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

Objective Studies have identied serotypes of Coxsackievirus B (CVB) enterovirus as a cause of type 1 diabetes. Studies have also identied 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 t curves based on periods estimated from the analysis with the original data. The results for all ve 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 ecient investigation of CVB as a pathogen of type 1 diabetes.


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 ve 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  Table 1.
With the ve periodic modes that were clearly observed in each PSD (Table 1), the least squares tting (LSF) curve (Additional le 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 con rmed 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 t.

Discussion And Conclusions
An important nding 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 identi ed 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 subtypespeci c enterovirus. To resolve the high incidence of type 1 diabetes in Europe, access to subtype-speci c enterovirus surveillance data is essential. We anticipate that this method of time-series analysis will be a useful tool for elucidating periodicity in subtype-speci c 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 e cient estimation CVB as pathogen of type 1 diabetes, to help reduce and prevent type 1 diabetes. Abbreviations LSF, least squares tting; 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 nal version of this paper, read the nal manuscript and it.