Epidemic Trends Research Articles

Increasing trends of overweight and obesity in treatment-naive people living with HIV in Shenzhen from 2014 to 2020: an emerging health concern.

With the early initiation of antiretroviral therapy (ART) in China, the demographics of treatment-naïve people living with HIV (PLWH) are moving closer to those of the general population, which is characterized by a gradual increase in metabolic indicators. However, the epidemic trends of overweight and obesity over the past decade in treatment-naïve PLWH ready to initiate ART have not yet been investigated. A cross-sectional study was conducted, including 12,135 consecutive treatment-naïve PLWH ready to initiate ART in Shenzhen, using data retrieved from the China National Free Antiretroviral Treatment Program database from 2014 to 2020. The chi-square test was used to examine the trends of overweight and obesity between age groups, and multivariate logistic regression was used to identify the association of overweight and obesity with hyperglycemia and dyslipidemia. During the 7-year study period, 12,135 treatment-naïve PLWH ready to initiate ART were included, among whom 1,837 (15.1%) were overweight and 388 (3.2%) were obese. The prevalence of overweight rose from 11.4 to 17.3% (Z = -4.58, P for trend <0.01) and that of obesity from 2.0% to 4.2% (Z = -6.45, P for trend <0.01) from 2014 to 2020. The annual prevalence of overweight was the highest in the age group of participants >35 years compared to prevalence in other age groups during the period 2014-2020. Compared with those who were not overweight or obese, PLWH who were overweight or obese were more likely to have hyperglycemia (aOR 1.84, 95% CI: 1.37-2.49 for overweight; aOR 2.68, 95% CI: 1.62-4.44 for obesity), higher ALT level (aOR 2.70, 95% CI: 2.33-3.13 for overweight; aOR 3.85, 95% CI: 2.93-5.05 for obesity), higher TG levels (aOR 1.89, 95% CI 1.63-2.19 for overweight; aOR 2.56, 95% CI 1.97-3.32 for obesity), and lower HDL levels (aOR 1.67, 95% CI 1.44-1.95 for overweight; aOR 2.06, 95% CI 1.54-2.77 for obesity). The prevalence of overweight and obesity in treatment-naive PLWH increased steadily from 2014 to 2020 in Shenzhen. Overweight and obese in treatment-naive PLWH ready to initiate ART were associated with dyslipidemia and hyperglycemia. Public health authorities should take proactive steps to address these issues by implementing targeted screening, intervention programs including lifestyle modifications, and integrated healthcare services.

Epidemiological characteristics and spatio-temporal analysis of brucellosis in Shandong province, 2015–2021

BackgroundBrucellosis is one of the major public health problems in China, it not only causes huge economic losses to the society, but also threatens the human’s physical and mental health. The reported cases of brucellosis in Shandong province were at a high level, therefore, it is necessary for us to understand the epidemic characteristics and distribution trend of Brucellosis in Shandong province. This study aims to describe the epidemiological characteristics and spatial clustering characteristics of brucellosis in Shandong Province, provide a reference for the scientific prevention and control.MethodsHuman brucellosis data in Shandong province from 2015 to 2021 were obtained from the China Information System for Disease Control and Prevention, the data were analyzed by descriptive epidemiological methods, spatial autocorrelation analysis and spatial-temporal cluster analysis methods use ArcGIS and SaTScan software, the results were presented in ArcMap.ResultsA total of 22,251 human cases of brucellosis were reported, the annual incidence ranged between 2.41/100,000 and 4.07/100,000 from 2015 to 2021 in Shandong province, incidence has been decreasing year by year, while there was a significant increase in 2021. The distribution of brucellosis was of a seasonal trend, mainly concentrating during March to August. The age of the cases was mainly concentrated in the 30–74 age ranges, the average annual incidence rate was significantly higher in males than in females. The spatial analysis showed that the epidemics were mainly concentrated in the north and southwest. For the spatial autocorrelation analysis, a high global autocorrelation was observed at the county level, and the high–high clusters mainly distributed in the north and southwest region. For the spatio-temporal scanning, the most likely cluster areas mainly distributed in the north area, and then gradually moved southward, and the radius of clustered narrowed.ConclusionsHuman brucellosis remains a common challenge, particularly in northern region in spring and summer. More disease prevention and control measures should be taken in high-risk populations, and such higher-risk susceptible areas to reduce the incidence of brucellosis and ensure the health of the people.

A hybrid model for tuberculosis forecasting based on empirical mode decomposition in China

BackgroundPulmonary Tuberculosis is a major public health problem endangering people's health, a scientifically accurate predictive model is of great practical significance for the prevention and treatment of pulmonary tuberculosis.MethodsThe reported incidence data of pulmonary tuberculosis were from the National Public Health Science Data Center (https://www.phsciencedata.cn/). The ARIMA, LSTM, EMD-SARIMA, EMD-LSTM, EMD-ARMA-LSTM models were established using the reported monthly incidence of tuberculosis reported in China from January 2008 to December 2018. The MSE, MAE, RMSE and MAPE were used to evaluate the performance of the models to determine the best model.ResultsComparing decomposition-based single model with undecomposed single model, it was found that: when predicting the incidence trend in the next year, compared with SARIMA model, the MSE, MAE, RMSE and MAPE of EMD-SARIMA decreased by 39.3%, 19.0%, 22.1% and 19.8%, respectively. The MSE, MAE, RMSE and MAPE of EMD-LSTM were reduced by 40.5%, 12.8%, 22.9% and 12.7%, respectively, compared with the LSTM model; Comparing the decomposition-based hybrid model with the decomposition-based single model, it was found that: when predicting the incidence trend in the next year, compared with EMD-SARIMA model, the MSE, MAE, RMSE and MAPE of EMD-ARMA-LSTM model decreased by 21.7%, 10.6%, 11.5% and 11.2%, respectively. The MSE, MAE, RMSE and MAPE of EMD-ARMA-LSTM were reduced by 16.7%, 9.6%, 8.7% and 12.3%, respectively, compared with EMD-LSTM model. Furthermore, the performance of the model were consistent when predicting the incidence trend in the next 3 months, 6 months and 9 months.ConclusionThe prediction performance of the decomposition-based single model is better than that of the undecomposed single model, and the prediction performance of the combined model using the advantages of different models is better than that of the decomposition-based single model, so the EMD-ARMA-LSTM combination model can improve the prediction accuracy better than other models, which can provide a theoretical basis for predicting the epidemic trend of pulmonary tuberculosis and formulating prevention and control policies.

Cyclophilins and gibberellin-regulated proteins in IgE-mediated allergic diseases

SummaryThe prevalence of immunoglobulin E (IgE)-mediated allergic diseases is currently experiencing an epidemic trend characterized by an increase in both the number of affected individuals and the proportion of patients with multiple sensitizations. The majority of these multiple sensitizations are attributed to IgE reactions to genuine allergenic proteins from unrelated species. However, there is a growing trend of patients becoming sensitized to highly cross-reactive molecules, such as profilins, polcalcins, lipocalins, serum albumins, tropomyosins, and non-specific lipid transfer proteins (nsLTPs). In addition, allergen families that were previously considered of minimal importance are now gaining recognition for their role in the pathogenesis of IgE-mediated allergic diseases. Consequently, these allergen families are increasingly being considered in the diagnostic process. In this review, we aim to provide a comprehensive summary of the biochemical and allergological information about two of these “new” allergen families: cyclophilins (Cyp) and gibberellin-regulated proteins (GRP).

Epidemic Trends of Imported Shigellosis Cases in Korea and Japan, 2016–2020

Epidemic Trends of Imported Shigellosis Cases in Korea and Japan, 2016–2020

What can we learn from the five-year African swine fever epidemic in Asia?

Today's global swine industry is exposed to the unprecedented threat of African swine fever (ASF). Asia, the site of the most recent epidemics, could serve as a huge viral reservoir for the rest of the world given the severity of the damage, the huge swine industry, and the high volume of trade with other countries around the world. As the majority of ASF notifications in Asia today originate from pig farms, the movement of live pigs and associated pork products are considered critical control points for disease management. Particularly, small-scale or backyard farms with low biosecurity levels are considered major risk factors. Meanwhile, wild boars account for most notified cases in some countries and regions, which makes the epidemiological scenario different from that in other Asian countries. As such, the current epidemic situation and higher risk factors differ widely between these countries. A variety of studies on ASF control have been conducted and many valuable insights have been obtained in Asia; nevertheless, the overall picture of the epidemic is still unclear. The purpose of this review is to provide an accurate picture of the epidemic situation across Asia, focusing on each subregion to comprehensively explain the disease outbreak. The knowledge gained from the ASF epidemics experienced in Asia over the past 5 years would be useful for disease control in areas that are already infected, such as Europe, as well as for non-affected areas to address preventive measures. To this end, the review includes two aspects: a descriptive analytical review based on publicly available databases showing overall epidemic trends, and an individualized review at the subregional level based on the available literature.

Emergency regional food supply chain design and its labor demand forecasting model: application to COVID-19 pandemic disruption

The COVID-19 pandemic has severely disrupted the global food supply chain through various interventions, such as city closures, traffic restrictions, and silent management. Limited research has been conducted on the design of emergency regional food supply chains (ERFSC) and its labor demand forecasting under government-mandated interventions. This paper applies emergency supply chain management theory to analyze the business processes of the ERFSC and proposes a multi-level ERFSC network tailored to different risk levels. Additionally, a food demand forecasting model and a mathematical model for stochastic labor demand planning are constructed based on the development trend of regional epidemics. An empirical analysis is presented using Huaguoyuan, Guiyang, China, as an example. The results demonstrate that the proposed ERFSC design and its labor demand forecasting model can achieve secure supply and accurate distribution of necessities in regions with different risk levels. These findings have important policy and research implications for the government and practitioners to take interventions and actions to ensure food supply for residents in the context of city closure or silent management. This study serves as a pilot study that will be further extended by the authors from geographical and policy perspectives.

Measuring the worldwide spread of COVID-19 using a comprehensive modeling method

BackgroundWith the global spread of COVID-19, detecting high-risk countries/regions timely and dynamically is essential; therefore, we sought to develop automatic, quantitative and scalable analysis methods to observe and estimate COVID-19 spread worldwide and further generate reliable and timely decision-making support for public health management using a comprehensive modeling method based on multiple mathematical models.MethodsWe collected global COVID-19 epidemic data reported from January 23 to September 30, 2020, to observe and estimate its possible spread trends. Countries were divided into three outbreak levels: high, middle, and low. Trends analysis was performed by calculating the growth rate, and then country grouping was implemented using group-based trajectory modeling on the three levels. Individual countries from each group were also chosen to further disclose the outbreak situations using two predicting models: the logistic growth model and the SEIR model.ResultsAll 187 observed countries' trajectory subgroups were identified using two grouping strategies: with and without population consideration. By measuring epidemic trends and predicting the epidemic size and peak of individual countries, our study found that the logistic growth model generally estimated a smaller epidemic size than the SEIR model. According to SEIR modeling, confirmed cases in each country would take an average of 9–12 months to reach the outbreak peak from the day the first case occurred. Additionally, the average number of cases at the peak time will reach approximately 10–20% of the countries’ populations, and the countries with high trends and a high predicted size must pay special attention and implement public health interventions in a timely manner.ConclusionsWe demonstrated comprehensive observations and predictions of the COVID-19 outbreak in 187 countries using a comprehensive modeling method. The methods proposed in this study can measure COVID-19 development from multiple perspectives and are generalizable to other epidemic diseases. Furthermore, the methods also provide reliable and timely decision-making support for public health management.

Rare peptide anchors of HLA class I alleles contribute to the COVID-19 disease severity and T cell memory

Understanding how human leukocyte antigen (HLA) polymorphism affects both the susceptibility and severity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection will help to identify individuals at higher risk to better manage and prioritize vaccination at the clinical level and explain the differences in epidemic trends in different regions at the epidemiological level. This study compared the frequencies of HLA class I alleles (HLA-A, B) in 214 coronavirus disease 2019 (COVID-19) patients with different disease severity and 35 healthy controls and analyzed the correlations between specific HLA alleles and disease severity and T cell memory. The results showed no significant difference in HLA allele frequencies between COVID-19 patients and healthy controls (P > 0.05). The allele HLA-B*13:02 was significantly correlated with the disease severity of COVID-19 patients (P = 0.006). After adjustment for age and disease severity, the T cell responses of COVID-19 convalescents with the allele HLA-B*40:01 may be lower at six months (P = 0.044) and 12 months (P = 0.069). Moreover, these results may be due to their rare peptide anchors by analyzing the binding peptide motifs of these HLA alleles. The study may be valuable for investigating the potential association of specific HLA alleles with SARS-CoV-2 infection.

Effective vaccination strategies to control COVID-19 in Korea: a modeling study.

In Korea, as immunity levels of the coronavirus disease 2019 (COVID-19) in the population acquired through previous infections and vaccinations have decreased, booster vaccinations have emerged as a necessary measure to control new outbreaks. The objective of this study was to identify the most suitable vaccination strategy for controlling the surge in COVID-19 cases. A mathematical model was developed to concurrently evaluate the immunity levels induced by vaccines and infections. This model was then employed to investigate the potential for future resurgence and the possibility of control through the use of vaccines and antivirals. As of May 11, 2023, if the current epidemic trend persists without further vaccination efforts, a peak in resurgence is anticipated to occur around mid-October of the same year. Under the most favorable circumstances, the peak number of severely hospitalized patients could be reduced by 43% (n=480) compared to the scenario without vaccine intervention (n=849). Depending on outbreak trends and vaccination strategies, the best timing for vaccination in terms of minimizing this peak varies from May 2023 to August 2023. Our findings suggest that if the epidemic persist, the best timing for administering vaccinations would need to be earlier than currently outlined in the Korean plan. It is imperative to continue monitoring outbreak trends, as this is key to determining the best vaccination timing in order to manage potential future surges.

Quantitative analysis of SARS-CoV-2 RNA in wastewater and evaluation of sampling frequency during the downward period of a COVID-19 wave in Japan

Wastewater-based epidemiology (WBE) is a practical approach for detecting the presence of SARS-CoV-2 infections and assessing the epidemic trend of the coronavirus disease 2019 (COVID-19). The purpose of this study was to evaluate the minimum sampling frequency required to properly identify the COVID-19 trend during the downward epidemic period when using a highly sensitive RNA detection method. WBE was conducted using the Efficient and Practical virus Identification System with ENhanced Sensitivity for Solids (EPISENS-S), a highly sensitive SARS-CoV-2 RNA detection method, at nine neighboring wastewater treatment plants (WWTPs). These WWTPs were in the same prefecture in Japan, and they had different sewer types, sampling methods, and sampling frequencies. The overall detection rate of SARS-CoV-2 RNA was 97.8 % during the entire study period when the geometric means of new COVID-19 cases per 100,000 inhabitants were between 3.3 and 7.7 in each WWTP. The maximum SARS-CoV-2 RNA concentration in wastewater was 2.14 × 104 copies/L, which corresponded to pepper mild mottle virus (PMMoV)–normalized concentrations of 6.54 × 10−3. We evaluated the effect of sampling frequencies on the probability of a significant correlation with the number of newly reported COVID-19 cases by hypothetically reducing the sampling frequency in the same dataset. When the wastewater sampling frequency occurred 5, 3, 2, and 1 times per week, these results exhibited significant correlations of 100 % (5/5), 89 % (8/9), 85 % (23/27), and 48 % (13/27), respectively. To achieve significant correlation with a high probability of over 85 %, a minimum sampling frequency of twice per week is required, even if sampling methods and sewer types are different. WBE using the EPISENS-S method and a sampling frequency of more than twice a week can be used to properly monitor COVID-19 wave epidemic trends, even during downward periods.

The epidemiology of patients with toxoplasmosis and its associated risk factors in Taiwan during the 2007-2020 period.

Toxoplasmosis is a zoonotic parasitic disease caused by the protozoan Toxoplasma gondii (T. gondii), and may cause miscarriage and birth defects during pregnancy. This study aimed to assess the epidemiological features, epidemic trends, and correlations between the before number of confirmed toxoplasmosis cases in Taiwan from 2007 to 2020 in gender, age, season, and residential area, and hypothesized the environmental and climate factors also might affect the disease in Taiwan. This study reviewed publicly available annual summary data on reported toxoplasmosis cases in the Taiwan Centers for Diseases Control (TCDC) between 2007 and 2020. This study collected 150 confirmed domestic and nine patients with imported toxoplasmosis. There was an increasing trend in the incidence of toxoplasmosis, 0.09-0.89 cases per 1,000,000 people, peaking in 2017. The average annual toxoplasmosis incidence was 4.4, 13, and 18 during 2007-2011, 2012-2016, and 2017-2020, respectively. Comparing sex, age, season, and place of residence, the incidence rate was highest in male, 20-39 years-old patients, summer, and the eastern region, with 1.02, 1.72, 0.38, and 3.63 cases per million population, respectively. Additionally, comparing the distribution of cases by age group in Taiwan, there were significant differences between 40-59 years-old in the northern region (odds ratio (OR) = 0.343, 95% confidence interval (CI) = 0.167-0.705, p = 0.004) and 40-59 years-old in the southern region (OR = 4.488, and 95% CI = 2.149-9.374, p< 0.001), respectively. Linear regression analysis also showed that PM (particulate matter) 2.5 (μg/m3) concentration was positively associated with toxoplasmosis (β = 0.095, p = 0.037). CO concentration was negatively correlated with toxoplasmosis (β = -14.001, p = 0.021). This study is the first to report domestic and confirmed cases of imported toxoplasmosis from the surveillance data of the TCDC between 2007 and 2020. It identified that residence and age were associated with an increased risk of toxoplasmosis in Taiwan. This study confirmed that toxoplasmosis remains a prevalent infectious disease in Taiwan, its epidemic is gradually increasing and becoming more severe. These findings might be useful for policy-makers and clinical experts to direct prevention and control activities to patients with T. gondii, which causes the most severe illness and greatest burden to Taiwanese people.

Dynamic analysis of rabies transmission and elimination in mainland China

Rabies is an acute zoonotic infectious disease caused by rabies virus. In 2015, the World Health Organization proposed the goal of eliminating dog-induced human rabies by 2030. In response to this goal positively, China has been dedicated to the control and elimination of rabies mainly caused by dogs, for nearly 10 years. By applying infectious disease dynamics, in this paper, we establish a dog-human rabies transmission model to forecast future epidemic trends of rabies, assess whether the goal of eliminating dog-induced human rabies cases in China can be achieved in 2030, and further evaluate and suggest the follow-up sustained preventive measures after the elimination of human rabies. By analyzing and simulating above dynamic model, it is concluded that rabies has been well controlled in China in recent years, but dog-induced human rabies cannot be eliminated by 2030 according to current situation. In addition, we propose to improve rabies control efforts by increasing the immunization coverage rate of rural domestic dogs, controlling the number of stray dogs and preventing the import of rabies virus in wild animals. Immunization coverage rate of rural domestic dogs which is currently less than 10% is far from requirement, and it needs to reach 50%–60% to meet the goal of 2030. Since it is difficult to immunize stray dogs, we suggest to control the number of stray dogs below 15.27 million to achieve the goal. If the goal of eliminating human rabies is reached in 2030, the essential immunization coverage needs to be maintained for 18 years to reduce the number of canine rabies cases to zero. Lastly, to prevent transmission of rabies virus from wild animals to dogs, the thresholds of the number of dogs and the immunization coverage rate of dogs after eliminating canine rabies cases are also discussed.

Circulation of SARS-CoV-2 Omicron sub-lineages revealed by multiplex genotyping RT-qPCR assays for sewage surveillance

Sewage surveillance has proven to be an essential complementary tool to clinical diagnosis in combating the COVID-19 pandemic by tracking the spread of the SARS-CoV-2 virus and evaluating infection levels in populations. With the striking spreading and continuous evolution of SARS-CoV-2 Omicron VOC that characterized with higher transmissibility and potential immune evasion, there is an urgent need for the rapid surveillance of this prevalent strain and its sub-lineages in sewage. In this study, based on three multiplex allele-specific (AS) RT-qPCR assays, we established a rapid and high-throughput detection workflow for the simultaneous discrimination of Omicron sub-lineages BA.2.2, BA.2.12.1, BA.4 and BA.5 (hereafter referred to as BA.4/BA.5) to track their community circulation in Hong Kong. All primer-probe sets in the multiplex assays could correctly discriminate and quantitate their target genotypes with high sensitivity and specificity, even when multiple variants co-existed in the sewage samples. Using the established multiplex assays, the trends of SARS-CoV-2 total viral load and variant dynamics in influent samples collected from 11 wastewater treatment plants (WWTPs) during June 2022 and September 2022, aligned with the clinical data, successfully unveiling the swift emergence and predominance of Omicron BA.4/BA.5 in Hong Kong. The study highlights the feasibility and applicability of multiplex RT-qPCR assays for monitoring epidemic trends and tracking variant displacement dynamics in sewage samples, providing a more rapid, high-throughput and cost-effective alternative to enhance the current sewage surveillance system.

Study on the influencing factors of piecewise multi-strain crossover epidemic spread under data contamination

The ongoing impact of the novel coronavirus disease 2019 (COVID-19) on work and daily life persists as we transition from emergency to normal circumstances. The continuous mutation of viral strains has resulted in a shift from a single strain to multiple cross-strains, contributing to the spread of the epidemic. Variations in infection rates of the same strain occur because of the implementation of diverse preventive measures at different times. This study investigated the dynamics of the pandemic in the presence of concurrent strains. Building on the classical Susceptible, Exposed, Infected, and Recovered (SEIR) model, a robust piecewise multi-strain cross-epidemic trend prediction model was proposed that employs the Hodges–Lehmann estimator to handle uncertain and contamination-prone epidemic information. A comparative analysis of epidemic spread trend curves across diverse populations using different robust methods revealed the superiority of the Hodges–Lehmann estimator-based model over the traditional method. The accurate prediction results of the model demonstrate its high reliability in tracking the changing trend of the COVID-19 outbreak, thereby supporting its implementation in subsequent epidemic prevention and control measures.

Molecular epidemiological study on rubella virus circulating in Yunnan Province during 2011-2021

Objective: To understand the genotype distribution and transmission pattern of rubella virus (RuV) circulating in Yunnan Province. Methods: Throat swab samples were collected from rubella outbreaks and sporadic cases in nine prefectures/cities of Yunnan Province from 2011 to 2021. Virus isolation, amplification of target genes and sequence determination were performed on the RuV-positive samples. The genotypes and lineages of Yunnan strains were determined by comparing them with the reference strains, and further phylogenetic analysis was performed with Yunnan strains and strains circulating in other provinces of China during the same period. Results: RuV circulating in Yunnan province during 2011-2021 showed significant genetic diversity, and three lineages, 1E-L1, 2B-L1 and 1E-L2, were detected. Two lineage-switches were also identified, including the conversion of 1E-L1 to 2B-L1 between 2012 and 2013, and the replacement of 2B-L1 to 1E-L2 after 2018. The time of the switches was basically consistent with the outbreak in Yunnan province in 2012 and the time of the rubella reemergence and epidemic between 2018 and 2019. The amino acid sequence of RuV virus strains in Yunnan province was highly conserved, and no important functional regions were changed. Conclusions: The transmission pattern of RuV in Yunnan province is generally consistent with the epidemic trend of RuV in other provinces of China.

Changes in Influenza Activities Impacted by NPI Based on 4-Year Surveillance in China: Epidemic Patterns and Trends.

Since the Non-pharmaceutical Intervention (NPI) by COVID-19 emerged, influenza activity has been somewhat altered. The aim of this study was to explore changes in influenza activities in the context of COVID-19 based on the sentinel hospitals/units in Guangdong, southern China. The surveillance data in influenza-like illness (ILI) were collected from 21 cities in Guangdong between September 2017 and August 2021, while 43 hospitals/units were selected to analyze the predominant types of influenza, population characteristics, and seasonal features by three methods (the concentration ratio, the seasonal index, and the circulation distribution), based on a descriptive epidemiological approach. During the four consecutive influenza seasons, a total of 157345 ILIs were tested, of which 9.05% were positive for influenza virus (n = 14238), with the highest positive rates for both IAV (13.20%) and IBV (5.41%) in the 2018-2019 season. After the emergence of COVID-19, influenza cases decreased near to zero from March 2020 till March 2021, and the dominant type of influenza virus changed from IAV to IBV. The highest positive rate of influenza existed in the age-group of 5 ~ < 15years in each season for IAV (P < 0.001), which was consistent with that for IBV (P < 0.001). The highest annual positive rates for IBV emerged in eastern Guangdong, while the highest annual positive rates of IAV in different seasons existed in different regions. Furthermore, compared with the epidemic period (ranged from December to June) during 2017-2019, the period ended three months early (March 2020) in 2019-2020, and started by five months behind (April 2021) during 2020-2021. The highest positive rates in 5 ~ < 15 age-group suggested the susceptible in this age-group mostly had infected with infected B/Victoria. Influenced by the emergence of COVID-19 and NPI responses, the epidemic patterns and trends of influenza activities have changed in Guangdong, 2017-2021.

Epidemic Trends and Biofilm Formation Mechanisms of Haemophilus influenzae: Insights into Clinical Implications and Prevention Strategies.

Haemophilus influenzae (H. influenzae) is a significant pathogen responsible for causing respiratory tract infections and invasive diseases, leading to a considerable disease burden. The Haemophilus influenzae type b (Hib) conjugate vaccine has notably decreased the incidence of severe infections caused by Hib strains, and other non-typable H. influenzae (NTHi) serotypes have emerged as epidemic strains worldwide. As a result, the global epidemic trends and antibiotic resistance characteristics of H. influenzae have been altered. Researches on the virulence factors of H. influenzae, particularly the mechanisms underlying biofilm formation, and the development of anti-biofilm strategies hold significant clinical value. This article provides a summary of the epidemic trends, typing methods, virulence factors, biofilm formation mechanisms, and prevention strategies of H. influenzae. The increasing prevalence of NTHi strains and antibiotic resistance among H. influenzae, especially the high β-lactamase positivity and the emergence of BLNAR strains have increased clinical difficulties. Understanding its virulence factors, especially the formation mechanism of biofilm, and formulating effective anti-biofilm strategies may help to reduce the clinical impact. Therefore, future research efforts should focus on developing new approaches to prevent and control H. influenzae infections.

Epidemic trends of dyslipidemia in young adults: a real-world study including more than 20,000 samples

BackgroundThere is an urgent need to learn more about the epidemiological features of dyslipidemia in youth to address the high burden of cardiovascular disease.MethodsThis experiment was an observational, cross-sectional study. The samples were collected from 22,379 college students at Xinjiang Medical University.ResultThe overall prevalence of dyslipidemia was 13.17%, which was significantly higher in men (23%) than in women (7.2%), p < 0.01. Similarly, the prevalence rate of obesity in men (11.4%) was significantly higher than that in women (3.4%). The composition of blood lipids, such as triglyceride (TG), total cholesterol (TC), and low density lipoprotein cholesterol (LDL-C), began to increase gradually from the age of 22 and showed a sharp increase after the age of 30; however, a reverse trend was present in high density lipoprotein cholesterol (HDL-C). In terms of the proportion of dyslipidemia in both men and women, low HDL-C accounted for the largest proportion (74%), followed by elevated TGs (14.5%). The overall distribution of rates of dyslipidemia and excess weight showed a U-shaped trend with increasing age, with the lowest rates seen in the 20–24 age group.ConclusionOur study sheds light on the epidemiological features of dyslipidemia in young adults and enriches the limited data available on dyslipidemia, providing a reference for the close monitoring and control of risk factors to reduce the occurrence and progression of atherosclerotic cardiovascular disease events.

Using the Baidu index to predict trends in the incidence of tuberculosis in Jiangsu Province, China.

To analyze the time series in the correlation between search terms related to tuberculosis (TB) and actual incidence data in China. To screen out the "leading" terms and construct a timely and efficient TB prediction model that can predict the next wave of TB epidemic trend in advance. Monthly incidence data of tuberculosis in Jiangsu Province, China, were collected from January 2011 to December 2020. A scoping approach was used to identify TB search terms around common TB terms, prevention, symptoms and treatment. Search terms for Jiangsu Province, China, from January 2011 to December 2020 were collected from the Baidu index database. Correlation coefficients between search terms and actual incidence were calculated using Python 3.6 software. The multiple linear regression model was constructed using SPSS 26.0 software, which also calculated the goodness of fit and prediction error of the model predictions. A total of 16 keywords with correlation coefficients greater than 0.6 were screened, of which 11 were the leading terms. The R2 of the prediction model was 0.67 and the MAPE was 10.23%. The TB prediction model based on Baidu Index data was able to predict the next wave of TB epidemic trends and intensity 2 months in advance. This forecasting model is currently only available for Jiangsu Province.