A rapid molecular detection tool for toxigenic M1UK Streptococcus pyogenes.

BACKGROUND: The gradual replacement of the Streptococcus pyogenes M1global genotype by a newly emergent M1UK variant is a global public health threat warranting increased surveillance. M1UK differs from progenitor M1global genotype by 27 single nucleotide polymorphisms (SNPs) and is characterised by increased speA superantigen expression in vitro. METHODS: An allele-specific real-time PCR assay was developed for the rapid detection of M1UK strains. The assay was used in combination with whole-genome sequencing to determine emm (sub)type distribution for 51 invasive (n = 9) and non-invasive (n = 42) S. pyogenes clinical isolates. RESULTS: Emm1 was the most prevalent S. pyogenes emm serotype (n = 11) in this set of clinical isolates, with M1UK being the dominant emm1 genotype (4/5 invasive, 3/6 non-invasive isolates). The assay accurately detected M1UK strains. Whole genome sequencing revealed continued presence of Australian M1UK sub-lineages associated with epidemic scarlet fever-causing S. pyogenes in Asia. CONCLUSIONS: Our study establishes a suitable target for detection of the toxigenic M1UK, and confirms the maintenance of M1UK strains in Queensland, Australia. This assay can be deployed in laboratories and provides a valuable, cost-effective tool to enhance surveillance of the expanding M1UK clone.

Invasive Group A Streptococcus Hypervirulent M1UK Clone, Canada, 2018-2023.

To determine invasive group A Streptococcus trends in Canada, we characterized emm1 isolates collected during 2018-2023. The percentage of hypervirulent M1UK lineage isolates increased significantly, from 22.1% in 2018 to 60.2% in 2023. Genomic analysis identified geographically and temporally associated clusters and genes associated with virulent bacteriophage acquisition.

Epidemiology of scarlet fever in Victoria, Australia, 2007-2017.

In the last 10-15 years, there has been a global resurgence of scarlet fever, an infection historically associated with significant morbidity and mortality. It is unknown whether scarlet fever incidence has increased in Australia. We aimed to examine the incidence, predictors and severity of scarlet fever in the state of Victoria, Australia from 2007 to 2017, analyzing scarlet fever emergency department (ED) presentations, hospitalizations and deaths. Of the 1 578 scarlet fever cases during the study period, most occurred in children aged <10 years (1 344, 85%), in males (882, 56%), and during winter and spring months (918, 57%). There were no deaths with scarlet fever, however, 374 cases (24%) were admitted to hospital. The annual incidence of scarlet fever was stable during the study period (mean, 2.5; range, 1.9-3.1 cases per 100 000). Annual incidence was highest in children aged <5 years (19.3 per 100 000), and was 21% higher in males than females, adjusting for age and year (incidence rate ratio, 1.21, 95%CI 1.09-1.34). Whilst scarlet fever ED presentations and hospitalizations were stable in Victoria from 2007 to 2017, the recent identification of a Streptococcus pyogenes variant in Australia associated with epidemic scarlet fever overseas highlights the risk of future outbreaks.

Epidemiological changes of scarlet fever before, during and after the COVID-19 pandemic in Chongqing, China: a 19-year surveillance and prediction study.

BACKGROUND: This study aimed to investigate the epidemiological changes in scarlet fever before, during and after the COVID-19 pandemic (2005-2023) and predict the incidence of the disease in 2024 and 2025 in Chongqing Municipality, Southwest China. METHODS: Descriptive analysis was used to summarize the characteristics of the scarlet fever epidemic. Spatial autocorrelation analysis was utilized to explore the distribution pattern of the disease, and the seasonal autoregressive integrated moving average (SARIMA) model was constructed to predict its incidence in 2024 and 2025. RESULTS: Between 2005 and 2023, 9,593 scarlet fever cases were reported in Chongqing, which resulted in an annual average incidence of 1.6694 per 100,000 people. Children aged 3-7 were the primary victims of this disease, with the highest average incidence found among children aged 6 (5.0002 per 100,000 people). Kindergarten children were the dominant infected population, accounting for as much as 54.32% of cases, followed by students (34.09%). The incidence for the male was 1.51 times greater than that for the female. The monthly distribution of the incidence showed a bimodal pattern, with one peak occurring between April and June and another in November or December. The spatial autocorrelation analysis revealed that scarlet fever cases were markedly clustered; the areas with higher incidence were mainly concentrated in Chongqing's urban areas and its adjacent districts, and gradually spreading to remote areas after 2020. The incidence of scarlet fever increased by 106.54% and 39.33% in the post-upsurge period (2015-2019) and the dynamic zero-COVID period (2020-2022), respectively, compared to the pre-upsurge period (2005-2014) (P < 0.001). During the dynamic zero-COVID period, the incidence of scarlet fever decreased by 68.61%, 25.66%, and 10.59% (P < 0.001) in 2020, 2021, and 2022, respectively, compared to the predicted incidence. In 2023, after the dynamic zero-COVID period, the reported cases decreased to 1.5168 per 100,000 people unexpectedly instead of increasing. The cases of scarlet fever are predicted to increase in 2024 (675 cases) and 2025 (705 cases). CONCLUSIONS: Children aged 3-7 years are the most affected population, particularly males, and kindergartens and primary schools serving as transmission hotspots. It is predicted that the high incidence of scarlet fever in Chongqing will persist in 2024 and 2025, and the outer districts (counties) beyond urban zone would bear the brunt of the impact. Therefore, imminent public health planning and resource allocation should be focused within those areas.

The association between the scarlet fever and meteorological factors, air pollutants and their interactions in children in northwest China.

Scarlet fever (SF) is an acute respiratory transmitted disease that primarily affects children. The influence of meteorological factors and air pollutants on SF in children has been proved, but the relevant evidence in Northwest China is still lacking. Based on the weekly reported cases of SF in children in Lanzhou, northwest China, from 2014 to 2018, we used geographical detectors, distributed lag nonlinear models (DLNM), and bivariate response models to explore the influence of meteorological factors and air pollutants with SF. It was found that ozone (O3), carbon monoxide (CO), sulfur dioxide (SO2), temperature, pressure, water vapor pressure and wind speed were significantly correlated with SF based on geographical detectors. With the median as reference, the influence of high temperature, low pressure and high pressure on SF has a risk effect (relative risk (RR) > 1), and under extreme conditions, the dangerous effect was still significant. High O3 had the strongest effect at a 6-week delay, with an RR of 5.43 (95%CI: 1.74,16.96). The risk effect of high SO2 was strongest in the week of exposure, and the maximum risk effect was 1.37 (95%CI: 1.08,1.73). The interactions showed synergistic effects between high temperatures and O3, high pressure and high SO2, high nitrogen dioxide (NO2) and high particulate matter with diameter of less than 10 µm (PM10), respectively. In conclusion, high temperature, pressure, high O3 and SO2 were the most important factors affecting the occurrence of SF in children, which will provide theoretical support for follow-up research and disease prevention policy formulation.

Medical aspects of the tour by Martin Martin (c 1660-1719) of the Western and Northern Islands of Scotland, Circa 1695.

This review is based investigations on the Western Isles, Scotland, by Martin Martin, a notable Scottish Highlander, academic and medical doctor, of the 17th-18th century. His extensive observations of the geography and peoples of these Isles were recorded in his books, "On the Description of the Western Islands of Scotland Circa 1695" and "A Late Voyage to St Kilda". In these books and subsequent papers there were some noteworthy observations on the occurrence (and as he says non-occurrence) of "epidemical" diseases and conditions afflicting the peoples of The Isle of Skye and the Western Isles of Scotland in this period, and these are discussed in this review. Martin also gives details of a wide variety of remedies that were observed or reported by inhabitants around that time. Some of these remedies are interesting for their relevance to the period but others are of doubtful merit. These are reviewed here more for their significance in the understanding of the diseases and conditions of humans and even in some cases animals at that time. Introductions by Charles Withers and R.W. Munro, 11 and re-assessments of the contributions of Martin and colleagues of that time have given insight into the health and condition of peoples of the Western Isles of Scotland(the Occidental) (Martin 1695; Martin 1716).

Scaling features in high-concentrations PM2.5 evolution: the Ignored factor affecting scarlet fever incidence.

As an acute respiratory disease, scarlet fever has great harm to public health. Some evidence indicates that the time distribution pattern of heavy PM2.5 pollution occurrence may have an impact on health risks. This study aims to reveal the relation between scaling features in high-concentrations PM2.5 (HC-PM2.5) evolution and scarlet fever incidence (SFI). Based on the data of Hong Kong from 2012 to 2019, fractal box-counting dimension (D) is introduced to capture the scaling features of HC-PM2.5. It has been found that index D can quantify the time distribution of HC-PM2.5, and lower D values indicate more cluster distribution of HC-PM2.5. Moreover, scale-invariance in HC-PM2.5 at different time scales has been discovered, which indicates that HC-PM2.5 occurrence is not random but follows a typical power-law distribution. Next, the exposure-response relationship between SFI and scale-invariance in HC-PM2.5 is explored by Distributed lag non-linear model, in conjunction with meteorological factors. It has been discovered that scale-invariance in HC-PM2.5 has a nonlinear effect on SFI. Low and moderate D values of HC-PM2.5 are identified as risk factors for SFI at small time-scale. Moreover, relative risk shows a decreasing trend with the increase of exposure time. These results suggest that exposure to short-term clustered HC-PM2.5 makes individual more prone to SFI than exposure to long-term uniform HC-PM2.5. This means that individuals in slightly-polluted regions may face a greater risk of SFI, once the PM2.5 concentration keeps rising. In the future, it is expected that the relative risk of scarlet fever for a specific region can be estimated based on the quantitative analysis of scaling features in high-concentrations PM2.5 evolution.

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.

Epidemiological analysis of Group A streptococcus infection diseases among children in Beijing, China under COVID-19 pandemic.

BACKGROUND: Group A streptococcus is human-restricted gram-positive pathogen, responsible for various clinical presentations from mild epidermis infections to life threatened invasive diseases. Under COVID-19 pandemic,. the characteristics of the epidemic strains of GAS could be different. PURPOSE: To investigate epidemiological and molecular features of isolates from GAS infections among children in Beijing, China between January 2020 and December 2021. Antimicrobial susceptibility profiling was performed based on Cinical Laboratory Sandards Institute. Distribution of macrolide-resistance genes, emm types, and superantigens was examined by polymerase chain reaction. RESULTS: 114 GAS isolates were collected which were frequent resistance against erythromycin (94.74%), followed by clindamycin (92.98%), tetracycline (87.72%). Emm12 (46.49%), emm1 (25.44%) were dominant emm types. Distribution of ermB, ermA, and mefA gene was 93.85%, 2.63%, and 14.04%, respectively. Frequent superantigenes identified were smeZ (97.39%), speG (95.65%), and speC (92.17%). Emm1 strains possessed smeZ, ssa, and speC, while emm12 possessed smeZ, ssa, speG, and speC. Erythromycin resistance was predominantly mediated by ermB. Scarlet fever strains harbored smeZ (98.81%), speC (94.05%). Impetigo strains harbored smeZ (88.98%), ssa (88.89%), and speC (88.89%). Psoriasis strains harbored smeZ (100%). CONCLUSIONS: Under COVID-19 pandemic, our collections of GAS infection cutaneous diseases decreased dramatically. Epidemiological analysis of GAS infections among children during COVID-19 pandemic was not significantly different from our previous study. There was a correlation among emm, superantigen gene and disease manifestations. Long-term surveillance and investigation of emm types and superantigens of GAS prevalence are imperative.

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.

[Paying attention to the epidemic of group A Streptococcus infections in multiple European and American countries]. / å ³æ³¨æ¬§ç¾Žå¤šå›½Aæ—é“¾çƒèŒæ„ŸæŸ“ç–«æƒ .

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.

Modeling the effects of air pollutants and meteorological factors on scarlet fever in five provinces, Northwest China, 2013-2018.

As a common infectious disease, scarlet fever has exposed a tendency of slow fluctuating ups and downs in recent years with a certain periodicity. In this work, a novel differential equation epidemic model with freely transmitted viruses is introduced to investigate the transmission dynamics of scarlet fever in Northwest China. First, the correlation analysis reveals that the incidence rate of scarlet fever is significantly positively correlated with air pressure (r = 0.61), conversely negatively correlated with precipitation (r = -0.15). Furthermore, the basic reproduction number R0 is derived, and this paper proves that the unique disease-free periodic solution P0 is globally symptotically stable when R0 < 1, while the disease is uniformly persistent and at least one positive periodic solution exists when R0 > 1. Moreover, by studying the qualitative of correlation between the effective reproduction number and air pollutants or meteorological factors, the seasonal variation pattern of incidence is summarized. Our investigations suggest that the relevant epidemic prevention departments should pay close attention to changes in environmental factors of the five provinces of Northwest China to formulate timely prevention strategies before the arrival of the high-risk period.

Forecasting the monthly incidence of scarlet fever in Chongqing, China using the SARIMA model.

The incidence of scarlet fever has increased dramatically in recent years in Chongqing, China, but there has no effective method to forecast it. This study aimed to develop a forecasting model of the incidence of scarlet fever using a seasonal autoregressive integrated moving average (SARIMA) model. Monthly scarlet fever data between 2011 and 2019 in Chongqing, China were retrieved from the Notifiable Infectious Disease Surveillance System. From 2011 to 2019, a total of 5073 scarlet fever cases were reported in Chongqing, the male-to-female ratio was 1.44:1, children aged 3-9 years old accounted for 81.86% of the cases, while 42.70 and 42.58% of the reported cases were students and kindergarten children, respectively. The data from 2011 to 2018 were used to fit a SARIMA model and data in 2019 were used to validate the model. The normalised Bayesian information criterion (BIC), the coefficient of determination (R2) and the root mean squared error (RMSE) were used to evaluate the goodness-of-fit of the fitted model. The optimal SARIMA model was identified as (3, 1, 3) (3, 1, 0)12. The RMSE and mean absolute per cent error (MAPE) were used to assess the accuracy of the model. The RMSE and MAPE of the predicted values were 19.40 and 0.25 respectively, indicating that the predicted values matched the observed values reasonably well. Taken together, the SARIMA model could be employed to forecast scarlet fever incidence trend, providing support for scarlet fever control and prevention.

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.

The association between ambient air pollution and scarlet fever in Qingdao, China, 2014-2018: a quantitative analysis.

BACKGROUND: We conducted a distributed lag non-linear time series analysis to quantify the association between air pollution and scarlet fever in Qingdao city during 2014-2018. METHODS: A distributed lag non-linear model (DLNM) combined with a generalized additive mixed model (GAMM) was applied to quantify the distributed lag effects of air pollutions on scarlet fever, with daily incidence of scarlet fever as the dependent variable and air pollutions as the independent variable adjusted for potential confounders. RESULTS: A total of 6316 cases of scarlet fever were notified, and there were 376 days occurring air pollution during the study period. Scarlet fever was significantly associated with air pollutions at a lag of 7 days with different relative risk (RR) of air pollution degrees [1.172, 95% confidence interval (CI): 1.038-1.323 in mild air pollution; 1.374, 95% CI 1.078-1.749 in moderate air pollution; 1.610, 95% CI 1.163-2.314 in severe air pollution; 1.887, 95% CI 1.163-3.061 in most severe air pollution]. CONCLUSIONS: Our findings show that air pollution is positively associated with scarlet fever in Qingdao, and the risk of scarlet fever could be increased along with the degrees of air pollution. It contributes to developing strategies to prevent and reduce health impact from scarlet fever and other non-vaccine-preventable respiratory infectious diseases in air polluted areas.

Effects of COVID-19 Prevention Measures on Other Common Infections, Taiwan.

To determine whether policies to limit transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) hinder spread of other infectious diseases, we analyzed the National Health Insurance database in Taiwan. Rates of other infections were significantly lower after SARS-CoV-2 prevention measures were announced. This finding can be applied to cost-effectiveness of SARS-CoV-2 prevention.

Scarlet fever outbreak in a primary and middle school in Germany: importance of case ascertainment and risk communication.

A cluster of 18 scarlet fever cases and large illness absenteeism (32%, 58/184) in a school prompted concern and further investigation. We conducted telephone interviews with parents to ascertain cases and better comprehend parents' views. We identified 19 cases, of which 13 reported scarlet fever diagnosis by a physician and only seven fulfilled the probable case definition. We concluded that the outbreak was far smaller than suspected and found that communication and reporting could be improved. Accurate information and communication is essential in an outbreak; the school's concern could have been alleviated sooner and response measures better targeted.

Molecular epidemiology and antimicrobial resistance of group a streptococcus recovered from patients in Beijing, China.

BACKGROUND: Group A streptococcus (GAS) is an important human pathogen responsible for a broad range of infections. Epidemiological surveillance has been crucial to detect changes in the geographical and temporal variation of the disease pattern. The objective of this study was to investigate the molecular epidemiological characteristics and antimicrobial resistance of GAS isolates from patients in Children's Hospital in Beijing. METHODS: From 2016 to 2017, pharyngeal swab samples were collected from the outpatients in Children's Hospital, Capital Institute of Pediatrics, who were diagnosed with scarlet fever. Antimicrobial susceptibility test was performed according to the distribution of conventional antibiotics and Clinical and Laboratory Standards Institute (CLSI) recommendations. The distribution of the macrolide-resistance genes (ermB, ermA, mefA), emm (M protein-coding gene) typing, and superantigens (SAg) gene profiling were examined by polymerase chain reaction (PCR). RESULTS: A total of 297 GAS isolates were collected. The susceptibility of the isolates to penicillin, ceftriaxone, and levofloxacin was 100%. The resistance rate to erythromycin and clindamycin was 98.3 and 96.6%, respectively. The dominant emm types were emm12 (65.32%), emm1 (27.61%), emm75 (2.69%), and emm89 (1.35%). Of the 297 isolates, 290 (97.64%) carried the ermB gene, and 5 (1.68%) carried the mefA gene, while none carried the ermA gene. The most common superantigen genes identified from GAS isolates were smeZ (96.97%), speC (92.59%), speG (91.58%), ssa (85.52%), speI (54.55%), speH (52.19%), and speA (34.34%). Isolates with the genotype emm1 possessed speA, speC, speG, speJ, speM, ssa, and smeZ, while emm12 possessed speC, speG, speH, speI, speM, ssa, and smeZ superantigens. CONCLUSIONS: The prevalent strain of GAS isolates in Beijing has a high resistance rate to macrolides; however, penicillin can still be the preferred antibiotic for treatment. Erythromycin resistance was predominantly mediated by ermB. The common emm types were emm12 and emm1. There was a correlation between emm and the superantigen gene. Thus, long-term monitoring and investigation of the emm types and superantigen genes of GAS prevalence are imperative.

[Epidemiological characteristics of scarlet fever in Hubei Province from 2009 to 2018].

Descriptive epidemiological methods were adopted to analyze the epidemiological characteristics of scarlet fever in Hubei Province from 2009 to 2018. During this study, the epidemiological data was obtained from the Chinese Center for Disease Control and Prevention Information System. The results showed that there were 7 329 cases of scarlet fever reported in Hubei Province from 2009 to 2018 and the annual average incidence was 1.25 per 10 000. The number of cases was increased by years, and in 2018 it was the highest peak during these ten years and up to 1 758 which was account for 24% in these ten years, and the incidence was 2.98 per 10 000. The number of reported cases of male and female was 4 473 and 2 856, respectively, and both of the incidence rates were on the rise. The reported cases were distributed in 0-60 year-old group, mainly in the <15-year-old group (98.4%, 7 214/7 329). The season with the highest incidence rates were summer and winter. There were cases in all cities and prefectures, and the number of cases were the highest in Wuhan (n=2 105), Yichang (n=1 390), Jingzhou (n=954) and Enshi (n=1 090). Hence, it was very necessary to be on guard for the outbreak of scarlet fever and strengthen the surveillance in summer and winter for the people who were younger than 15 years old, especially in Wuhan, Yichang, Jingzhou and Enshi.

Detection of Epidemic Scarlet Fever Group A Streptococcus in Australia.

Sentinel hospital surveillance was instituted in Australia to detect the presence of pandemic group A Streptococcus strains causing scarlet fever. Genomic and phylogenetic analyses indicated the presence of an Australian GAS emm12 scarlet fever isolate related to United Kingdom outbreak strains. National surveillance to monitor this pandemic is recommended.