e","manuscriptTitle":"Time-Series Analysis of Coxsackievirus B Serotype Surveillance Data in Japan","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-03-09 00:30:00","doi":"10.21203/rs.3.rs-286112/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Withdrawn by author","date":"2021-08-28T00:00:00+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2021-05-28T00:00:00+00:00","index":2,"fulltext":""},{"type":"reviewerAgreed","content":"","date":"2021-05-23T00:00:00+00:00","index":1,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-04-15T00:00:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2021-02-28T16:15:01+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2021-02-24T00:00:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"","date":"2021-02-16T00:00:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2021-02-16T00:00:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-research-notes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"resn","sideBox":"Learn more about [BMC Research Notes](http://bmcresnotes.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/resn/default.aspx","title":"BMC Research Notes","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"129d1039-daf9-4fa8-8678-ac542cebc404","owner":[],"postedDate":"March 9th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":2822816,"name":"Biophysics"}],"tags":[],"updatedAt":"2021-03-09T00:30:00+00:00","versionOfRecord":[],"versionCreatedAt":"2021-03-09 00:30:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-286112","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-286112","identity":"rs-286112","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}