Emergence of novel SARS-CoV-2 variants keeps slowing down

Emergence of novel SARS-CoV-2 variants keeps slowing down | 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 Article Emergence of novel SARS-CoV-2 variants keeps slowing down Xin Sun, Zhuoran Huang, Jiayu Sang, Peipei Guo, Jiani Zhang, Jiaxin Li, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8761051/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 15 You are reading this latest preprint version Abstract Background Although COVID-19 pandemic has no longer been classified as an international public health emergency of concern after May 2023, multiple variants with different characteristics keep emerging. Given the complex spatial-temporal characteristics of epidemics, responding to potential changes in variants is a significant public health challenge. Serotypes are defined as unique variants within specific immune response characteristics, which are used in the study of various pathogens. The sero-epidemiological features of COVID-19 may offer new insight into public health. Methods Based on the global SARS-CoV-2 genome deposition data in Our World in Data (OWID) based on GISAID from March 1, 2021 to May 3, 2024, we analyzed the epidemic features of different variants based on the serotype concept and combined with cross-sectional studies. Results In the dataset comprising variants from 58 countries or regions with complete information, we calculated the median duration times of epidemic, the median epidemic peak times for Serotypes Ⅱ-Ⅴ and the median alternation times of epidemic for Serotypes I-Ⅱ to Ⅴ-Ⅵ respectively. There are linear relationships for these time period between the epidemics of Serotypes Ⅱ, Ⅲ, Ⅳ and Ⅴ, except the longer duration time for Serotype I. By constructing a simple linear regression and curve regression equation, the emerging of a new serotype can be predicted with the time around 2025, or even later. Conclusion The gradually-increased prevalence periods for SARS-CoV-2 serotypes except Serotype I may suggest a slowing down mutation rate. Understanding the epidemic time of different serotypes can provide insight into the surveillance and forecasting of COVID-19. SARS-CoV-2 serotypes prevalence slow forecast Figures Figure 1 Figure 2 Figure 3 Background With the consideration of a decline in the associated human health risks, on May 5, 2023, the World Health Organization (WHO) declared the report that COVID-19 had transitioned from a Public Health Emergency of International Concern (PHEIC) to a persistent and ongoing health concern [ 1 ]. However, the ongoing mutation and evolution of SARS-CoV-2 strains persist. In the past period of time, new variants including JN.1, KP.3.1.1, XEC, LP.8.1, NB.1.8.1 and XFG have emerged, each with distinct viral characteristics and under continuous surveillance by WHO [ 2 ]. The WHO has classified the variants of SARS-CoV-2 as variant of concern, variant of interest, and variant under monitoring [ 3 ]. This classification method has achieved the purpose of monitoring, warning, and mitigating the public health risks of different SARS-CoV-2 variants. However, through individual variant analysis, it is challenging to ascertain the transmission patterns among different variants and generalize its application across diverse regions and populations. This challenge arises from the concurrent circulation of multiple variant strains across global regions. These complex characteristics make it even more difficult to predict the upcoming wave of infections [ 4 ]. The serotype of a pathogen is defined as a distinct variant within a microbial species, characterized by specific immune reactions mainly relying on the surface antigens. Antibodies generated against one serotype typically exhibit limited efficacy against other serotypes [ 5 ]. The serotype concept has been used for the classification of poliovirus (PV), dengue virus (DENV), and other various pathogens, which benefits the virus surveillance, vaccine administration, etc. Poliovirus can be classified into three different serotypes, which are distinguished by the antigenic sites in the viral capsid proteins [ 6 ]. All three serotypes of poliovirus cause paralytic disease [ 7 ]. Four serotypes (DEN-1 to DEN-4) of DENV [ 8 ], are also key factors influencing the infection severity and the vaccine development [ 9 ]. Different SARS-CoV-2 variants can also be categorized into different serotypes. In early 2022, it was proposed to classify the prototype strain and early variants of concern (VOCs) up to Delta as Serotype 1, while the Omicron variants as Serotype 2 due to their distinct pathological, structural, and antigenic features compared to Serotype 1 [ 5 ]. Subsequently, a serotype analysis study was conducted using human sera obtained from individuals who had recovered from initial infections. The currently-known human-infecting SARS-related coronaviruses were ultimately classified into three distinct serotypes, i.e. SARS-CoV-1, ancestral SARS-CoV-2, and the SARS-CoV-2 Omicron variants [ 10 ]. According to the similarity of RBD (Receptor-binding domain) antigenicity, Wang et al. further categorized 23 representative variant strains into five serotypes (Serotype Ⅰ to Ⅴ) based on the cross-neutralization levels between pseudoviruses and RBD mRNA vaccine immunized mouse antisera to the existing dominant strains [ 11 ]. Afterwards, BA2.86 was further updated as Serotype Ⅵ [ 12 ]. Recently, consistent results were obtained in subsequent classification studies using the spike (S) protein as the immunogen [ 13 ]. Focusing on the distribution and epidemiological patterns of various variants [ 2 , 14 ], the characteristics of the COVID-19 pandemic were investigated, including spatial-temporal trends, population group features [ 15 – 17 ], as well as the dynamics of transmission models [ 18 ]. However, how the novel concepts of SARS-CoV-2 serotypes could shed light on epidemic patterns of COVID-19 is still unknown. Serotype classification holds significant importance in terms of epidemiology, disease prevention and control, as well as vaccine selection. In this study, combined with evidence from serotype classification, we analyzed relevant data and information on the SARS-CoV-2 epidemic situation in different countries, revealing the epidemiological patterns of SARS-CoV-2 from a fresh perspective. Methods Data source The information of SARS-CoV-2 variants circulating in various countries was obtained from Our World in Data (OWID) website, updated from March 2021 to May 2024 [ 19 ]. The OWID database was updated every two weeks, using the release data of each SARS-CoV-2 variant in GISAID database [ 20 – 23 ]. Inclusion standards The OWID database used in this study includes data information from 125 countries or regions. Due to the lack of consistent reporting on variant strain proportions every two weeks across all countries or regions, significant data gaps exist in certain areas. To ensure adequate time period information for analysis and maintain data quality, our study mandated that selected countries provide at least two sets of serotype data during a specified prevalence period, encompassing the duration of the epidemic, peak times, or alternating periods with at least two serotypes. We ultimately used 58 countries or regions for serotype prevalence analysis based on the quality of the data. According to the updated data as of May 3rd from the OWID website [ 19 ]. The database includes 19 dominantly prevalent variants of SARS-CoV-2, namely Alpha、Beta、Gamma、Delta、BA.1、BA.2、BA.2.12.1、BA.4、BA.5、BQ.1、XBB、XBB.1.5、XBB.1.16、XBB.1.9、XBB.2.3、EG.5.1、XBB.1.5.70、HK.3 and BA.2.86. The serotype classification criteria refer to the classification content in previous studies [ 10 – 12 ]. Based on existing evidence and the S protein epitope information published by GISAID [ 24 ], the above strains can be classified into six serotypes (supplement Fig. 1 ), including Serotype Ⅰ (Alpha、Beta、Gamma、Delta), Serotype Ⅱ (BA.1), Serotype Ⅲ(BA.2、BA.2.12.1), Serotype Ⅳ (BA.4、BA.5、BQ.1), Serotype Ⅴ (XBB、XBB.1.5、XBB.1.16、XBB.1.9、XBB.2.3、EG.5.1、XBB.1.5.70、HK.3) and Serotype Ⅵ (BA.2.86). Table 2 Linear regression epidemic periods between SARS-CoV-2 serotypes. TP dura a TP peak TP alter TP inter Coefficient b 18.157*** (1.760) 10.152*** (0.426) 16.716*** (0.388) 13.984*** (0.387) Constant 4.378 (5.094) -13.696*** (1.436) -21.724*** (1.348) -26.018*** (1.320) R 2 0.513 0.773 0.915 0.883 Adjusted R 2 0.508 0.772 0.915 0.883 F Statistic 106.442*** (df = 1; 101) 569.156*** (df = 1; 167) 1856.198*** (df = 1; 172) 1308.476*** (df = 1; 173) a The duration of the time period of epidemic (TP dura ), the peak time period of epidemic (TP peak ), the alternation time period of epidemic (TP alter ) and the interval between the epidemic onsets (TP inter ). Considering the unique epidemic period for Serotype I, stringent control measures were implemented globally during the initial stages of outbreak before vaccination commenced. Therefore, Serotype I was not included in the comparison of the same epidemic period. Meanwhile, for TP dura , as there is no available termination point for Serotypes V and Ⅵ, we consider the duration of Serotypes II-Ⅳ as a benchmark; For TP peak , to exclude the influence of single-country values for Serotype Ⅵ during its peak time, we select the peak times for Serotypes Ⅱ-V. For TP alter , since Serotype Ⅶ has not yet emerged, the prevailing alternation period of Serotypes Ⅱ-V was chosen. For TP inter , it can be used to predict the emerging time of a potential new serotype for SARS-CoV-2, which can be used to calculate such as TP inter I = T onset Ⅱ - T onset I . To eliminate the influence of Serotype I, we selected data from serotypes Ⅱ-Ⅵ to calculate TP inter Ⅱ -TP inter Ⅵ . b TP dura =18.157X + 4.378, TP peak =10.152X-13.696, TP alter =16.716X-21.724, the X indicates the serotype of SARS-CoV-2. TP inter = 13.984X-26.018, the X represents the serotypes of the time difference between X and X + 1. (Ex. When X = 6, it means the time difference between serotypes Ⅵ and Ⅶ). Definition of time data The prevalence of variant strains in each country is updated biweekly in OWID database, based on the deposition data of genome sequences in GISAID. The deposition data for each variant strain released during each update is classified and organized into corresponding serotype proportion data by deposition date. These data were used for facilitating determination of relevant “time points” through compilation of serotype proportions. Drawing from our meticulously organized database, we herein define four distinct “time points” and three corresponding “time periods” as follows: The onset time point of the epidemic (T onset ): The onset time point of the epidemic is defined as the date when each country or region initially reports a proportion of variant strains with corresponding serotypes exceeding 0.01%. Since the pre-Omicron variants belong to Serotype I, the onset time point of Serotype I epidemic will be based on the date when each country or region officially announced the discovery of their first SARS-CoV-2 case. The end time point of the epidemic (T end ): The end time point of the epidemic is defined as when the proportion of the original prevalent serotype reported by each country or region for the first time falls below 0.01%. To ensure accuracy and avoid errors caused by missed or false reports, or disappearance of time points, this time point should also satisfy the condition that in subsequent two depositions (one month time), the proportion of original prevalent serotype remains still less than 0.01%. The peak time point of the epidemic (T peak ): The peak time point of the epidemic is defined as the date when the proportion of initial serotype variants reaches its highest point in each country or region. The alternation time point of epidemic (T alter ): The alternation time point of epidemic is defined as the date at which the proportion of newly emerging serotype variants surpasses that of existing prevalent serotype variants. According to the designated “time points” for the epidemic, three distinct “time periods” are identified and measured in weeks. The duration “time period” of epidemic (TP dura ): TP dura = T end - T onset , represents the length of time a certain serotype will prevail in a country or region. The peak “time period” of epidemic (TP peak ): TP peak = T peak - T onset , represents the duration it takes for a certain serotype to reach its highest proportion in a country or region. The alternation “time period” of epidemic (TP alter ): TP alter = T alter - T onset , represents how long a certain serotype will be replaced by the next serotype, no longer prevailing as the dominant serotype. Data input and quality assurance During the data extraction process, a dual-entry data input procedure is implemented to establish an Excel database for SARS-CoV-2 serotypes based on relevant definitions. In case of any discrepancies during result consolidation, they will be resolved through discussions between two researchers or referred to a third researcher for resolution. All researchers participating in this study have the right to inspect the raw data, verify and correct any missing or biased data, to ensure the quality of the dataset. Classification standards for countries and regions According to the World Bank's classification standards based on income levels as of July 1, 2024 [ 25 ], the countries or regions involved in this study are divided into three groups: high-income group, upper-middle-income group, and lower-middle-income group. Meanwhile, the Global Health Security Index updated and released in 2021 by Johns Hopkins University, Nuclear Threat Initiative (NTI), and other institutions, serves as a benchmark for assessing the health security of different countries [ 26 ]. This study uses a scoring system with a maximum score of 100 points and divides each 20-point range into one level. The rating range of the countries or regions involved in this study is between 20 and 80 points, with levels of high security index (60–80), medium security index (40–60), and low security index (20–40). Statistical analysis Descriptive epidemiological research methods were employed to analyze the distribution of different SARS-CoV-2 serotypes in various regions since the outbreak. The median time point was used to describe the overall distribution. Non-matching non-parametric tests were utilized to compare distribution of different SARS-CoV-2 serotypes among income levels or global health security capacities (Mann-Whitney test for two groups and the Kruskal-Wallis test for three groups). Dunn's Test was used to perform pairwise comparisons among three sets of data. Spearman correlation analysis was applied to explore the correlation between serotypes and epidemic timelines. Univariate linear regression and curve regression were used to predict potential time for the future serotypes. Data organization was conducted using Excel (Microsoft 2019), and statistical analysis was performed using SPSS version 26.0 (IBM Corp. Released 2019. Armonk, NY, United States: IBM Corp), R (4.4.1) and GraphPad Prism (10.1.2) software. We used the PowerPoint (Microsoft 2019) and GraphPad Prism (10.1.2) for all drawings in our study. The merging and adjustment of images were done using AI software (Adobe Illustrator 2024). A two-sided P value of less than 0.05 was considered statistically significant. Results The time point and time period of SARS-CoV-2 serotype epidemics The global incidence situations in the past nearly five years are complicated, with a significant increase in the number of cases in early 2022 and 2023. The median T onset for Serotype Ⅰ was February 2020, based on the data collected from the official announcements of different countries and regions (Supplement Table 1 ). Table 1 Epidemic time periods of SARS-CoV-2 serotypes. TP dura (week) a TP peak (week) TP alter (week) Serotype I 111 b NA 98 (107,118.25) NA (96.5,101) Serotype II 34 8 14 (30,46) (6,8.5) (12,16) Serotype III 64 16 26 (58,74) (16,18) (24,26) Serotype IV 72 22 44 (60,82) (18,28) (42,52) Serotype V NA 45 64 NA (36.5,48.5) (62,68) Serotype VI NA 26* NA NA NA NA a The duration of the time period of epidemic (TP dura ), the peak time period of epidemic (TP peak ), the alternation time period of epidemic (TP alter ). "NA" represents the data were unavailable. The definitions of the TP dura , TP peak and TP alter were described in the Methods. b The medians TP dura , TP peak and TP alter were shown in the table with interquartile ranges in the brackets. "*" indicates that there is a data for only one country. The specific data were described in the Supplementary material (Supplement Table 2 –4). Based on data collected and organized from the OWID database, the median T end and T alter for Serotype Ⅰ were March and January 2022, respectively; for Serotype Ⅱ, the median T onset , T end , T peak , and T alter were December 2021, August 2022, January, and March, respectively; for Serotype Ⅲ, the median T onset , T end , T peak , and T alter were January 2022, March 2023, May 2022, and July 2022, respectively; for Serotype Ⅳ, the median T onset , T end , T peak , and T alter were April 2022, August 2023, September 2022, and February 2023, respectively. For Serotype Ⅴ, the median T onset , T peak , and T alter were September 2022, July 2023, and December 2023, respectively. The median T onset for Serotype Ⅵ was September 2023 (supplement Fig. 2 ; supplement Table 2 –4). Based on the median prevalence time period that can be calculated from the prevalence time nodes, the TP dura and TP alter for Serotype Ⅰ were 111 weeks and 98 weeks, respectively; for Serotype Ⅱ, the TP dura , TP peak and TP alter were 34 weeks, 8 weeks, and 14 weeks; for Serotype Ⅲ, the TP dura , TP peak and TP alter were 64 weeks, 16 weeks and 26 weeks; for Serotype Ⅳ, the TP dura , TP peak and TP alter were 72 weeks, 22 weeks and 44 weeks; for Serotype Ⅴ, the TP peak and TP alter were 45 weeks and 64 weeks (Fig. 1 A and Table 1 ). The extending epidemic periods of SARS-CoV-2 serotypes A comparative analysis was conducted to examine the duration of different serotypes, based on epidemic times (TP dura ), peak times (TP peak ), and alternation times (TP alter ) using data from 58 countries or regions. Considering the unique epidemic period for Serotype I as depicted in Fig. 1 A, stringent control measures were implemented globally during the initial stages of outbreak before vaccination commenced. Therefore, Serotype I was not included in the comparison of the same epidemic period. The results showed that there was an obvious increasing trend for the epidemic periods from Serotype Ⅱ to Serotype Ⅴ. Statistically significant differences of TP dura , TP peak , and TP alter were found between the contiguous serotypes (Fig. 1 B). Meanwhile, regarding the duration of epidemic, as there is no available termination point for Serotypes Ⅴ and Ⅵ, we consider the duration of Serotypes Ⅱ-Ⅳ as a benchmark; For the peak time of epidemic, to exclude the influence of single-country values for Serotype Ⅵ during its peak time, we select the peak times for Serotypes Ⅱ-Ⅴ. For the alternation time of epidemic, since Serotype Ⅶ has not yet emerged, the prevailing alternation period of Serotypes Ⅱ-Ⅴ was chosen. Based on the selected criteria mentioned above, this study plotted scatter plots of different time periods for different serotypes. By analyzing the characteristics of these scatter plots, it was found that the TP dura , TP peak , and TP alter exhibited certain linear relationships. According to the results of Spearman's linear correlation analysis, the correlation coefficients for epidemic duration, peak timing, and alternation period were 0.755, 0.898, and 0.955 respectively. This indicates that there is a linear correlation between serotype classification and epidemic time periods (Fig. 1 C). The potential impact factors on the epidemic dynamics of SARS-CoV-2 serotypes According to the World Bank's classification of countries based on different income levels [ 25 ], the 58 included countries or regions are categorized into three groups: high-income level (72·41%, 42/58), upper-middle-income level (22·41%, 13/58), and lower-middle-income level (5·17%, 3/58). There is no association between the TP dura of different serotypes and income level grouping, although some time periods have differences between the income levels for certain serotypes (supplement Fig. 3 ). According to the Global Health Security (GHS) Index,[ 26 ] the 58 countries or regions included in this study are categorized into three groups: high security index (32·76%, 19/58), medium security index (62·07%, 36/58), and low security index (5·17%, 3/58). Longer TP dura and TP alter were observed in the high-security index group than the medium-security index group, especially for Serotypes Ⅲ (Supplement Fig. 4). We also plotted a scatter plot and curve regression for high-income level and upper-middle-income level in terms of income level grouping, as well as high security index and medium security index in terms of Global Health Security index (Supplement Fig. 5). It showed that higher-income and higher-safety-index groups have longer TP dura from all data, indicating a certain degree of tail effect. We have summarized the levels of ACE2 (Angiotensin-converting enzyme 2) affinity for different variants based on the previously-reported data and categorized them into corresponding Serotypes Ⅰ-Ⅵ (Supplement Table 6) [ 27 ]. The affinity levels of strains within Serotypes Ⅰ-Ⅵ did not show obvious trend for fluctuations, and association with serotypes. The prediction of the potential emerging of a new SARS-CoV-2 serotype The linear trends of the epidemic time periods, i.e. TP dura , TP peak , and TP alter enable us to predict the emerging time of a potential new serotype for SARS-CoV-2, named Serotype Ⅶ. To achieve this, we organized the existing temporal information node data and analyzed the differences in epidemic onset times among these serotypes. We defined the interval between epidemic onsets as TP inter and identified a related relationship based on scatter plots and Spearman's linear correlation analysis results (correlation coefficient = 0.949). To predict the possible occurrence time of the next serotype, TP inter was used to construct a simple linear regression equation to estimate the emergence time of new serotypes (Supplement Table 5). By computing the time difference between predicted Serotype Ⅶ and current Serotype Ⅵ as 57.89 weeks with a 95% confidence interval of (55.64,60.14), in conjunction with September 11th, 2023 being identified as the median epidemic start date for Serotype Ⅵ, we converted the aforementioned confidence interval into specific dates and estimated that emergence of the new serotype may transpire within October 4th to November 4th, 2024 (Supplement Fig. 6A). Through using a second-order polynomial regression model, by computing the time difference between predicted Serotype Ⅶ and current Serotype Ⅵ as 77.42 weeks with a 95% confidence interval of (68.09, 86.76), we estimated that emergence of the new serotype may transpire within December 31st, 2024 to May 10th, 2025 (Fig. 2 A). At the same time, we also employed other models for comparison. The logarithmic model predicts Serotype Ⅶ emergence around August 11th, 2024 with relatively high temporal precision (Supplement Fig. 6B), whereas the exponential model projects a later and highly uncertain emergence near May 16th, 2025 (Fig. 2 B) and the logistic model suggests an intermediate prediction around March 2nd, 2025 with moderate confidence, collectively illustrating how model structure strongly shapes temporal inference (Fig. 2 C). According to the different onset time points of serotypes, we also plotted relevant graphs (Supplement Fig. 7A). It is a characteristic that the onset time of the epidemic may show a curved distribution after Serotype Ⅱ. Therefore, we conducted another curve regression prediction methods based on the onset point of serotypes (Supplement Fig. 7B). The new serotype predicted using second and third-order polynomial regression is expected to emerge around October 2024 or March 2025. Furthermore, the estimated TP dura for Serotype Ⅴ is 95.16 weeks with a 95% confidence interval ranging from 86.96 to 103.36 weeks. Similarly, for Serotype Ⅵ, the estimated TP dura is 113.32 weeks with a 95% confidence interval ranging from 101.77 to 124.87 weeks. This means the Serotype Ⅴ and Ⅵ may end in July 2024 and November 2025, respectively (Table 2 ). Serotype Ⅵ will reach its peak at approximately 47.22 weeks (95% confidence interval: 44.72 to 49.71), which is August 2024 (Table 2 ). As the same way, TP alter for Serotype Ⅵ is 78.57 weeks with a 95% confidence interval of (76.35, 80.80), which indicates Serotype Ⅵ may be replaced by the predicted Serotype Ⅶ at March, 2025 (Table 2 ). However, considering the trend of a slowdown in the emergence of SARS-CoV-2 serotypes, the new serotype may eventually emerge in March 2025 or even later. Discussion Serotype classification methods enable a refined understanding of viruses and their variants, which holds significant importance in terms of epidemiology, disease prevention and control, as well as vaccine selection [ 28 ]. The pre-Omicron variants were designated as Serotype Ⅰ, the epidemic duration and alternation of which were significantly longer compared to other serotypes, which we attributed to the stringent prevention and control measures implemented by various countries during the early stages of the outbreak [ 29 ]. During a survey on the impact of major non-pharmaceutical interventions in 11 European countries from the start of the COVID-19 pandemic in 2020, it was found that these interventions had a significant effect in reducing virus transmission [ 30 ]. Meanwhile, from an immunological perspective, the approval of many countries' COVID-19 vaccines started from December 2020 to early 2021 [ 31 – 33 ], so during the prevalence of Serotype Ⅰ, the impact of vaccine administration on variants was relatively minimal. After further division of the included countries, it was found that there were only certain epidemic differences in serotypes among countries with different levels of global health security index. We hypothesize that this disparity may be attributed to the uneven representation of countries across different classification groups, as well as the divergence between the Global Health Security Index and actual capacities for public health prevention and control. Taking the United States as an illustrative example, despite securing the top rank in the Global Health Security Index, it continues to demonstrate a significant burden of reported cases and fatalities [ 26 ]. In addition, the study included a limited number of countries or regions with lower income levels and low security index scores. The inclusion of these selected countries in the study itself demonstrates their relatively good medical level and certain epidemic monitoring capabilities. Meanwhile, considering the period from 2020 to the end of 2023, some organizations were accelerating the development and production of COVID-19 vaccines through the implementation of the COVID-19 Vaccines Global Access Facility (COVAX) to ensure fair and equitable access to vaccines for every country in the world [ 34 ]. This concept also holds promise for narrowing the disparity in income levels and medical capabilities among different countries. It is worth noting that, we also found that the high-income and high-security groups have a longer duration, indicating that their serotype changes are slower. This suggests that high income levels and high health standards may still be potential factors that require further exploration. The study found that the level of receptor affinity may not be related to serotype typing results. Additionally, there is no similarity in the affinity levels among variants within the same serotype. Some scholars speculate that immune evasion is constrained by receptor binding, and RBD will take “two-steps-forward and one-step backward”, when the binding affinity falls out of the optimal scope [ 35 ]. Further research on ACE2 affinity levels among different serotypes will be needed to investigate this phenomenon in the future. Based on the serological patterns of SARS-CoV-2, the median duration of serotype Ⅳ's epidemic has already exceeded 70 weeks, indicating that subsequent serotypes will continue to have annual epidemics. Additionally, the peak time of serotype epidemics is also extending, suggesting that new serotypes will not rapidly reach their highest proportion as in earlier stages; instead, there will be a simultaneous coexistence of multiple different serotypes during certain time periods. Considering the aforementioned temporal information, even if the vaccination frequency targeting this serotype is extended to an annual basis, it can still offer a certain level of population protection. This bears practical implications for amalgamating influenza and COVID-19 vaccines for yearly administration. This study also calculates the corresponding model and then predicts the possible time for the next serotype to appear. We used different methods for prediction and arrived at relatively consistent results, inferring that the emergence of Serotypes will likely occur by late 2024 to early 2025. Existing research focuses on calculating models for predicting future virus evolution of variants by combining large-scale neutralization assays [ 36 ], By integrating our study, better monitoring and early warning of SARS-CoV-2 variants may be achieved. As of the end of December 2024, no new serotypes have been defined [ 37 ]. However, considering the trend of a slowdown in the emergence of SARS-CoV-2 serotypes and the delay in disease surveillance, the new serotype may eventually be found even later (Fig. 3 ). In April 2025, the emergence of the new variant BA.3.2, which carries more than 50 mutation sites relative to the BA.3 lineage, has raised concerns about the potential epidemic it may cause. Research findings indicate that antigenic cartography based on pseudovirus neutralisation titres revealed BA.3.2 to be antigenically distinct from the JN.1 and XBB.1.5 lineages [ 38 ]. The latest research also confirmed that BA.3.2 might be able to evade the neutralizing antibodies used to treat COVID-19 or those induced by vaccination with significantly greater efficiency. In individuals who received the JN.1 booster shot, the variant's ability to evade antibodies is similar to or even stronger than that of the LP8.1.1 variant [ 39 ]. This variant strain indicates the emergence of the next SARS-CoV-2 serotype, which is close to the predicted time in our study. The increase in the activity level of SARS-CoV-2 infections indicates that we still need to rely on continuous monitoring of serotype changes to prevent the occurrence of antigen drift. We speculate that new viruses may ultimately replace the original virus (Supplement Fig. 8). This suggests that we may need to rely on continuous monitoring of serotype changes to prevent the occurrence of antigen drift. Our analysis has several limitations. First, variant proportions may be inaccurate due to limited sequencing coverage, potentially overrepresenting newly identified or closely monitored variants. Additionally, this study does not cover all countries, so further research is needed to address global variation. Second, our findings rely on adequate diagnostic testing rates and representative sampling, which are particularly limited in low income and middle-income countries, introducing potential spatiotemporal biases and delaying detection of emerging serotypes [ 40 ]. Meanwhile, when exploring the relationship between serotype and ACE2 affinity, only a limited number of corresponding variants provide information on affinity levels which makes it difficult to reflect all the information. Due to the limitations of the data, future studies should incorporate more representative datasets to improve reliability in forecasting. Third, the actual timing of serotype outbreaks likely precedes our calculated dates due to delays in sequence deposits, with inconsistent data release intervals across countries causing gaps. Despite discussions on data feasibility, subjective decisions on time points remain, and our data may not fully capture local serotype incidence. Additionally, some SARS-CoV-2 strains lack clear serotype classifications in the database, which may influence results. Broader factors such as policy, demographics, geography, vaccination rates, and herd immunity also likely impact serotype prevalence. Finally, current laboratory evidence of SARS-CoV-2 serotypes is based on experimental animals, but humans have a more complex immune background. Future studies need to explore the classification principles of SARS-CoV-2 serotypes and the impact factors of their epidemics in great depth. Conclusions Although COVID-19 is no longer a PHEIC, it remains imperative to maintain vigilance in monitoring its variants. This study offers a novel perspective on SARS-CoV-2 epidemics by incorporating the concept of SARS-CoV-2 serotype classification, elucidating the epidemiological characteristics of different serotypes worldwide. The findings provide fresh insights and valuable clues for future researchers engaged in variant surveillance, vaccine administration efforts, and unraveling the trajectory of COVID-19 mutations. Declarations Ethics approval and consent to participate There are no ethical concerns in this study. Competing interests The authors declare no competing interests. Funding The study was supported by the National Key Research and Development Program of China (2022YFC2604100), the National Natural Science Foundation of China (92269203) and the Major Project of Guangzhou National Laboratory (GZNL2025C01001). Author Contribution J.L. and G.F.G.: proposed and designed the study. X.S., Z.H., J.Z., and J.L.: collected the data. X.L., W.M., K.N. and J.S.: provided technical support. X.S., J.Z., J.S. and P.G.: analyzed and interpreted data. X.S. and J.L.: wrote the first draft of the manuscript. All authors reviewed and approved the content of the final version of the manuscript. J.L. is the guarantor. Acknowledgement We thank “Our World in Data” and “GISAID” database for providing open data information, as well as all the staff behind them. We also thank Professor Hongjie Yu for his constructive suggestions on this work. Data Availability The study used the information of SARS-CoV-2 variants circulating in various countries was obtained from Our World in Data (OWID) website. OWID website collated the data of SARS-CoV-2 variants in various regions from the GISAID database. All the data utilized were publicly released and accessible. 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Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe. Nature. 2020;584(7820):257–61. Medicines and healthcare products regulatory agency. Regulatory approval of Pfizer/BioNTech vaccine for COVID-19. 2020. https://www.gov.uk/government/publications/regulatory-approval-of-pfizer-biontech-vaccine-for-covid-19 . Accessed 10 Aug 2024. Dooling K, McClung N, Chamberland M, Marin M, Wallace M, Bell BP, et al. The advisory committee on immunization practices' interim recommendation for allocating initial supplies of COVID-19 vaccine - United States, 2020. MMWR Morb Mortal Wkly Rep. 2020;69(49):1857–59. Health sciences authority singapore. HSA grants interim authorisation for moderna COVID-19 vaccine in Singapore. 2021. https://www.hsa.gov.sg/announcements/press-release/hsa-grants-interim-authorisation-for-moderna-covid-19-vaccine-in-singapore . Accessed 10 Aug 2024. World Health Organization. COVAX, Working for global equitable access to COVID-19 vaccines. 2023. https://www.who.int/initiatives/act-accelerator/covax#cms . Accessed 10 Aug 2024. Li W, Xu Z, Niu T, Xie Y, Zhao Z, Li D, et al. Key mechanistic features of the trade-off between antibody escape and host cell binding in the SARS-CoV-2 Omicron variant spike proteins. Embo J. 2024;43(8):1484–98. Cao Y, Jian F, Wang J, Yu Y, Song W, Yisimayi A, et al. Imprinted SARS-CoV-2 humoral immunity induces convergent Omicron RBD evolution. Nature. 2023;614(7948):521–29. Du P, Li J, Kong T, Lu B, Li R, Gao GF, et al. Defining the serotypes of SARS-CoV-2 subvariants up to December, 2024. Lancet Microbe. 2025;6(8):101124. Guo C, Yu Y, Liu J, Jian F, Yang S, Song W, et al. Antigenic and virological characteristics of SARS-CoV-2 Variant BA.3.2, XFG, and NB.1.8.1. Lancet Infect Dis. 2025;25(7):e374–7. Zhang L, Kempf A, Nehlmeier I, Chen N, Stankov MV, Happle C, et al. Host cell entry and neutralisation sensitivity of SARS-CoV-2 BA.3.2. Lancet Microbe. 2025;6(11):101165. Han AX, Toporowski A, Sacks JA, Perkins MD, Briand S, van Kerkhove M, et al. SARS-CoV-2 diagnostic testing rates determine the sensitivity of genomic surveillance programs. Nat Genet. 2023;55(1):26–33. Additional Declarations No competing interests reported. Supplementary Files Table1.docx Table2.docx SupplementAppendix.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 17 Apr, 2026 Reviews received at journal 15 Apr, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviews received at journal 24 Mar, 2026 Reviewers agreed at journal 18 Mar, 2026 Reviewers agreed at journal 12 Mar, 2026 Reviewers agreed at journal 25 Feb, 2026 Reviews received at journal 19 Feb, 2026 Reviewers agreed at journal 19 Feb, 2026 Reviewers invited by journal 04 Feb, 2026 Editor invited by journal 03 Feb, 2026 Editor assigned by journal 02 Feb, 2026 Submission checks completed at journal 02 Feb, 2026 First submitted to journal 02 Feb, 2026 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-8761051","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":586103207,"identity":"e6078620-007a-4b1a-ac04-7dd1a4cacb0b","order_by":0,"name":"Xin Sun","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Sun","suffix":""},{"id":586103208,"identity":"3c4e50a1-837d-498a-bea2-6ed144dc739b","order_by":1,"name":"Zhuoran Huang","email":"","orcid":"","institution":"Columbia University","correspondingAuthor":false,"prefix":"","firstName":"Zhuoran","middleName":"","lastName":"Huang","suffix":""},{"id":586103209,"identity":"95b12664-f8fc-4730-b6ea-27a67dd2b855","order_by":2,"name":"Jiayu Sang","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Jiayu","middleName":"","lastName":"Sang","suffix":""},{"id":586103210,"identity":"877202b8-2738-45ce-949b-009af698f3f6","order_by":3,"name":"Peipei Guo","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Peipei","middleName":"","lastName":"Guo","suffix":""},{"id":586103211,"identity":"0b3b7003-e679-4851-b9ca-82e97f4f7788","order_by":4,"name":"Jiani Zhang","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Jiani","middleName":"","lastName":"Zhang","suffix":""},{"id":586103212,"identity":"92239d20-6755-4cfd-917f-e8a3f9141314","order_by":5,"name":"Jiaxin Li","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Jiaxin","middleName":"","lastName":"Li","suffix":""},{"id":586103213,"identity":"ddfd676c-b443-4601-b420-a1d1bf9d378f","order_by":6,"name":"Jiahui Si","email":"","orcid":"","institution":"Institute of Microbiology","correspondingAuthor":false,"prefix":"","firstName":"Jiahui","middleName":"","lastName":"Si","suffix":""},{"id":586103214,"identity":"49fa974f-e61d-4d51-a51a-df8e2c475b58","order_by":7,"name":"Wei Ma","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Ma","suffix":""},{"id":586103215,"identity":"e544d71b-9148-4b4c-8db4-032ba1068087","order_by":8,"name":"Kaida Ning","email":"","orcid":"","institution":"Peng Cheng Laboratory","correspondingAuthor":false,"prefix":"","firstName":"Kaida","middleName":"","lastName":"Ning","suffix":""},{"id":586103216,"identity":"0a4d9766-d442-478c-859d-3eabf9aefc9b","order_by":9,"name":"Xinxue Liu","email":"","orcid":"","institution":"University of Oxford","correspondingAuthor":false,"prefix":"","firstName":"Xinxue","middleName":"","lastName":"Liu","suffix":""},{"id":586103217,"identity":"6e7b879e-2eee-461b-8d70-a478798547e5","order_by":10,"name":"George Fu Gao","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"George","middleName":"Fu","lastName":"Gao","suffix":""},{"id":586103218,"identity":"0040188e-db5e-457b-a4cf-dedc71b264f4","order_by":11,"name":"Jun Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIie3OMQrCMBTG8a8U0iXYNaUQr1Bx8jYvu0MnJ4eCEDdnj+ERKgFdiouLQwdBqGtBcBHEVJ3bjoL5D+9BeD8I4HL9YAPfDgFwFmTAGXk3YV8iB9xeUy/y3WMpqC8JeIKJLpWOrsea5iXCZe7d0taPWRLpSul4mia0qyAK8uN1NzENISJmgFPz2ItEBeX0NBj2JWMmgm2mtEHSTVgKcTCS8akPtar4qFCLuI2Eodn4Ymb4cLm/1PW9lHJvtrc2Ygse4r3tD5sJeFk7sHn1h547L10ul+s/ewFGRUL/iaRgSQAAAABJRU5ErkJggg==","orcid":"","institution":"Guangzhou National Laboratory","correspondingAuthor":true,"prefix":"","firstName":"Jun","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2026-02-02 06:10:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8761051/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8761051/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102070684,"identity":"29e56abc-13ee-4ceb-8b54-4284173d97ab","added_by":"auto","created_at":"2026-02-06 19:30:12","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":575808,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe time periods of SARS-CoV-2 epidemics based on serotypes in 58 countries or regions.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. The time periods of epidemic for all SARS-CoV-2 serotypes in 58 countries with data available. The error bars represent the median of the 95% CI. Among them, TP\u003csub\u003epeak\u003c/sub\u003e represents the time required for a certain serotype to reach the highest proportion in a country or region. Given that serotype Ⅰ is the initial serotype and no relevant data was collected, its TP\u003csub\u003epeak\u003c/sub\u003e value cannot be calculated. B. Comparative analysis of the differences in epidemic periods among different serotypes (Considering the unique epidemic period for Serotype I as depicted in Figure 1A, stringent control measures were implemented globally during the initial stages of outbreak before vaccination commenced. Therefore, Serotype I was not included in the comparison of the same epidemic period). Boxplot centers indicate group median, bodies show interquartile range (IQR), and whiskers extend to the largest and smallest value. The *, **, ***, and **** indicate\u003cem\u003e P\u003c/em\u003e values less than 0.05, 0.01, 0.001 and 0.0001, respectively. C. \u0026nbsp;According to the results of Spearman's linear correlation analysis, the correlation coefficients for epidemic duration, peak timing, and alternation period were 0.755, 0.898, and 0.955 respectively. The straight line in the graph was obtained by fitting using the linear regression feature in GraphPad Prism 10.1.2. It can be obtained by calculated: TP\u003csub\u003edura\u003c/sub\u003e =18.157X+4.378, TP\u003csub\u003epeak\u003c/sub\u003e =10.152X-13.696, TP\u003csub\u003ealter\u003c/sub\u003e =16.716X-21.724. The data were described in the Table 2.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8761051/v1/bb591820cb885defc291cf64.jpg"},{"id":102295815,"identity":"2292f94f-388c-4a67-b405-1ed39e7a8a19","added_by":"auto","created_at":"2026-02-10 10:15:09","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":323551,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePredictions were generated based on the epidemic interval time for different SARS-CoV-2 serotypes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Distribution of\u003cstrong\u003e \u003c/strong\u003ethe interval between the epidemic onsets (TP\u003csub\u003einter\u003c/sub\u003e) for different SARS-CoV-2 serotypes. TP\u003csub\u003einter\u003c/sub\u003e was used to constructed a second-order polynomial curve fitting calculation to estimate the emergence time of new serotypes. The second-order polynomial equation is: Y =9.778-9.449X+3.454X\u003csup\u003e2\u003c/sup\u003e, where X represents the Xth serotype. (e.g., X=6 represents the 6\u003csup\u003eth\u003c/sup\u003e serotype, the corresponding Y is the interval between serotype Ⅵ and serotype Ⅶ). By computing the time difference between predicted Serotype Ⅶ and current Serotype Ⅵ as 77.42 weeks (95%CI：68.09，86.76), in conjunction with September 11th, 2023 being identified as the median epidemic start date for Serotype Ⅵ, we estimated that emergence of the new serotype may transpire within 2025. Using the curve estimation function of the regression module in SPSS to calculate predicted values and 95% CI, where 95% CI serves as the prediction interval with a confidence level of 95%. B. By applying an Exponential curve fitting model for estimating new serotype emergence, the calculated time difference between predicted Serotype Ⅶ and current Serotype Ⅵ is 87.43 weeks. Considering September 11th, 2023, as the median epidemic start date for Serotype Ⅵ, the estimated emergence date falls around May 16th, 2025. This model's 95% confidence interval for the time difference is notably broad, spanning from 17.29 to 294.97 weeks, which translates to a wide emergence window between January 9th, 2024, and April 22nd, 2029, reflecting significant uncertainty in the exponential model's long-term projection. Using R to calculate and plot the graph. \u0026nbsp;C. Utilizing a Logistic curve fitting calculation to estimate the emergence time of new serotypes, the time difference between predicted Serotype Ⅶ and current Serotype Ⅵ was computed as 76.87 weeks. In conjunction with September 11th, 2023, being identified as the median epidemic start date for Serotype Ⅵ, this indicates an estimated emergence around March 2nd, 2025. The associated 95% confidence interval for this time difference ranges from 69.4 to 91.32 weeks, suggesting the new serotype's emergence may transpire within January 10th, 2025, to June 12th, 2025. Using R to calculate and plot the graph.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8761051/v1/e7e7956a19038664ea3a3238.jpg"},{"id":102070685,"identity":"03d09e8f-677a-4f4b-8ed9-78d1a37ab0f7","added_by":"auto","created_at":"2026-02-06 19:30:12","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":161893,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe schema graph of the distribution of the onset points of the epidemic for Serotype Ⅱ-Ⅶ.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDifferent colors represent different SARS-CoV-2 serotypes. Through figure 3 predictive methods for the epidemic of serotypes, the onset time of potential Serotype Ⅶ may emerge in 2025 or even later. The emergence time of new serotypes keeps slowing down, but special events still need to be noted. Continuous virus monitoring is required to prevent the widespread transmission of new mutations. Other prediction methods of this study are detailed in the supplementary materials.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8761051/v1/557af923049781ba3e117005.jpg"},{"id":102298917,"identity":"b410505b-7428-4a42-8463-f240aa47ba2e","added_by":"auto","created_at":"2026-02-10 11:01:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1988955,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8761051/v1/e6ef37ef-b812-4743-975e-58da4902f472.pdf"},{"id":102295736,"identity":"8fc7ce64-be48-4023-b1d2-23dc58caa764","added_by":"auto","created_at":"2026-02-10 10:14:30","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":18299,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8761051/v1/2a69e98b219e7626f38a126e.docx"},{"id":102295710,"identity":"0a7bc0d6-8276-4c06-b649-47cf6689f690","added_by":"auto","created_at":"2026-02-10 10:14:10","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19314,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-8761051/v1/55a159d86bb1a69c76e24137.docx"},{"id":102070688,"identity":"52dae8eb-2d9c-4181-a76b-6de87d77d589","added_by":"auto","created_at":"2026-02-06 19:30:12","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2750680,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementAppendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-8761051/v1/4cdf3e67cb8b45dbd680e85c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Emergence of novel SARS-CoV-2 variants keeps slowing down","fulltext":[{"header":"Background","content":"\u003cp\u003eWith the consideration of a decline in the associated human health risks, on May 5, 2023, the World Health Organization (WHO) declared the report that COVID-19 had transitioned from a Public Health Emergency of International Concern (PHEIC) to a persistent and ongoing health concern [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, the ongoing mutation and evolution of SARS-CoV-2 strains persist. In the past period of time, new variants including JN.1, KP.3.1.1, XEC, LP.8.1, NB.1.8.1 and XFG have emerged, each with distinct viral characteristics and under continuous surveillance by WHO [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe WHO has classified the variants of SARS-CoV-2 as variant of concern, variant of interest, and variant under monitoring [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This classification method has achieved the purpose of monitoring, warning, and mitigating the public health risks of different SARS-CoV-2 variants. However, through individual variant analysis, it is challenging to ascertain the transmission patterns among different variants and generalize its application across diverse regions and populations. This challenge arises from the concurrent circulation of multiple variant strains across global regions. These complex characteristics make it even more difficult to predict the upcoming wave of infections [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe serotype of a pathogen is defined as a distinct variant within a microbial species, characterized by specific immune reactions mainly relying on the surface antigens. Antibodies generated against one serotype typically exhibit limited efficacy against other serotypes [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The serotype concept has been used for the classification of poliovirus (PV), dengue virus (DENV), and other various pathogens, which benefits the virus surveillance, vaccine administration, etc. Poliovirus can be classified into three different serotypes, which are distinguished by the antigenic sites in the viral capsid proteins [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. All three serotypes of poliovirus cause paralytic disease [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Four serotypes (DEN-1 to DEN-4) of DENV [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], are also key factors influencing the infection severity and the vaccine development [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDifferent SARS-CoV-2 variants can also be categorized into different serotypes. In early 2022, it was proposed to classify the prototype strain and early variants of concern (VOCs) up to Delta as Serotype 1, while the Omicron variants as Serotype 2 due to their distinct pathological, structural, and antigenic features compared to Serotype 1 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Subsequently, a serotype analysis study was conducted using human sera obtained from individuals who had recovered from initial infections. The currently-known human-infecting SARS-related coronaviruses were ultimately classified into three distinct serotypes, i.e. SARS-CoV-1, ancestral SARS-CoV-2, and the SARS-CoV-2 Omicron variants [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. According to the similarity of RBD (Receptor-binding domain) antigenicity, Wang et al. further categorized 23 representative variant strains into five serotypes (Serotype Ⅰ to Ⅴ) based on the cross-neutralization levels between pseudoviruses and RBD mRNA vaccine immunized mouse antisera to the existing dominant strains [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Afterwards, BA2.86 was further updated as Serotype Ⅵ [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Recently, consistent results were obtained in subsequent classification studies using the spike (S) protein as the immunogen [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFocusing on the distribution and epidemiological patterns of various variants [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], the characteristics of the COVID-19 pandemic were investigated, including spatial-temporal trends, population group features [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], as well as the dynamics of transmission models [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, how the novel concepts of SARS-CoV-2 serotypes could shed light on epidemic patterns of COVID-19 is still unknown.\u003c/p\u003e \u003cp\u003eSerotype classification holds significant importance in terms of epidemiology, disease prevention and control, as well as vaccine selection. In this study, combined with evidence from serotype classification, we analyzed relevant data and information on the SARS-CoV-2 epidemic situation in different countries, revealing the epidemiological patterns of SARS-CoV-2 from a fresh perspective.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source\u003c/h2\u003e \u003cp\u003eThe information of SARS-CoV-2 variants circulating in various countries was obtained from Our World in Data (OWID) website, updated from March 2021 to May 2024 [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The OWID database was updated every two weeks, using the release data of each SARS-CoV-2 variant in GISAID database [\u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInclusion standards\u003c/h3\u003e\n\u003cp\u003eThe OWID database used in this study includes data information from 125 countries or regions. Due to the lack of consistent reporting on variant strain proportions every two weeks across all countries or regions, significant data gaps exist in certain areas. To ensure adequate time period information for analysis and maintain data quality, our study mandated that selected countries provide at least two sets of serotype data during a specified prevalence period, encompassing the duration of the epidemic, peak times, or alternating periods with at least two serotypes. We ultimately used 58 countries or regions for serotype prevalence analysis based on the quality of the data.\u003c/p\u003e \u003cp\u003eAccording to the updated data as of May 3rd from the OWID website [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The database includes 19 dominantly prevalent variants of SARS-CoV-2, namely Alpha、Beta、Gamma、Delta、BA.1、BA.2、BA.2.12.1、BA.4、BA.5、BQ.1、XBB、XBB.1.5、XBB.1.16、XBB.1.9、XBB.2.3、EG.5.1、XBB.1.5.70、HK.3 and BA.2.86. The serotype classification criteria refer to the classification content in previous studies [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Based on existing evidence and the S protein epitope information published by GISAID [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], the above strains can be classified into six serotypes (supplement Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), including Serotype Ⅰ (Alpha、Beta、Gamma、Delta), Serotype Ⅱ (BA.1), Serotype Ⅲ(BA.2、BA.2.12.1), Serotype Ⅳ (BA.4、BA.5、BQ.1), Serotype Ⅴ (XBB、XBB.1.5、XBB.1.16、XBB.1.9、XBB.2.3、EG.5.1、XBB.1.5.70、HK.3) and Serotype Ⅵ (BA.2.86).\u003c/p\u003e \u003cp\u003e \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 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLinear regression epidemic periods between SARS-CoV-2 serotypes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTP\u003csub\u003edura\u003c/sub\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTP\u003csub\u003epeak\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTP\u003csub\u003ealter\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTP\u003csub\u003einter\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoefficient\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.157***\u003c/p\u003e \u003cp\u003e(1.760)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.152***\u003c/p\u003e \u003cp\u003e(0.426)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.716***\u003c/p\u003e \u003cp\u003e(0.388)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.984***\u003c/p\u003e \u003cp\u003e(0.387)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.378\u003c/p\u003e \u003cp\u003e(5.094)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-13.696***\u003c/p\u003e \u003cp\u003e(1.436)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-21.724***\u003c/p\u003e \u003cp\u003e(1.348)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-26.018***\u003c/p\u003e \u003cp\u003e(1.320)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted\u0026nbsp;R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u0026nbsp;Statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106.442***\u003c/p\u003e \u003cp\u003e(df\u0026thinsp;=\u0026thinsp;1; 101)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e569.156***\u003c/p\u003e \u003cp\u003e(df\u0026thinsp;=\u0026thinsp;1; 167)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1856.198***\u003c/p\u003e \u003cp\u003e(df\u0026thinsp;=\u0026thinsp;1; 172)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1308.476***\u003c/p\u003e \u003cp\u003e(df\u0026thinsp;=\u0026thinsp;1; 173)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003ea\u003c/sup\u003e The duration of the time period of epidemic (TP\u003csub\u003edura\u003c/sub\u003e), the peak time period of epidemic (TP\u003csub\u003epeak\u003c/sub\u003e), the alternation time period of epidemic (TP\u003csub\u003ealter\u003c/sub\u003e) and the interval between the epidemic onsets (TP\u003csub\u003einter\u003c/sub\u003e). Considering the unique epidemic period for Serotype I, stringent control measures were implemented globally during the initial stages of outbreak before vaccination commenced. Therefore, Serotype I was not included in the comparison of the same epidemic period. Meanwhile, for TP\u003csub\u003edura\u003c/sub\u003e, as there is no available termination point for Serotypes V and Ⅵ, we consider the duration of Serotypes II-Ⅳ as a benchmark; For TP\u003csub\u003epeak\u003c/sub\u003e, to exclude the influence of single-country values for Serotype Ⅵ during its peak time, we select the peak times for Serotypes Ⅱ-V. For TP\u003csub\u003ealter\u003c/sub\u003e, since Serotype Ⅶ has not yet emerged, the prevailing alternation period of Serotypes Ⅱ-V was chosen. For TP\u003csub\u003einter\u003c/sub\u003e, it can be used to predict the emerging time of a potential new serotype for SARS-CoV-2, which can be used to calculate such as TP\u003csub\u003einter\u003c/sub\u003e \u003csup\u003eI\u003c/sup\u003e= T\u003csub\u003eonset\u003c/sub\u003e \u003csup\u003eⅡ\u003c/sup\u003e - T\u003csub\u003eonset\u003c/sub\u003e \u003csup\u003eI\u003c/sup\u003e. To eliminate the influence of Serotype I, we selected data from serotypes Ⅱ-Ⅵ to calculate TP\u003csub\u003einter\u003c/sub\u003e \u003csup\u003eⅡ\u003c/sup\u003e-TP\u003csub\u003einter\u003c/sub\u003e \u003csup\u003eⅥ\u003c/sup\u003e.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003eb\u003c/sup\u003e TP\u003csub\u003edura\u003c/sub\u003e =18.157X\u0026thinsp;+\u0026thinsp;4.378, TP\u003csub\u003epeak\u003c/sub\u003e =10.152X-13.696, TP\u003csub\u003ealter\u003c/sub\u003e =16.716X-21.724, the X indicates the serotype of SARS-CoV-2. TP\u003csub\u003einter\u003c/sub\u003e = 13.984X-26.018, the X represents the serotypes of the time difference between X and X\u0026thinsp;+\u0026thinsp;1. (Ex. When X\u0026thinsp;=\u0026thinsp;6, it means the time difference between serotypes Ⅵ and Ⅶ).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eDefinition of time data\u003c/h3\u003e\n\u003cp\u003eThe prevalence of variant strains in each country is updated biweekly in OWID database, based on the deposition data of genome sequences in GISAID. The deposition data for each variant strain released during each update is classified and organized into corresponding serotype proportion data by deposition date. These data were used for facilitating determination of relevant \u0026ldquo;time points\u0026rdquo; through compilation of serotype proportions. Drawing from our meticulously organized database, we herein define four distinct \u0026ldquo;time points\u0026rdquo; and three corresponding \u0026ldquo;time periods\u0026rdquo; as follows:\u003c/p\u003e \u003cp\u003eThe onset time point of the epidemic (T\u003csub\u003eonset\u003c/sub\u003e): The onset time point of the epidemic is defined as the date when each country or region initially reports a proportion of variant strains with corresponding serotypes exceeding 0.01%. Since the pre-Omicron variants belong to Serotype I, the onset time point of Serotype I epidemic will be based on the date when each country or region officially announced the discovery of their first SARS-CoV-2 case.\u003c/p\u003e \u003cp\u003eThe end time point of the epidemic (T\u003csub\u003eend\u003c/sub\u003e): The end time point of the epidemic is defined as when the proportion of the original prevalent serotype reported by each country or region for the first time falls below 0.01%. To ensure accuracy and avoid errors caused by missed or false reports, or disappearance of time points, this time point should also satisfy the condition that in subsequent two depositions (one month time), the proportion of original prevalent serotype remains still less than 0.01%.\u003c/p\u003e \u003cp\u003eThe peak time point of the epidemic (T\u003csub\u003epeak\u003c/sub\u003e): The peak time point of the epidemic is defined as the date when the proportion of initial serotype variants reaches its highest point in each country or region.\u003c/p\u003e \u003cp\u003eThe alternation time point of epidemic (T\u003csub\u003ealter\u003c/sub\u003e): The alternation time point of epidemic is defined as the date at which the proportion of newly emerging serotype variants surpasses that of existing prevalent serotype variants.\u003c/p\u003e \u003cp\u003eAccording to the designated \u0026ldquo;time points\u0026rdquo; for the epidemic, three distinct \u0026ldquo;time periods\u0026rdquo; are identified and measured in weeks.\u003c/p\u003e \u003cp\u003eThe duration \u0026ldquo;time period\u0026rdquo; of epidemic (TP\u003csub\u003edura\u003c/sub\u003e): TP\u003csub\u003edura\u003c/sub\u003e= T\u003csub\u003eend\u003c/sub\u003e - T\u003csub\u003eonset\u003c/sub\u003e, represents the length of time a certain serotype will prevail in a country or region.\u003c/p\u003e \u003cp\u003eThe peak \u0026ldquo;time period\u0026rdquo; of epidemic (TP\u003csub\u003epeak\u003c/sub\u003e): TP\u003csub\u003epeak\u003c/sub\u003e = T\u003csub\u003epeak\u003c/sub\u003e - T\u003csub\u003eonset\u003c/sub\u003e, represents the duration it takes for a certain serotype to reach its highest proportion in a country or region.\u003c/p\u003e \u003cp\u003eThe alternation \u0026ldquo;time period\u0026rdquo; of epidemic (TP\u003csub\u003ealter\u003c/sub\u003e): TP\u003csub\u003ealter\u003c/sub\u003e= T\u003csub\u003ealter\u003c/sub\u003e - T\u003csub\u003eonset\u003c/sub\u003e, represents how long a certain serotype will be replaced by the next serotype, no longer prevailing as the dominant serotype.\u003c/p\u003e\n\u003ch3\u003eData input and quality assurance\u003c/h3\u003e\n\u003cp\u003eDuring the data extraction process, a dual-entry data input procedure is implemented to establish an Excel database for SARS-CoV-2 serotypes based on relevant definitions. In case of any discrepancies during result consolidation, they will be resolved through discussions between two researchers or referred to a third researcher for resolution. All researchers participating in this study have the right to inspect the raw data, verify and correct any missing or biased data, to ensure the quality of the dataset.\u003c/p\u003e\n\u003ch3\u003eClassification standards for countries and regions\u003c/h3\u003e\n\u003cp\u003eAccording to the World Bank's classification standards based on income levels as of July 1, 2024 [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], the countries or regions involved in this study are divided into three groups: high-income group, upper-middle-income group, and lower-middle-income group. Meanwhile, the Global Health Security Index updated and released in 2021 by Johns Hopkins University, Nuclear Threat Initiative (NTI), and other institutions, serves as a benchmark for assessing the health security of different countries [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This study uses a scoring system with a maximum score of 100 points and divides each 20-point range into one level. The rating range of the countries or regions involved in this study is between 20 and 80 points, with levels of high security index (60\u0026ndash;80), medium security index (40\u0026ndash;60), and low security index (20\u0026ndash;40).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDescriptive epidemiological research methods were employed to analyze the distribution of different SARS-CoV-2 serotypes in various regions since the outbreak. The median time point was used to describe the overall distribution. Non-matching non-parametric tests were utilized to compare distribution of different SARS-CoV-2 serotypes among income levels or global health security capacities (Mann-Whitney test for two groups and the Kruskal-Wallis test for three groups). Dunn's Test was used to perform pairwise comparisons among three sets of data. Spearman correlation analysis was applied to explore the correlation between serotypes and epidemic timelines. Univariate linear regression and curve regression were used to predict potential time for the future serotypes.\u003c/p\u003e \u003cp\u003eData organization was conducted using Excel (Microsoft 2019), and statistical analysis was performed using SPSS version 26.0 (IBM Corp. Released 2019. Armonk, NY, United States: IBM Corp), R (4.4.1) and GraphPad Prism (10.1.2) software. We used the PowerPoint (Microsoft 2019) and GraphPad Prism (10.1.2) for all drawings in our study. The merging and adjustment of images were done using AI software (Adobe Illustrator 2024). A two-sided \u003cem\u003eP\u003c/em\u003e value of less than 0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eThe time point and time period of SARS-CoV-2 serotype epidemics\u003c/h2\u003e \u003cp\u003eThe global incidence situations in the past nearly five years are complicated, with a significant increase in the number of cases in early 2022 and 2023. The median T\u003csub\u003eonset\u003c/sub\u003e for Serotype Ⅰ was February 2020, based on the data collected from the official announcements of different countries and regions (Supplement Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\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 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEpidemic time periods of SARS-CoV-2 serotypes.\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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTP\u003csub\u003edura\u003c/sub\u003e (week)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTP\u003csub\u003epeak\u003c/sub\u003e (week)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTP\u003csub\u003ealter\u003c/sub\u003e (week)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerotype I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(107,118.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(96.5,101)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerotype II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(30,46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(6,8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(12,16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerotype III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(58,74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(16,18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(24,26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerotype IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(60,82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(18,28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(42,52)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerotype V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(36.5,48.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(62,68)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerotype VI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003ea\u003c/sup\u003e The duration of the time period of epidemic (TP\u003csub\u003edura\u003c/sub\u003e), the peak time period of epidemic (TP\u003csub\u003epeak\u003c/sub\u003e), the alternation time period of epidemic (TP\u003csub\u003ealter\u003c/sub\u003e). \"NA\" represents the data were unavailable. The definitions of the TP\u003csub\u003edura\u003c/sub\u003e, TP\u003csub\u003epeak\u003c/sub\u003e and TP\u003csub\u003ealter\u003c/sub\u003e were described in the Methods.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003eb\u003c/sup\u003e The medians TP\u003csub\u003edura\u003c/sub\u003e, TP\u003csub\u003epeak\u003c/sub\u003e and TP\u003csub\u003ealter\u003c/sub\u003e were shown in the table with interquartile ranges in the brackets. \"*\" indicates that there is a data for only one country. The specific data were described in the Supplementary material (Supplement Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;4).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBased on data collected and organized from the OWID database, the median T\u003csub\u003eend\u003c/sub\u003e and T\u003csub\u003ealter\u003c/sub\u003e for Serotype Ⅰ were March and January 2022, respectively; for Serotype Ⅱ, the median T\u003csub\u003eonset\u003c/sub\u003e, T\u003csub\u003eend\u003c/sub\u003e, T\u003csub\u003epeak\u003c/sub\u003e, and T\u003csub\u003ealter\u003c/sub\u003e were December 2021, August 2022, January, and March, respectively; for Serotype Ⅲ, the median T\u003csub\u003eonset\u003c/sub\u003e, T\u003csub\u003eend\u003c/sub\u003e, T\u003csub\u003epeak\u003c/sub\u003e, and T\u003csub\u003ealter\u003c/sub\u003e were January 2022, March 2023, May 2022, and July 2022, respectively; for Serotype Ⅳ, the median T\u003csub\u003eonset\u003c/sub\u003e, T\u003csub\u003eend\u003c/sub\u003e, T\u003csub\u003epeak\u003c/sub\u003e, and T\u003csub\u003ealter\u003c/sub\u003e were April 2022, August 2023, September 2022, and February 2023, respectively. For Serotype Ⅴ, the median T\u003csub\u003eonset\u003c/sub\u003e, T\u003csub\u003epeak\u003c/sub\u003e, and T\u003csub\u003ealter\u003c/sub\u003e were September 2022, July 2023, and December 2023, respectively. The median T\u003csub\u003eonset\u003c/sub\u003e for Serotype Ⅵ was September 2023 (supplement Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; supplement Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;4).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on the median prevalence time period that can be calculated from the prevalence time nodes, the TP\u003csub\u003edura\u003c/sub\u003e and TP\u003csub\u003ealter\u003c/sub\u003e for Serotype Ⅰ were 111 weeks and 98 weeks, respectively; for Serotype Ⅱ, the TP\u003csub\u003edura\u003c/sub\u003e, TP\u003csub\u003epeak\u003c/sub\u003e and TP\u003csub\u003ealter\u003c/sub\u003e were 34 weeks, 8 weeks, and 14 weeks; for Serotype Ⅲ, the TP\u003csub\u003edura\u003c/sub\u003e, TP\u003csub\u003epeak\u003c/sub\u003e and TP\u003csub\u003ealter\u003c/sub\u003e were 64 weeks, 16 weeks and 26 weeks; for Serotype Ⅳ, the TP\u003csub\u003edura\u003c/sub\u003e, TP\u003csub\u003epeak\u003c/sub\u003e and TP\u003csub\u003ealter\u003c/sub\u003e were 72 weeks, 22 weeks and 44 weeks; for Serotype Ⅴ, the TP\u003csub\u003epeak\u003c/sub\u003e and TP\u003csub\u003ealter\u003c/sub\u003e were 45 weeks and 64 weeks (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eThe extending epidemic periods of SARS-CoV-2 serotypes\u003c/h2\u003e \u003cp\u003eA comparative analysis was conducted to examine the duration of different serotypes, based on epidemic times (TP\u003csub\u003edura\u003c/sub\u003e), peak times (TP\u003csub\u003epeak\u003c/sub\u003e), and alternation times (TP\u003csub\u003ealter\u003c/sub\u003e) using data from 58 countries or regions. Considering the unique epidemic period for Serotype I as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, stringent control measures were implemented globally during the initial stages of outbreak before vaccination commenced. Therefore, Serotype I was not included in the comparison of the same epidemic period.\u003c/p\u003e \u003cp\u003eThe results showed that there was an obvious increasing trend for the epidemic periods from Serotype Ⅱ to Serotype Ⅴ. Statistically significant differences of TP\u003csub\u003edura\u003c/sub\u003e, TP\u003csub\u003epeak\u003c/sub\u003e, and TP\u003csub\u003ealter\u003c/sub\u003e were found between the contiguous serotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Meanwhile, regarding the duration of epidemic, as there is no available termination point for Serotypes Ⅴ and Ⅵ, we consider the duration of Serotypes Ⅱ-Ⅳ as a benchmark; For the peak time of epidemic, to exclude the influence of single-country values for Serotype Ⅵ during its peak time, we select the peak times for Serotypes Ⅱ-Ⅴ. For the alternation time of epidemic, since Serotype Ⅶ has not yet emerged, the prevailing alternation period of Serotypes Ⅱ-Ⅴ was chosen.\u003c/p\u003e \u003cp\u003eBased on the selected criteria mentioned above, this study plotted scatter plots of different time periods for different serotypes. By analyzing the characteristics of these scatter plots, it was found that the TP\u003csub\u003edura\u003c/sub\u003e, TP\u003csub\u003epeak\u003c/sub\u003e, and TP\u003csub\u003ealter\u003c/sub\u003e exhibited certain linear relationships. According to the results of Spearman's linear correlation analysis, the correlation coefficients for epidemic duration, peak timing, and alternation period were 0.755, 0.898, and 0.955 respectively. This indicates that there is a linear correlation between serotype classification and epidemic time periods (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eThe potential impact factors on the epidemic dynamics of SARS-CoV-2 serotypes\u003c/h2\u003e \u003cp\u003eAccording to the World Bank's classification of countries based on different income levels [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], the 58 included countries or regions are categorized into three groups: high-income level (72\u0026middot;41%, 42/58), upper-middle-income level (22\u0026middot;41%, 13/58), and lower-middle-income level (5\u0026middot;17%, 3/58). There is no association between the TP\u003csub\u003edura\u003c/sub\u003e of different serotypes and income level grouping, although some time periods have differences between the income levels for certain serotypes (supplement Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAccording to the Global Health Security (GHS) Index,[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] the 58 countries or regions included in this study are categorized into three groups: high security index (32\u0026middot;76%, 19/58), medium security index (62\u0026middot;07%, 36/58), and low security index (5\u0026middot;17%, 3/58). Longer TP\u003csub\u003edura\u003c/sub\u003e and TP\u003csub\u003ealter\u003c/sub\u003e were observed in the high-security index group than the medium-security index group, especially for Serotypes Ⅲ (Supplement Fig.\u0026nbsp;4).\u003c/p\u003e \u003cp\u003eWe also plotted a scatter plot and curve regression for high-income level and upper-middle-income level in terms of income level grouping, as well as high security index and medium security index in terms of Global Health Security index (Supplement Fig.\u0026nbsp;5). It showed that higher-income and higher-safety-index groups have longer TP\u003csub\u003edura\u003c/sub\u003e from all data, indicating a certain degree of tail effect.\u003c/p\u003e \u003cp\u003eWe have summarized the levels of ACE2 (Angiotensin-converting enzyme 2) affinity for different variants based on the previously-reported data and categorized them into corresponding Serotypes Ⅰ-Ⅵ (Supplement Table\u0026nbsp;6) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The affinity levels of strains within Serotypes Ⅰ-Ⅵ did not show obvious trend for fluctuations, and association with serotypes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eThe prediction of the potential emerging of a new SARS-CoV-2 serotype\u003c/h2\u003e \u003cp\u003eThe linear trends of the epidemic time periods, i.e. TP\u003csub\u003edura\u003c/sub\u003e, TP\u003csub\u003epeak\u003c/sub\u003e, and TP\u003csub\u003ealter\u003c/sub\u003e enable us to predict the emerging time of a potential new serotype for SARS-CoV-2, named Serotype Ⅶ. To achieve this, we organized the existing temporal information node data and analyzed the differences in epidemic onset times among these serotypes. We defined the interval between epidemic onsets as TP\u003csub\u003einter\u003c/sub\u003e and identified a related relationship based on scatter plots and Spearman's linear correlation analysis results (correlation coefficient\u0026thinsp;=\u0026thinsp;0.949).\u003c/p\u003e \u003cp\u003eTo predict the possible occurrence time of the next serotype, TP\u003csub\u003einter\u003c/sub\u003e was used to construct a simple linear regression equation to estimate the emergence time of new serotypes (Supplement Table\u0026nbsp;5). By computing the time difference between predicted Serotype Ⅶ and current Serotype Ⅵ as 57.89 weeks with a 95% confidence interval of (55.64,60.14), in conjunction with September 11th, 2023 being identified as the median epidemic start date for Serotype Ⅵ, we converted the aforementioned confidence interval into specific dates and estimated that emergence of the new serotype may transpire within October 4th to November 4th, 2024 (Supplement Fig.\u0026nbsp;6A). Through using a second-order polynomial regression model, by computing the time difference between predicted Serotype Ⅶ and current Serotype Ⅵ as 77.42 weeks with a 95% confidence interval of (68.09, 86.76), we estimated that emergence of the new serotype may transpire within December 31st, 2024 to May 10th, 2025 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). At the same time, we also employed other models for comparison. The logarithmic model predicts Serotype Ⅶ emergence around August 11th, 2024 with relatively high temporal precision (Supplement Fig.\u0026nbsp;6B), whereas the exponential model projects a later and highly uncertain emergence near May 16th, 2025 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) and the logistic model suggests an intermediate prediction around March 2nd, 2025 with moderate confidence, collectively illustrating how model structure strongly shapes temporal inference (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eAccording to the different onset time points of serotypes, we also plotted relevant graphs (Supplement Fig.\u0026nbsp;7A). It is a characteristic that the onset time of the epidemic may show a curved distribution after Serotype Ⅱ. Therefore, we conducted another curve regression prediction methods based on the onset point of serotypes (Supplement Fig.\u0026nbsp;7B). The new serotype predicted using second and third-order polynomial regression is expected to emerge around October 2024 or March 2025.\u003c/p\u003e \u003cp\u003eFurthermore, the estimated TP\u003csub\u003edura\u003c/sub\u003e for Serotype Ⅴ is 95.16 weeks with a 95% confidence interval ranging from 86.96 to 103.36 weeks. Similarly, for Serotype Ⅵ, the estimated TP\u003csub\u003edura\u003c/sub\u003e is 113.32 weeks with a 95% confidence interval ranging from 101.77 to 124.87 weeks. This means the Serotype Ⅴ and Ⅵ may end in July 2024 and November 2025, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Serotype Ⅵ will reach its peak at approximately 47.22 weeks (95% confidence interval: 44.72 to 49.71), which is August 2024 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). As the same way, TP\u003csub\u003ealter\u003c/sub\u003e for Serotype Ⅵ is 78.57 weeks with a 95% confidence interval of (76.35, 80.80), which indicates Serotype Ⅵ may be replaced by the predicted Serotype Ⅶ at March, 2025 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, considering the trend of a slowdown in the emergence of SARS-CoV-2 serotypes, the new serotype may eventually emerge in March 2025 or even later.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eSerotype classification methods enable a refined understanding of viruses and their variants, which holds significant importance in terms of epidemiology, disease prevention and control, as well as vaccine selection [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The pre-Omicron variants were designated as Serotype Ⅰ, the epidemic duration and alternation of which were significantly longer compared to other serotypes, which we attributed to the stringent prevention and control measures implemented by various countries during the early stages of the outbreak [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. During a survey on the impact of major non-pharmaceutical interventions in 11 European countries from the start of the COVID-19 pandemic in 2020, it was found that these interventions had a significant effect in reducing virus transmission [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Meanwhile, from an immunological perspective, the approval of many countries' COVID-19 vaccines started from December 2020 to early 2021 [\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], so during the prevalence of Serotype Ⅰ, the impact of vaccine administration on variants was relatively minimal.\u003c/p\u003e \u003cp\u003eAfter further division of the included countries, it was found that there were only certain epidemic differences in serotypes among countries with different levels of global health security index. We hypothesize that this disparity may be attributed to the uneven representation of countries across different classification groups, as well as the divergence between the Global Health Security Index and actual capacities for public health prevention and control. Taking the United States as an illustrative example, despite securing the top rank in the Global Health Security Index, it continues to demonstrate a significant burden of reported cases and fatalities [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In addition, the study included a limited number of countries or regions with lower income levels and low security index scores. The inclusion of these selected countries in the study itself demonstrates their relatively good medical level and certain epidemic monitoring capabilities. Meanwhile, considering the period from 2020 to the end of 2023, some organizations were accelerating the development and production of COVID-19 vaccines through the implementation of the COVID-19 Vaccines Global Access Facility (COVAX) to ensure fair and equitable access to vaccines for every country in the world [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. This concept also holds promise for narrowing the disparity in income levels and medical capabilities among different countries. It is worth noting that, we also found that the high-income and high-security groups have a longer duration, indicating that their serotype changes are slower. This suggests that high income levels and high health standards may still be potential factors that require further exploration. The study found that the level of receptor affinity may not be related to serotype typing results. Additionally, there is no similarity in the affinity levels among variants within the same serotype. Some scholars speculate that immune evasion is constrained by receptor binding, and RBD will take \u0026ldquo;two-steps-forward and one-step backward\u0026rdquo;, when the binding affinity falls out of the optimal scope [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Further research on ACE2 affinity levels among different serotypes will be needed to investigate this phenomenon in the future.\u003c/p\u003e \u003cp\u003eBased on the serological patterns of SARS-CoV-2, the median duration of serotype Ⅳ's epidemic has already exceeded 70 weeks, indicating that subsequent serotypes will continue to have annual epidemics. Additionally, the peak time of serotype epidemics is also extending, suggesting that new serotypes will not rapidly reach their highest proportion as in earlier stages; instead, there will be a simultaneous coexistence of multiple different serotypes during certain time periods. Considering the aforementioned temporal information, even if the vaccination frequency targeting this serotype is extended to an annual basis, it can still offer a certain level of population protection. This bears practical implications for amalgamating influenza and COVID-19 vaccines for yearly administration.\u003c/p\u003e \u003cp\u003eThis study also calculates the corresponding model and then predicts the possible time for the next serotype to appear. We used different methods for prediction and arrived at relatively consistent results, inferring that the emergence of Serotypes will likely occur by late 2024 to early 2025. Existing research focuses on calculating models for predicting future virus evolution of variants by combining large-scale neutralization assays [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], By integrating our study, better monitoring and early warning of SARS-CoV-2 variants may be achieved.\u003c/p\u003e \u003cp\u003eAs of the end of December 2024, no new serotypes have been defined [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. However, considering the trend of a slowdown in the emergence of SARS-CoV-2 serotypes and the delay in disease surveillance, the new serotype may eventually be found even later (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In April 2025, the emergence of the new variant BA.3.2, which carries more than 50 mutation sites relative to the BA.3 lineage, has raised concerns about the potential epidemic it may cause. Research findings indicate that antigenic cartography based on pseudovirus neutralisation titres revealed BA.3.2 to be antigenically distinct from the JN.1 and XBB.1.5 lineages [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The latest research also confirmed that BA.3.2 might be able to evade the neutralizing antibodies used to treat COVID-19 or those induced by vaccination with significantly greater efficiency. In individuals who received the JN.1 booster shot, the variant's ability to evade antibodies is similar to or even stronger than that of the LP8.1.1 variant [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. This variant strain indicates the emergence of the next SARS-CoV-2 serotype, which is close to the predicted time in our study. The increase in the activity level of SARS-CoV-2 infections indicates that we still need to rely on continuous monitoring of serotype changes to prevent the occurrence of antigen drift. We speculate that new viruses may ultimately replace the original virus (Supplement Fig.\u0026nbsp;8). This suggests that we may need to rely on continuous monitoring of serotype changes to prevent the occurrence of antigen drift.\u003c/p\u003e \u003cp\u003eOur analysis has several limitations. First, variant proportions may be inaccurate due to limited sequencing coverage, potentially overrepresenting newly identified or closely monitored variants. Additionally, this study does not cover all countries, so further research is needed to address global variation. Second, our findings rely on adequate diagnostic testing rates and representative sampling, which are particularly limited in low income and middle-income countries, introducing potential spatiotemporal biases and delaying detection of emerging serotypes [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Meanwhile, when exploring the relationship between serotype and ACE2 affinity, only a limited number of corresponding variants provide information on affinity levels which makes it difficult to reflect all the information. Due to the limitations of the data, future studies should incorporate more representative datasets to improve reliability in forecasting. Third, the actual timing of serotype outbreaks likely precedes our calculated dates due to delays in sequence deposits, with inconsistent data release intervals across countries causing gaps. Despite discussions on data feasibility, subjective decisions on time points remain, and our data may not fully capture local serotype incidence. Additionally, some SARS-CoV-2 strains lack clear serotype classifications in the database, which may influence results. Broader factors such as policy, demographics, geography, vaccination rates, and herd immunity also likely impact serotype prevalence. Finally, current laboratory evidence of SARS-CoV-2 serotypes is based on experimental animals, but humans have a more complex immune background. Future studies need to explore the classification principles of SARS-CoV-2 serotypes and the impact factors of their epidemics in great depth.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eAlthough COVID-19 is no longer a PHEIC, it remains imperative to maintain vigilance in monitoring its variants. This study offers a novel perspective on SARS-CoV-2 epidemics by incorporating the concept of SARS-CoV-2 serotype classification, elucidating the epidemiological characteristics of different serotypes worldwide. The findings provide fresh insights and valuable clues for future researchers engaged in variant surveillance, vaccine administration efforts, and unraveling the trajectory of COVID-19 mutations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThere are no ethical concerns in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThe study was supported by the National Key Research and Development Program of China (2022YFC2604100), the National Natural Science Foundation of China (92269203) and the Major Project of Guangzhou National Laboratory (GZNL2025C01001).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eJ.L. and G.F.G.: proposed and designed the study. X.S., Z.H., J.Z., and J.L.: collected the data. X.L., W.M., K.N. and J.S.: provided technical support. X.S., J.Z., J.S. and P.G.: analyzed and interpreted data. X.S. and J.L.: wrote the first draft of the manuscript. All authors reviewed and approved the content of the final version of the manuscript. J.L. is the guarantor.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eWe thank \u0026ldquo;Our World in Data\u0026rdquo; and \u0026ldquo;GISAID\u0026rdquo; database for providing open data information, as well as all the staff behind them. We also thank Professor Hongjie Yu for his constructive suggestions on this work.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe study used the information of SARS-CoV-2 variants circulating in various countries was obtained from Our World in Data (OWID) website. OWID website collated the data of SARS-CoV-2 variants in various regions from the GISAID database. 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Nat Genet. 2023;55(1):26\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e\u003c/ol\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-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"SARS-CoV-2, serotypes, prevalence, slow, forecast","lastPublishedDoi":"10.21203/rs.3.rs-8761051/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8761051/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAlthough COVID-19 pandemic has no longer been classified as an international public health emergency of concern after May 2023, multiple variants with different characteristics keep emerging. Given the complex spatial-temporal characteristics of epidemics, responding to potential changes in variants is a significant public health challenge. Serotypes are defined as unique variants within specific immune response characteristics, which are used in the study of various pathogens. The sero-epidemiological features of COVID-19 may offer new insight into public health.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eBased on the global SARS-CoV-2 genome deposition data in Our World in Data (OWID) based on GISAID from March 1, 2021 to May 3, 2024, we analyzed the epidemic features of different variants based on the serotype concept and combined with cross-sectional studies.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn the dataset comprising variants from 58 countries or regions with complete information, we calculated the median duration times of epidemic, the median epidemic peak times for Serotypes Ⅱ-Ⅴ and the median alternation times of epidemic for Serotypes I-Ⅱ to Ⅴ-Ⅵ respectively. There are linear relationships for these time period between the epidemics of Serotypes Ⅱ, Ⅲ, Ⅳ and Ⅴ, except the longer duration time for Serotype I. By constructing a simple linear regression and curve regression equation, the emerging of a new serotype can be predicted with the time around 2025, or even later.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe gradually-increased prevalence periods for SARS-CoV-2 serotypes except Serotype I may suggest a slowing down mutation rate. Understanding the epidemic time of different serotypes can provide insight into the surveillance and forecasting of COVID-19.\u003c/p\u003e","manuscriptTitle":"Emergence of novel SARS-CoV-2 variants keeps slowing down","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-06 19:30:07","doi":"10.21203/rs.3.rs-8761051/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-17T12:56:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-15T21:50:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"41860026167902396063458440522340682125","date":"2026-04-13T11:23:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"295909893384579283567394952099749324783","date":"2026-04-08T16:00:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-24T15:25:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"150619256761428005703523511613437165437","date":"2026-03-18T09:52:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"59347031395321417722512654317015409624","date":"2026-03-12T11:32:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"228105495010343499197418238855065748014","date":"2026-02-25T11:06:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-19T13:20:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"254588898319954632290621194183603630213","date":"2026-02-19T12:08:54+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-04T05:41:04+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-03T10:53:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-03T03:50:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-03T03:48:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-02-02T05:48:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"62db9b0a-9f25-4742-ae73-f74eef221c31","owner":[],"postedDate":"February 6th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-13T01:09:28+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-06 19:30:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8761051","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8761051","identity":"rs-8761051","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}