Detection of toxigenic M1UK lineage group A Streptococcus clones in Taiwan.

BACKGROUND: A new sublineage of emm1 group A Streptococcus (GAS), M1UK, has emerged in Europe, North America, and Australia. Notably, a significant portion of emm1 isolates in Asia, particularly in Hong Kong and mainland China, acquired scarlet fever-associated prophages following the 2011 Hong Kong scarlet fever outbreak. However, the presence of the M1UK sublineage has not yet been detected in Asia. METHODS: This study included 181 GAS isolates (2011-2021). The emm type of these isolates were determined, and 21 emm1 isolates from blood or pleural fluid (2011-2021) and 10 emm1 isolates from throat swabs (2016-2018) underwent analysis. The presence of the scarlet fever-associated prophages and the specific single nucleotide polymorphisms of the M1UK clone were determined by polymerase chain reaction and the genome sequencing. RESULTS: The M1UK lineage strains from throat swab and blood samples were identified. One of the M1UK strain in Taiwan carried the scarlet fever-associated prophage and therefore acquired the ssa, speC, and spd1 toxin repertoire. Nonetheless, the increase of M1UK was not observed until 2021, and there was a reduction in the diversity of emm types in 2020-2021, possibly due to the COVID-19 pandemic restriction policies in Taiwan. CONCLUSIONS: Our results suggested that the M1UK lineage clone has introduced in Taiwan. In Taiwan, the COVID-19 restrictions were officially released in March 2023; therefore, it would be crucial to continuously monitor the M1UK expansion and its related diseases in the post COVID-19 era.

Epidemiological characteristics of seven notifiable respiratory infectious diseases in the mainland of China: an analysis of national surveillance data from 2017 to 2021.

BACKGROUND: Respiratory infectious diseases (RIDs) remain a pressing public health concern, posing a significant threat to the well-being and lives of individuals. This study delves into the incidence of seven primary RIDs during the period 2017-2021, aiming to gain deeper insights into their epidemiological characteristics for the purpose of enhancing control and prevention strategies. METHODS: Data pertaining to seven notifiable RIDs, namely, seasonal influenza, pulmonary tuberculosis (PTB), mumps, scarlet fever, pertussis, rubella and measles, in the mainland of China between 2017 and 2021 were obtained from the National Notifiable Disease Reporting System (NNDRS). Joinpoint regression software was utilized to analyze temporal trends, while SaTScan software with a Poisson probability model was used to assess seasonal and spatial patterns. RESULTS: A total of 11,963,886 cases of the seven RIDs were reported during 2017-2021, and yielding a five-year average incidence rate of 170.73 per 100,000 individuals. Among these RIDs, seasonal influenza exhibited the highest average incidence rate (94.14 per 100,000), followed by PTB (55.52 per 100,000), mumps (15.16 per 100,000), scarlet fever (4.02 per 100,000), pertussis (1.10 per 100,000), rubella (0.59 per 100,000), and measles (0.21 per 100,000). Males experienced higher incidence rates across all seven RIDs. PTB incidence was notably elevated among farmers and individuals aged over 65, whereas the other RIDs primarily affected children and students under 15 years of age. The incidences of PTB and measles exhibited a declining trend from 2017 to 2021 (APC = -7.53%, P = 0.009; APC = -40.87%, P = 0.02), while the other five RIDs peaked in 2019. Concerning seasonal and spatial distribution, the seven RIDs displayed distinct characteristics, with variations observed for the same RIDs across different regions. The proportion of laboratory-confirmed cases fluctuated among the seven RIDs from 2017 to 2021, with measles and rubella exhibiting higher proportions and mumps and scarlet fever showing lower proportions. CONCLUSIONS: The incidence of PTB and measles demonstrated a decrease in the mainland of China between 2017 and 2021, while the remaining five RIDs reached a peak in 2019. Overall, RIDs continue to pose a significant public health challenge. Urgent action is required to bolster capacity-building efforts and enhance control and prevention strategies for RIDs, taking into account regional disparities and epidemiological nuances. With the rapid advancement of high-tech solutions, the development and effective implementation of a digital/intelligent RIDs control and prevention system are imperative to facilitate precise surveillance, early warnings, and swift responses.

Early Warning and Prediction of Scarlet Fever in China Using the Baidu Search Index and Autoregressive Integrated Moving Average With Explanatory Variable (ARIMAX) Model: Time Series Analysis.

BACKGROUND: Internet-derived data and the autoregressive integrated moving average (ARIMA) and ARIMA with explanatory variable (ARIMAX) models are extensively used for infectious disease surveillance. However, the effectiveness of the Baidu search index (BSI) in predicting the incidence of scarlet fever remains uncertain. OBJECTIVE: Our objective was to investigate whether a low-cost BSI monitoring system could potentially function as a valuable complement to traditional scarlet fever surveillance in China. METHODS: ARIMA and ARIMAX models were developed to predict the incidence of scarlet fever in China using data from the National Health Commission of the People's Republic of China between January 2011 and August 2022. The procedures included establishing a keyword database, keyword selection and filtering through Spearman rank correlation and cross-correlation analyses, construction of the scarlet fever comprehensive search index (CSI), modeling with the training sets, predicting with the testing sets, and comparing the prediction performances. RESULTS: The average monthly incidence of scarlet fever was 4462.17 (SD 3011.75) cases, and annual incidence exhibited an upward trend until 2019. The keyword database contained 52 keywords, but only 6 highly relevant ones were selected for modeling. A high Spearman rank correlation was observed between the scarlet fever reported cases and the scarlet fever CSI (rs=0.881). We developed the ARIMA(4,0,0)(0,1,2)(12) model, and the ARIMA(4,0,0)(0,1,2)(12) + CSI (Lag=0) and ARIMAX(1,0,2)(2,0,0)(12) models were combined with the BSI. The 3 models had a good fit and passed the residuals Ljung-Box test. The ARIMA(4,0,0)(0,1,2)(12), ARIMA(4,0,0)(0,1,2)(12) + CSI (Lag=0), and ARIMAX(1,0,2)(2,0,0)(12) models demonstrated favorable predictive capabilities, with mean absolute errors of 1692.16 (95% CI 584.88-2799.44), 1067.89 (95% CI 402.02-1733.76), and 639.75 (95% CI 188.12-1091.38), respectively; root mean squared errors of 2036.92 (95% CI 929.64-3144.20), 1224.92 (95% CI 559.04-1890.79), and 830.80 (95% CI 379.17-1282.43), respectively; and mean absolute percentage errors of 4.33% (95% CI 0.54%-8.13%), 3.36% (95% CI -0.24% to 6.96%), and 2.16% (95% CI -0.69% to 5.00%), respectively. The ARIMAX models outperformed the ARIMA models and had better prediction performances with smaller values. CONCLUSIONS: This study demonstrated that the BSI can be used for the early warning and prediction of scarlet fever, serving as a valuable supplement to traditional surveillance systems.

Epidemiological characteristics and spatiotemporal clustering of scarlet fever in Liaoning Province, China, 2010-2019.

BACKGROUND: To explore the epidemiological characteristics and spatiotemporal distribution of scarlet fever in Liaoning Province, which could provide scientific evidence for the formulation and improvement of prevention and control strategies and measures. METHODS: Data on scarlet fever cases and population were obtained from the China Information System for Disease Control and Prevention in Liaoning Province between 2010 and 2019. We examined the spatial and spatiotemporal clusters of scarlet fever across Liaoning Province using the Moran's I, local indicators of spatial association, local Gi* hotspot statistics, and Kulldorff's retrospective space-time scan statistical analysis. RESULTS: Between 1st January 2010 and 31st December 2019, 46,652 cases of scarlet fever were reported in Liaoning Province, with an annual average incidence of 10.67 per 100,000. The incidence of scarlet fever had obvious seasonality with high incidence in early summer June and early winter December. The male-to-female ratio was 1.53:1. The highest incidence of cases occurred in 3-9 year old children. The most likely spatiotemporal cluster and the secondary clusters were detected in urban regions of Shenyang and Dalian, Liaoning Province. CONCLUSIONS: The incidence of scarlet fever has obvious spatiotemporal clustering, with the high-risk areas mainly concentrated in urban area of Shenyang and Dalian, Liaoning Province. Control strategies need to focus on high-risk season, high-risk areas and high-risk populations in order to reduce the incidence of scarlet fever.

Incidence and trends of 17 notifiable bacterial infectious diseases in China, 2004-2019.

BACKGROUND: Certain bacterial infectious diseases are categorized as notifiable infectious diseases in China. Understanding the time-varying epidemiology of bacterial infections diseases can provide scientific evidence to inform prevention and control measures. METHODS: Yearly incidence data for all 17 major notifiable bacterial infectious diseases (BIDs) at the province level were obtained from the National Notifiable Infectious Disease Reporting Information System in China between 2004 and 2019. Of them 16 BIDs are divided into four categories, respiratory transmitted diseases (RTDs, 6 diseases), direct contact/fecal-oral transmitted diseases (DCFTDs, 3 diseases), blood-borne/sexually transmitted diseases (BSTDs, 2 diseases), and zoonotic and vector-borne diseases (ZVDs, 5 diseases), and neonatal tetanus is excluded in the analysis. We characterized the demographic, temporal, and geographical features of the BIDs and examined their changing trends using a joinpoint regression analysis. RESULTS: During 2004‒2019, 28 779 thousand cases of BIDs were reported, with an annualized incidence rate of 134.00 per 100 000. RTDs were the most commonly reported BIDs, accounting for 57.02% of the cases (16 410 639/28 779 000). Average annual percent changes (AAPC) in incidence were - 1.98% for RTDs, - 11.66% for DCFTDs, 4.74% for BSTDs, and 4.46% for ZVDs. Females had a higher incidence of syphilis than males, and other BIDs were more commonly reported in males. Among 0-5-year-olds, the diseases with the largest increases in incidence were pertussis (15.17% AAPC) and scarlet fever (12.05%). Children and students had the highest incidence rates of scarlet fever, pertussis, meningococcal meningitis, and bacillary dysentery. Northwest China had the highest incidence of RTDs, while South and East China had the highest incidences of BSTDs. Laboratory confirmation of BIDs increased from 43.80 to 64.04% during the study period. CONCLUSIONS: RTDs and DCFTDs decreased from 2004 to 2019 in China, while BSTDs and ZVDs increased during the same period. Great attention should be paid to BSTDs and ZVDs, active surveillance should be strengthened, and timely control measures should be adopted to reduce the incidence.

High-level levofloxacin-resistant emm12 group A Streptococcus, 2012-2018: A multicenter study in Taiwan.

High-level levofloxacin-resistant group A Streptococcus emerged in Taiwan in 2012. Among the 24 isolates identified, 23 belonged to emm12/ST36, most harbored the same GyrA and ParC mutations and were highly clonal. wgMLST showed them to be closely related to the Hong Kong scarlet fever outbreak strains. Continuous surveillance is warranted.

[Spatial clustering analysis of scarlet fever incidence in China from 2016 to 2020].

OBJECTIVE: To investigate the incidence trend and spatial clustering characteristics of scarlet fever in China from 2016 to 2020 to provide evidence for development of regional disease prevention and control strategies. METHODS: The incidence data of scarlet fever in 31 provinces and municipalities in mainland China from 2016 to 2020 were obtained from the Chinese Health Statistics Yearbook and the Public Health Science Data Center led by the Chinese Center for Disease Control and Prevention.The three-dimensional spatial trend map of scarlet fever incidence in China was drawn using ArcGIS to determine the regional trend of scarlet fever incidence.GeoDa spatial autocorrelation analysis was used to explore the spatial aggregation of scarlet fever in China in recent years. RESULTS: From 2016 to 2020, a total of 310 816 cases of scarlet fever were reported in 31 provinces, municipalities directly under the central government and autonomous regions, with an average annual incidence of 4.48/100 000.The reported incidence decreased from 4.32/100 000 in 2016 to 1.18/100 000 in 2020(Z=103.47, P < 0.001).The incidence of scarlet fever in China showed an obvious regional clustering from 2016 to 2019(Moran's I>0, P < 0.05), but was randomly distributed in 2020(Moran's I>0, P=0.16).The incidence of scarlet fever showed a U-shaped distribution in eastern and western regions of China, and increased gradually from the southern to northern regions.Inner Mongolia Autonomous Region and Hebei and Gansu provinces had the High-high (H-H) clusters of scarlet fever in China. CONCLUSION: Scarlet fever still has a high incidence in China with an obvious spatial clustering.For the northern regions of China with H-H clusters of scarlet fever, the allocation of health resources and public health education dynamics should be strengthened, and local scarlet fever prevention and control policies should be made to contain the hotspots of scarlet fever.

Spatiotemporal and Seasonal Trends of Class A and B Notifiable Infectious Diseases in China: Retrospective Analysis.

BACKGROUND: China is the most populous country globally and has made significant achievements in the control of infectious diseases over the last decades. The 2003 SARS epidemic triggered the initiation of the China Information System for Disease Control and Prevention (CISDCP). Since then, numerous studies have investigated the epidemiological features and trends of individual infectious diseases in China; however, few considered the changing spatiotemporal trends and seasonality of these infectious diseases over time. OBJECTIVE: This study aims to systematically review the spatiotemporal trends and seasonal characteristics of class A and class B notifiable infectious diseases in China during 2005-2020. METHODS: We extracted the incidence and mortality data of 8 types (27 diseases) of notifiable infectious diseases from the CISDCP. We used the Mann-Kendall and Sen's methods to investigate the diseases' temporal trends, Moran I statistic for their geographical distribution, and circular distribution analysis for their seasonality. RESULTS: Between January 2005 and December 2020, 51,028,733 incident cases and 261,851 attributable deaths were recorded. Pertussis (P=.03), dengue fever (P=.01), brucellosis (P=.001), scarlet fever (P=.02), AIDS (P<.001), syphilis (P<.001), hepatitis C (P<.001) and hepatitis E (P=.04) exhibited significant upward trends. Furthermore, measles (P<.001), bacillary and amebic dysentery (P<.001), malaria (P=.04), dengue fever (P=.006), brucellosis (P=.03), and tuberculosis (P=.003) exhibited significant seasonal patterns. We observed marked disease burden-related geographic disparities and heterogeneities. Notably, high-risk areas for various infectious diseases have remained relatively unchanged since 2005. In particular, hemorrhagic fever and brucellosis were largely concentrated in Northeast China; neonatal tetanus, typhoid and paratyphoid, Japanese encephalitis, leptospirosis, and AIDS in Southwest China; BAD in North China; schistosomiasis in Central China; anthrax, tuberculosis, and hepatitis A in Northwest China; rabies in South China; and gonorrhea in East China. However, the geographical distribution of syphilis, scarlet fever, and hepatitis E drifted from coastal to inland provinces during 2005-2020. CONCLUSIONS: The overall infectious disease burden in China is declining; however, hepatitis C and E, bacterial infections, and sexually transmitted infections continue to multiply, many of which have spread from coastal to inland provinces.

Emerging Invasive Group A Streptococcus M1UK Lineage Detected by Allele-Specific PCR, England, 20201.

Increasing reports of invasive Streptococcus pyogenes infections mandate surveillance for toxigenic lineage M1UK. An allele-specific PCR was developed to distinguish M1UK from other emm1 strains. The M1UK lineage represented 91% of invasive emm1 isolates in England in 2020. Allele-specific PCR will permit surveillance for M1UK without need for genome sequencing.

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At the end of 2022, the World Health Organization reported an increase in group A Streptococcus (GAS) infections, such as scarlet fever, in multiple countries. The outbreak primarily affected children under 10 years old, and the number of deaths was higher than anticipated, causing international concern. This paper reviews the current state of the GAS disease outbreak, its causes, and response measures. The authors aim to draw attention from clinical workers in China and increase their awareness and vigilance regarding this epidemic. Healthcare workers should be aware of the potential epidemiological changes in infectious diseases that may arise after the optimization of control measures for coronavirus disease 2019 to ensure children's health.

Recrudescence of Scarlet Fever and Its Implications for Dental Professionals.

A significant increase in the incidence of scarlet fever, mainly in Europe, has been noted during the COVID-19 postpandemic period. Scarlet fever is caused by a pyrogenic exotoxin-producing streptococcus-Streptococcus pyogenes-responsible for more than 500,000 deaths annually worldwide. Superantigens (SAgs) secreted by this Group A streptococcus (GAS) usually overstimulate the human immune system, causing an amplified hypersensitivity reaction leading to initial symptoms such as sore throat, high fever, and a sandpaper-like skin rash. There could be concurrent oral manifestations known as "strawberry tongue" or "raspberry tongue," which may be first noted by oral health professionals. The early diagnosis and treatment of this disease is critical to obviate the development of local and systemic sequelae such as acute rheumatic fever, endocarditis, and glomerulonephritis. Antibiotics should be prescribed early to mitigate its duration, sequelae, and community spread. Dental practitioners should be aware of the early symptoms of scarlet fever for infection detection, emergency patient management, and appropriate referral. This concise review outlines the prevalence, pathogenicity, oral and systemic manifestations, as well as the dental implications of scarlet fever.

[Analysis of the epidemiological characteristics of scarlet fever in Yantai City, Shandong Province from 2015 to 2019].

From 2015 to 2019, the annual average incidence rate of scarlet fever was 7.80/100 000 in Yantai City, which showed an increasing trend since 2017 (χ2trend=233.59, P<0.001). The peak period of this disease was from April to July and November to January of the next year. The ratio of male to female was 1.49∶1, with a higher prevalence among cases aged 3 to 9 years (2 357/2 552, 92.36%). Children in kindergartens, primary and middle school students, and scattered children were the high risk population, with the incidence rate of 159.86/100 000, 25.57/100 000 and 26.77/100 000, respectively. The global spatial auto-correlation analysis showed that the global Moran's I index of the reported incidence rate of scarlet fever in Yantai from 2015 to 2019 was 0.28, 0.29, 0.44, 0.48, and 0.22, respectively (all P values<0.05), suggesting that the incidence rate of scarlet fever in Yantai from 2015 to 2019 was spatial clustering. The local spatial auto-correlation analysis showed that the "high-high" clustering areas were mainly located in Laizhou City, Zhifu District, Haiyang City, Fushan District and Kaifa District, while the "low-high" clustering areas were mainly located in Haiyang City and Fushan District.

Detection of Streptococcus pyogenes M1UK in Australia and characterization of the mutation driving enhanced expression of superantigen SpeA.

A new variant of Streptococcus pyogenes serotype M1 (designated 'M1UK') has been reported in the United Kingdom, linked with seasonal scarlet fever surges, marked increase in invasive infections, and exhibiting enhanced expression of the superantigen SpeA. The progenitor S. pyogenes 'M1global' and M1UK clones can be differentiated by 27 SNPs and 4 indels, yet the mechanism for speA upregulation is unknown. Here we investigate the previously unappreciated expansion of M1UK in Australia, now isolated from the majority of serious infections caused by serotype M1 S. pyogenes. M1UK sub-lineages circulating in Australia also contain a novel toxin repertoire associated with epidemic scarlet fever causing S. pyogenes in Asia. A single SNP in the 5' transcriptional leader sequence of the transfer-messenger RNA gene ssrA drives enhanced SpeA superantigen expression as a result of ssrA terminator read-through in the M1UK lineage. This represents a previously unappreciated mechanism of toxin expression and urges enhanced international surveillance.

Application of logistic differential equation models for early warning of infectious diseases in Jilin Province.

BACKGROUND: There is still a relatively serious disease burden of infectious diseases and the warning time for different infectious diseases before implementation of interventions is important. The logistic differential equation models can be used for predicting early warning of infectious diseases. The aim of this study is to compare the disease fitting effects of the logistic differential equation (LDE) model and the generalized logistic differential equation (GLDE) model for the first time using data on multiple infectious diseases in Jilin Province and to calculate the early warning signals for different types of infectious diseases using these two models in Jilin Province to solve the disease early warning schedule for Jilin Province throughout the year. METHODS: Collecting the incidence of 22 infectious diseases in Jilin Province, China. The LDE and GLDE models were used to calculate the recommended warning week (RWW), the epidemic acceleration week (EAW) and warning removed week (WRW) for acute infectious diseases with seasonality, respectively. RESULTS: Five diseases were selected for analysis based on screening principles: hemorrhagic fever with renal syndrome (HFRS), shigellosis, mumps, Hand, foot and mouth disease (HFMD), and scarlet fever. The GLDE model fitted the above diseases better (0.80 ≤ R2 ≤ 0.94, P <  0. 005) than the LDE model. The estimated warning durations (per year) of the LDE model for the above diseases were: weeks 12-23 and 40-50; weeks 20-36; weeks 15-24 and 43-52; weeks 26-34; and weeks 16-25 and 41-50. While the durations of early warning (per year) estimated by the GLDE model were: weeks 7-24 and 36-51; weeks 13-37; weeks 11-26 and 39-54; weeks 23-35; and weeks 12-26 and 40-50. CONCLUSIONS: Compared to the LDE model, the GLDE model provides a better fit to the actual disease incidence data. The RWW appeared to be earlier when estimated with the GLDE model than the LDE model. In addition, the WRW estimated with the GLDE model were more lagged and had a longer warning time.

Spatiotemporal dynamics and potential ecological drivers of acute respiratory infectious diseases: an example of scarlet fever in Sichuan Province.

OBJECT: Scarlet fever is an acute respiratory infectious disease that endangers public health and imposes a huge economic burden. In this paper, we systematically studied its spatial and temporal evolution and explore its potential ecological drivers. The goal of this research is to provide a reference for analysis based on surveillance data of scarlet fever and other acute respiratory infectious illnesses, and offer suggestions for prevention and control. METHOD: This research is based on a spatiotemporal multivariate model (Endemic-Epidemic model). Firstly, we described the epidemiology status of the scarlet fever epidemic in Sichuan Province from 2016 to 2019. Secondly, we used spatial autocorrelation analysis to understand the spatial pattern. Thirdly, we applied the endemic-epidemic model to analyze the spatiotemporal dynamics by quantitatively decomposing cases into endemic, autoregressive, and spatiotemporal components. Finally, we explored potential ecological drivers that could influence the spread of scarlet fever. RESULTS: From 2016 to 2019, the incidence of scarlet fever in Sichuan Province varied much among cities. In terms of temporal distribution, there were 1-2 epidemic peaks per year, and they were mainly concentrated from April to June and October to December. In terms of transmission, the endemic and temporal spread were predominant. Our findings imply that the school holiday could help to reduce the spread of scarlet fever, and a standard increase in Gross Domestic Product (GDP) was associated with 2.6 folds contributions to the epidemic among cities. CONCLUSION: Scarlet fever outbreaks are more susceptible to previous cases, as temporal spread accounted for major transmission in many areas in Sichuan Province. The school holidays and GDP can influence the spread of infectious diseases. Given that covariates could not fully explain heterogeneity, adding random effects was essential to improve accuracy. Paying attention to critical populations and hotspots, as well as understanding potential drivers, is recommended for acute respiratory infections such as scarlet fever. For example, our study reveals GDP is positively associated with spatial spread, indicating we should consider GDP as an important factor when analyzing the potential drivers of acute infectious disease.

Retrospective study on the effectiveness and safety of the shortened 5- to 7-day antibiotic regimen for acute streptococcal pharyngotonsillitis compared to the classic 10-day regimen.

INTRODUCTION: Antibiotherapy regimens for management of acute streptococcal pharyngitis traditionally last 10 days, but the development of resistance to different antimicrobials has motivated the exploration of shorter courses. MATERIAL AND METHODS: We selected patients given a diagnosis of streptococcal pharyngitis in 2 paediatric caseloads of 1 primary care centre between June 2016 and April 2020. We compared outcomes in patients treated with 8- to 10-day courses versus 5- to 7-day courses. RESULTS: The analysis included 350 care episodes (252 patients). Sixty-four percent were managed with 8- to 10-day courses of antibiotherapy (group 1) and 36% with 5- to 7-day courses (group 2). There were no significant differences in the incidence of streptococcal pharyngitis or scarlet fever in the 3 months that followed (OR, 0.98; 95% confidence interval [CI], 0.46-2.03), with similar percentages in both groups (9.8% vs 9.5%). Overall, without differentiating based on the type of infection (streptococcal pharyngitis, scarlet fever or other streptococcal infections), we found similar outcomes (OR, 0.81; 95% CI, 0.41-1.59): 13.4% in group 1 and 11.1% in group 2. We also found no differences in the frequency of adverse events documented in the health records (OR, 0.29; 95% CI, 0.04-2.44): 2.7% in group 1 and 0.8% in group 2. CONCLUSIONS: In our experience, a shorter antibiotic course (5-7 days) is not less effective or more unsafe for management of acute streptococcal pharyngitis than the traditional 10-day course.