Epidemic Trends Research Articles

Assessment of Sleep Quality in Adolescents with Overweight and Obesity Using the Adolescent Sleep Hygiene Scale (ASHS)

Background: Adolescent overweight and obesity are a public health problem with an epidemic trend. There is growing evidence that sleep quality correlates to body weight. The aim of this study was to investigate, sleep quality in adolescents with obesity/overweight. Methods: A total of 100 adolescents with overweight/obesity aged 12–18 years were enrolled. Anthropometric parameters were recorded and a laboratory investigation in the fasting state [glucose, insulin, cholesterol (TC), high-density lipoprotein cholesterol (HDL), low-density lipoprotein cholesterol (LDL), triglycerides, uric acid and glycated hemoglobin (HbA1c)] was performed. Insulin resistance was calculated by the Homeostasis Model Assessment of Insulin Resistance index (HOMA-IR). Sleep quality was assessed with the Adolescent Sleep Hygiene Scale (ASHS) questionnaire. Results: According to ASHS, 93% of the participants were classified as “Good Sleepers” (GSs) (score > 3.8) and 7% as “Poor Sleepers” (PSs) (score < 3.8). PSs had a statistically higher body mass index (BMI) compared to GSs (p = 0.026). Increased body mass index (BMI) (r = −0.306, p = 0.002), fast insulin (r = −0.224, p = 0.027), and HOMA-IR (r = −0.260, p = 0.010) exerted a negative effect on sleep quality. Controlling for lipids and uric acid, only TC levels appeared to have a statistically significant and specifically positive correlation with the ASHS score (r = 0.202, p = 0.045). HbA1c values and waist circumference tended to be negatively correlated, but not significant to adolescent sleep quality [(r = −0.101, p = 0.330), (r = −0.095, p = 0.359), respectively]. The influence of central obesity on the ASHS score was also explored, but no correlation was found (p = 0.566). Conclusions: Sleep quality, as reflected by the ASHS score, was associated negatively with BMI, fasting insulin levels, and insulin resistance. Furthermore, a gender difference was observed, as adolescent males were found to achieve a higher overall ASHS score compared to females.

Evaluation of wastewater percent positive for assessing epidemic trends - A case study of COVID-19 in Shangrao, China

Evaluation of wastewater percent positive for assessing epidemic trends - A case study of COVID-19 in Shangrao, China

Epidemiological, clinical, and genotypic characteristics of pediatric Mycoplasma pneumoniae infections: an 8-year survey in Suzhou, China in the pre- and post-COVID-19 eras.

This study examines the epidemiology of Mycoplasma pneumoniae (M. pneumoniae) infections among children in Suzhou, China, during various pandemic phases. The goal is to discern evolving epidemic trends and to furnish robust evidence for clinical diagnosis and treatment. From January 1, 2016, to December 31, 2023, 113,625 consecutive patients with respiratory infections from three hospitals in Suzhou, China (Children's Hospital of Soochow University, Children's Hospital of Wujiang District, and Affiliated Suzhou Hospital of Nanjing University Medical School), were retrospectively enrolled in a surveillance study. Additionally, in 2023, children hospitalized with M. pneumoniae pneumonia at the Children's Hospital of Soochow University were tested for genotype (P1 gene typing, SNP genotyping) and macrolide resistance in their bronchoalveolar lavage fluid. From 2016 to 2023, the M. pneumoniae positive detection rate among pediatric respiratory infections fell from a pre-pandemic 21.1% to pandemic lows, then surged to 45.3% post-pandemic. Before the pandemic, peak M. pneumoniae infection rates occurred in summer, followed by autumn. Post-pandemic, the highest peak rates were in autumn. Peak M. pneumoniae detection rates occurred in 2019 and 2023, with a notable increase in children aged 6 and older in 2023. In this study, 200 M. pneumoniae-positive bronchoalveolar lavage fluid (BALF) cases in 2023 were randomly selected and analyzed for P1 genotype and SNP genotype. Among 156 cases, 81.4% were P1 genotype and 18.6% were P2 genotype. The proportion of severe M. pneumoniae pneumonia with the P1 type was significantly higher than that with the P2 type (p < 0.05). Of the 192 samples analyzed, 11 SNP genotypes were identified, with SNP-27 predominating (36.5%), followed by SNP-0 (21.4%), SNP-11 (18.8%), and SNP-34 (17.7%). Of the 192 BALF specimens, 97.3% exhibited macrolide resistance mutations, with A2063G mutations at 96.17%. The mutation rates for the 23S rRNA 2064 and 2,617 were 1.6 and 1.0%, respectively. Post-COVID-19 in Suzhou, China, M. pneumoniae infection patterns shifted significantly, with initial NPIs-induced declines followed by a sharp rise in cases, especially impacting school-age children. This trend underscores the importance of ongoing epidemiological surveillance and the development of strategic public health responses.

Non-linear dynamics and control of COVID-19 in india revisited : evidence of synergistic, antagonistic and threshold effects

Various researchers have investigated the COVID-19 pandemic dynamics since 2020 focusing on various aspects of the epidemics. However, the non-linear dynamics of this epidemic is still not well understood. India having the largest population in the world with wide cultural, genomics, and immunity diversity is being considered a suitable case for present investigation to generate the crucial information of non-linear phenomena during the first two waves of the epidemics. An SIRD model is suggested, integrating varying transmission, recovery, and mortality rates over time to explore the nonlinear behavior of the epidemic in India. The time-dependent transmission, recovery, and mortality are calculated using the finite difference method with the aid of actual data released by the Indian government, for the periods before and during the lockdown phases. The novel results generated from the model show evidence of synergistic, antagonistic, and threshold effects and the role of various measures in controlling these epidemic trends.

An adaptive weight ensemble approach to forecast influenza activity in an irregular seasonality context

Forecasting influenza activity in tropical and subtropical regions, such as Hong Kong, is challenging due to irregular seasonality and high variability. We develop a diverse set of statistical, machine learning, and deep learning approaches to forecast influenza activity in Hong Kong 0 to 8 weeks ahead, leveraging a unique multi-year surveillance record spanning 32 epidemics from 1998 to 2019. We consider a simple average ensemble (SAE) of the top two individual models, and develop an adaptive weight blending ensemble (AWBE) that dynamically updates model contribution. All models outperform the baseline constant incidence model, reducing the root mean square error (RMSE) by 23%–29% and weighted interval score (WIS) by 25%–31% for 8-week ahead forecasts. The SAE model performed similarly to individual models, while the AWBE model reduces RMSE by 52% and WIS by 53%, outperforming individual models for forecasts in different epidemic trends (growth, plateau, decline) and during both winter and summer seasons. Using the post-COVID data (2023–2024) as another test period, the AWBE model still reduces RMSE by 39% and WIS by 45%. Our framework contributes to comparing and benchmarking models in ensemble forecasts, enhancing evidence for synthesizing multiple models in disease forecasting for geographies with irregular influenza seasonality.

Analysis of the epidemiological trends and spatial patterns of esophageal cancer among male populations in China's mainland from 1990 to 2021.

This study aimed to delineate the temporal patterns of esophageal cancer epidemic trends and spatial clustering patterns among male populations in China's mainland from 1990 to 2021. This analysis aimed to provide a scientific rationale and empirical data to facilitate the formulation of targeted prevention and control strategies. Data on the number of cases and deaths, crude and age-standardized incidence and mortality rates of esophageal cancer in men were collected from the Global Burden of Disease Study and the Chinese Cancer Registry Annual Report. Global and local Moran's I spatial autocorrelation index was employed to quantify spatial clustering, and a disease map was drawn. From 1990 to 2021, the cumulative incidence and mortality of esophageal cancer in men were 6 100 342 and 5 972 294, respectively. The crude incidence and death rates increased in 2021, yet the age-standardized rates decreased significantly. Cixian County in Hebei Province had the highest age-standardized rates. The disease displayed spatial clustering, with relatively high rates in Shandong, Jiangsu, and Hebei Provinces. Since 1990, the incidence and mortality of esophageal cancer among men in mainland China have remained high, imposing a considerable burden. Although age-adjusted rates have declined, they are still relatively high overall, especially in Shandong, Hebei, and Jiangsu Provinces.

Vaccination subsidy allocation under budget constraints considering the human interpersonal contact pattern and vaccine protection effect in epidemics

Vaccination subsidy is one of the most important measures to increase the willingness of people to be vaccinated for epidemic prevention and control. Given the budget constraint of vaccination subsidy and the different roles of various age groups population in the epidemic transmission process, a novel approach is proposed to optimize the utilization of the limited vaccination subsidy budget. First, an age-structured SIR model that integrates the human interpersonal contact pattern and vaccine protection effect is developed to simulate the epidemic trend under a certain vaccination state. Then, a decision model is constructed to determine the optimal vaccination subsidy allocation scheme for various age groups based on the age-structured SIR model. A case study of Wuhan City is used to verify the decision approach, and a series of sensitivity analyses are conducted. The result shows that the peak time of an epidemic is positively related to the total treatment cost for all the infections, and more subsidies should be allocated to the populations with higher interpersonal contact frequencies for epidemic prevention and control.

Predictive ARIMA Model with a Machine Learning (ML) Approach for COVID-19 Data in Pakistan

This study is based on the application of an ARIMA (p, d, q) based machine learning (ML) approach to evaluate the dynamics of COVID-19 pandemic. The focus is on estimating epidemic trends and performing diagnostic scrutiny with model fitting. The data including all four waves of the pandemic pertaining to Pakistan, covering all four provinces (Sindh, Punjab, Khyber Pakhtunkhwa, Balochistan, as well as Gilgit Baltistan, Azad Jammu Kashmir, and the capital city Islamabad, collected from February 26, 2020, to September 30, 2021, is analyzed. The ML algorithm is used to optimize the results of ADF, unit root test which ensures the minimum of ACF, and PACF graphs intention of the data series. The results employ the fitted ARIMA models (1, 1, 1) and (1, 1, 7) for the 1st to 4th waves, confirming daily infected cases across the entire dataset of Pakistan. The cumulative trained observations are from the 1st wave (February 26, 2020, to October 20, 2020), 2nd wave (October 21, 2020, to March 16, 2021), 3rd wave (March 17, 2021, to July 10, 2021), and 4th wave (July 11, 2021, to September 30, 2021), with a further 14-day forecast (from October 1 to October 14, 2021). The results show a strong correlation between the trained and predicted values, ranging from 0.8789 to 0.99236. To select predictive model parameters, the model that results in the minimum Bayesian Information Criterion (BIC) value and residuals from the datasets obtained after detaching the unnecessary errors and the 95^% CI for the forecasting error ( ) are calculated. These values would help to decide the best fitted predictive model.

Early warning and predicting of COVID-19 using zero-inflated negative binomial regression model and negative binomial regression model

BackgroundIt is difficult to detect the outbreak of emergency infectious disease based on the exiting surveillance system. Here we investigate the utility of the Baidu Search Index, an indicator of how large of a keyword is in Baidu’s search volume, in the early warning and predicting the epidemic trend of COVID-19.MethodsThe daily number of cases and the Baidu Search Index of 8 keywords (weighted by population) from December 1, 2019 to March 15, 2020 were collected and analyzed with times series and Spearman correlation with different time lag. To predict the daily number of COVID-19 cases using the Baidu Search Index, Zero-inflated negative binomial regression was used in phase 1 and negative binomial regression model was used in phase 2 and phase 3 based on the characteristic of independent variable.ResultsThe Baidu Search Index of all keywords in Wuhan was significantly higher than Hubei (excluded Wuhan) and China (excluded Hubei). Before the causative pathogen was identified, the search volume of “Influenza” and “Pneumonia” in Wuhan increased with the number of new onset cases, their correlation coefficient was 0.69 and 0.59, respectively. After the pathogen was public but before COVID-19 was classified as a notifiable disease, the search volume of “SARS”, “Pneumonia”, “Coronavirus” in all study areas increased with the number of new onset cases with the correlation coefficient was 0.69 ~ 0.89, while “Influenza” changed to negative correlated (rs: -0.56 ~ -0.64). After COVID-19 was closely monitored, the Baidu Search Index of “COVID-19”, “Pneumonia”, “Coronavirus”, “SARS” and “Mask” could predict the epidemic trend with 15 days, 5 days and 6 days lead time, respectively in Wuhan, Hubei (excluded Wuhan) and China (excluded Hubei). The predicted number of cases would increase 1.84 and 4.81 folds, respectively than the actual number of cases in Wuhan and Hubei (excluded Wuhan) from 21 January to 9 February.ConclusionThe Baidu Search Index could be used in the early warning and predicting the epidemic trend of COVID-19, but the search keywords changed in different period. Considering the time lag from onset to diagnosis, especially in the areas with medical resources shortage, internet search data can be a highly effective supplement of the existing surveillance system.

Research on influenza epidemic and clinical characteristics based on influenza research database.

To compare the epidemic trends of different types of influenza viruses and the clinical characteristics of patients, so as to provide reference for influenza prevention and control. This was descriptive research. The human monitoring data collected from the Influenza Research Database (IRD) from 2006 to 2016 were used to descriptively analyze the distribution of influenza viruses in terms of time, geography, gender and age. The positive samples were divided into three groups based on the type of pathogen (H1N1 influenza A viruses, H3N2 influenza A viruses, and influenza B viruses). Compared and analyzed the distribution and clinical characteristics among groups. There were statistically significant differences in the positive rates among different countries (p< 0.001). The proportion of positive samples gradually decreased with age. The proportion of oseltamivir resistance was significantly higher in H1N1-positive patients compared with that in H3N2-positive patients (p< 0.001). Significant differences were observed in the vaccination status among H1N1, H3N2 and influenza B viruses (p< 0.001). Cough was common in all cases with H1N1, H3N2 and influenza B infections, while cough, fever and running nose occurred more frequently in influenza B-positive cases than those of H1N1-positive and H3N2-positive cases (p< 0.001). People aged 0-18 years are the major susceptible population to influenza, and H1N1 influenza viruses are the main pathogens of infection in this population, with major clinical manifestations of fever, cough and headache. The findings in this study highlight the necessity to strengthen the protection for this age group in clinical practice.

Spatiotemporal Trends in Human Immunodeficiency Virus Epidemics in Nigeria: A Systematic Review Protocol

The scourge of the Human Immunodeficiency Virus infection is not abating fast as expected despite the many strategies put in place by various stakeholders. Geographical variations in its distribution have been implicated in its slow decline in sub-Saharan Africa, and Nigeria carries the highest burden in West Africa with subnational variations in 2019. This may negatively impact the implementation of interventions if not properly targeted. Geospatial technique is very important for spatial identification of hotspots and clusters which makes it easy to implement strategies, allocate scares resources, interventions and programs to achieve a success in the fight against the epidemics. It is on this note that the researcher is set to conduct this systematic review to assess trends in human immunodeficiency virus epidemics in Nigeria. The protocol will follow the preferred reporting items for systematic review and meta-analysis (PRISMA-P) 2020 guideline. The protocol was registered with the International Prospective Register for Systematic Reviews (PROSPERO) with registration number CRD42023488081. Articles would be retrieved electronically through database such as PubMed, Medline, Scopus, web of Science citation index, Google Scholar, and the open grey. Search period would be December 15th 2023 to June 15th, 2024 for related published articles. Two researchers will independently review articles for inclusion and if there are any disagreements, a third researcher would be called. Risk of bias would be assessed using New Castle Ottawa Scale and JBI critical appraisal checklist. Furthermore, statistical analysis will be conducted using the Review Manager Version 5.4. In conclusion, the Identification of clusters/hotspots helps the targeted allocation of scares resources, implementation of interventions and programs more efficiently and effectively. This strategy combined with the already existing ones, is a sure way to win the fight against the HIV epidemics.

Estimating epidemic trajectories of SARS-CoV-2 and influenza A virus based on wastewater monitoring and a novel machine learning algorithm

The COVID-19 pandemic has altered the circulation of non-SARS-CoV-2 respiratory viruses. In this study, we carried out wastewater surveillance of SARS-CoV-2 and influenza A virus (IAV) in three key port cities in China through real-time quantitative PCR (RT–qPCR). Next, a novel machine learning algorithm (MLA) based on Gaussian model and random forest model was used to predict the epidemic trajectories of SARS-CoV-2 and IAV. The results showed that from February 2023 to January 2024, three port cities experienced two waves of SARS-CoV-2 infection, which peaked in late-May and late-August 2023, respectively. Two waves of IAV were observed in the spring and winter of 2023, respectively with considerable variations in terms of onset/offset date and duration. Furthermore, we employed MLA to extract the key features of epidemic trajectories of SARS-CoV-2 and IAV from February 3rd, to October 15th, 2023, and thereby predicted the epidemic trends of SARS-CoV-2 and IAV from October 16th, 2023 to April 22nd, 2024, which showed high consistency with the observed values. These collective findings offer an important understanding of SARS-CoV-2 and IAV epidemics, suggesting that wastewater surveillance together with MLA emerges as a powerful tool for risk assessment of respiratory viral diseases and improving public health preparedness.

Epidemiological trends in respiratory pathogens infections among children post-COVID-19: A cross-sectional study

ObjectiveThe aim of this study was to investigate the epidemiological trend of respiratory pathogens infections among children after the Coronavirus Disease 2019 (COVID-19) pandemic. MethodsThis study enrolled 575,373 children who came to our hospital for relevant respiratory pathogen antigen/antibody testing due to respiratory symptoms such as fever and cough. The demographic and laboratory data, including age, gender, testing time, and influenza A virus (IAV), influenza B virus (IBV), respiratory syncytial virus (RSV), adenovirus (ADV), and Mycoplasma pneumonia (MP) results, were collected from electronic medical records. SPSS (version 21.0) and GraphPad Prism 9 software were used for statistical analysis and figure creation. Results79,746 children tested positive for IAV IgM, and 3196 children tested positive for IBV IgM, with an overall positive rate of 28.5 % for IAV and 1.1 % for IBV. IAV infections peaked at 21,502 cases in March 2023. 80,699 children underwent RSV IgM testing from April to October 2023, with 5726 (7.1 %) testing positive. The apex of RSV infections occurred in May 2023, with 2140 cases. Regarding ADV, 100,460 children underwent testing from April to October 2023, with 1981 (11.9 %) testing positive. The pinnacle of ADV infections reached 4546 cases in November 2023. Concerning MP, 474,913 children underwent MP testing, with 73,833 (15.5 %) testing positive. The zenith of MP infections occurred in November 2023, with 25,291 cases. Further analysis revealed that the outbreaks of these pathogens are occurring earlier than in previous years. Additionally, our data showed that children aged >3 years accounted for 79.6 %, 87.8 %, 88.6 %, and 77.8 % of the total IAV‐positive, IBV‐positive, ADV-positive, and MP-positive children, respectively. Conversely, RSV primarily infected children <6 years. ConclusionVarious respiratory pathogens showed an epidemic trend in children among children post-COVID-19. These results indicated that we should pay timely attention to the epidemiological trends and characteristics of respiratory pathogens in children after the COVID-19 pandemic and provide relevant information for society and clinical practice.

Cyy-272, an indazole derivative, effectively mitigates obese cardiomyopathy as a JNK inhibitor

Obesity has shown a global epidemic trend. The high-lipid state caused by obesity can maintain the heart in a prolonged low-grade inflammatory state and cause ventricular remodeling, leading to a series of pathologies, such as hypertrophy, fibrosis, and apoptosis, which eventually develop into obese cardiomyopathy. Therefore, prolonged low-grade inflammation plays a crucial role in the progression of obese cardiomyopathy, making inflammation regulation an essential strategy for treating this disease. Cyy-272, an indazole derivative, is an anti-inflammatory compound independently synthesized by our laboratory. Our previous studies revealed that Cyy-272 can exert anti-inflammatory effects by inhibiting the phosphorylation and activation of C-Jun N-terminal kinase (JNK), thereby alleviating lipopolysaccharide (LPS)-induced acute lung injury (ALI). The current study aimed to evaluate the potential of Cyy-272 to mitigate the occurrence and progression of obese cardiomyopathy through the inhibition of the JNK signaling pathway. Our results indicate that the compound Cyy-272 has encouraging therapeutic effects on obesity-induced cardiac injury. It significantly inhibits inflammation in cardiomyocytes and heart tissues induced by high lipid concentrations, further alleviating the resulting hypertrophy, fibrosis, and apoptosis. Mechanistically, the protective effect of Cyy-272 on obese cardiomyopathy can be attributed to its direct inhibition of JNK protein phosphorylation. In conclusion, we identified a novel compound, Cyy-272, capable of alleviating obese cardiomyopathy and confirmed that its effect is achieved through direct inhibition of JNK.

Predicting the epidemiological trend of acute hemorrhagic conjunctivitis in China using Bayesian structural time-series model

This study aims to explore the application value of the Bayesian Time Structure Sequence (BSTS) model in estimating the acute hemorrhagic conjunctivitis (AHC) epidemics. The reported AHC cases spanning from January 2011 to October 2022 in China were collated. Utilizing R software, the BSTS and Autoregressive Integrated Moving Average (ARIMA) models were constructed using the data from January 2011 to December 2021. The prediction effect of both models was compared using the data from January to October 2022, and finally the AHC incidence from November 2022 to December 2023 was predicted. The results indicated that forecast errors under the BSTS model were lower than those under the ARIMA model. The actual AHC incidence in July 2022 from the ARIMA model deviated from the 95% confidence interval (CI) of the predicted value. However, the observed AHC incidence from the BSTS model fell within the 95% CI of the predicted value. Notably, the BSTS model predicted 26,474 new AHC cases in China from November 2022 to December 2023, exhibiting better prediction performance compared to the ARIMA model. This indicates that the BSTS model possesses a high application value for forecasting the epidemic trends of AHC, making it a valuable tool for disease surveillance and prevention strategies.

Comparison of the latest cancer statistics, cancer epidemic trends and determinants between China and the United States

Objective: To provide supports for the cancer prevention and control strategies in China by comparing the disease burden, epidemic trends, 5-year relative survival rate and major determinants of common cancers between China and the United States. Methods: A descriptive secondary analysis was conducted using data extracted from the GLOBOCAN database, the Surveillance, Epidemiology, and End Results database, Global Burden of disease 2019 database, and previous studies. The main indicators included the cases of malignant tumors in different sites, the cases of deaths, the age-standardized incidence (world standard incidence) and mortality (world standard mortality), the 5-year relative survival rate, and population attributable fraction (PAF). Results: In 2022, an estimated 4.825 million new cases and 2.574 million deaths of malignant neoplasms in China. The world standard incidence rate (201.6/100 000) in China was lower than that in the United States (367.0/100 000), and the world standard mortality rate (96.5/100 000) was higher than that in the United States (82.3/100 000). Lung cancer ranked first in the disease burden of malignant tumors in China, the new cases and deaths accounted for 22.0% and 28.5% of all malignant tumors, respectively. The top three malignant tumors in China were breast cancer (11.5%), prostate cancer (9.7%) and lung cancer (9.5%), which were also among the top five causes of death. However, the second to fifth leading causes of death from malignant tumors in China were digestive system tumors (liver cancer 12.3%, stomach cancer 10.1%, colorectal cancer 9.3%, and esophageal cancer 7.3%). From 2000 to 2018, the world standard incidence of malignant tumors showed an increasing trend and the world standard mortality of malignant tumors showed a decreasing trend in China, while the world standard incidence and mortality of malignant tumors in the United States showed a significant decreasing trend after 2000. The incidence of breast cancer, colorectal cancer and thyroid cancer increased rapidly in China, while the incidence and mortality of stomach cancer, liver cancer and esophageal cancer decreased, but they still had a heavy disease burden. From 2003 to 2015, the overall 5-year relative survival rate of malignant tumors increased from 30.9% to 40.5% in China. However, with the exception of esophageal cancer, the 5-year relative survival rates of other major malignant tumors were lower than those in the United States. In 2019, the PAF of malignant tumors death attributable to potential modifiable risk factors was 48.3% in China, which was similar to the United States (49.8%). Of these, smoking was the most important attributable risk factor, and the PAF was more than 30% both in China and the United States. In addition, about 18.8% of malignant tumors were caused by preventable chronic infections, such as hepatitis B virus and Helicobacter pylori, while less than 4% of malignant tumors in the United States were caused by infection. Conclusions: China has made great progress in the prevention and treatment of malignant tumors, but it still faces a serious disease burden. The cancer spectrum is changing from developing countries to developed countries. We should pay attention to modifiable factors, take comprehensive measures, and prevent cancer scientifically.

Epidemiologic trends and changes in humoral immunity and lymphocyte subsets levels among hospitalized children with Mycoplasma pneumoniae infection during 2019-2023.

To understand the changes in humoral immunity and lymphocyte subsets levels among hospitalized children with Mycoplasma pneumoniae (MP) infection from 2019 to 2023. This study retrospectively analyzed inpatients aged 0-14 years who were diagnosed with MP infection or MP pneumonia in a tertiary hospital from January 2019 to December 2023. The children were divided into three groups: before the implementation of nonpharmaceutical interventions (NPIs), during the implementation of NPIs, and after the NPIs being lifted. A total of 4103 patients were enrolled in this study, of whom 2125 were diagnosed with MP infection and 1978 were diagnosed with MP pneumonia. The number of MP infection cases dramatically decreased early during the implementation of NPIs, and the previous epidemic trend resumed after the NPIs were lifted, with the number of cases during the period 2019-2023 peaked in November 2023. In children aged < 5 years, the levels of IgA and IgM and the percentages of total T cells and cytotoxic T cells in the "before the implementation of NPIs" group were greater than those in the other groups, and the percentage of total B cells was lower than that in the other groups. In children aged ≥ 5 years, the IgM level in the "before the implementation of NPIs" group was greater than that in the other groups. The number of MP-infected hospitalized children decreased significantly after NPI implementation and reached its highest peak during 2019-2023 in November 2023. After the NPIs were lifted, the level of humoral immunity was decreased and balance lymphocyte subsets were disrupted, especially in children aged < 5 years. We should pay close attention to and prevent MP infection in a timely manner after epidemics caused by large respiratory pathogens.

Integrating socio-psychological factors in the SEIR model optimized by a genetic algorithm for COVID-19 trend analysis

The global spread of COVID-19 has profoundly affected health and economies, highlighting the need for precise epidemic trend predictions for effective interventions. In this study, we used infectious disease models to simulate and predict the trajectory of COVID-19. An SEIR (susceptible, exposed, infected, removed) model was established using Wuhan data to reflect the pandemic. We then trained a genetic algorithm-based SEIR (GA-SEIR) model using data from a specific U.S. region and focused on individual susceptibility and infection dynamics. By integrating socio-psychological factors, we achieved a significant enhancement to the GA-SEIR model, leading to the development of an optimized version. This refined GA-SEIR model significantly improved our ability to simulate the spread and control of the epidemic and to effectively track trends. Remarkably, it successfully predicted the resurgence of COVID-19 in mainland China in April 2023, demonstrating its robustness and reliability. The refined GA-SEIR model provides crucial insights for public health authorities, enabling them to design and implement proactive strategies for outbreak containment and mitigation. Its substantial contributions to epidemic modelling and public health planning are invaluable, particularly in managing and controlling respiratory infectious diseases such as COVID-19.

Prevalence, Genotype Diversity, and Distinct Pathogenicity of 205 Gammacoronavirus Infectious Bronchitis Virus Isolates in China during 2019-2023.

Gammacoronavirus infectious bronchitis virus (IBV) causes a highly contagious disease in chickens and seriously endangers the poultry industry. The emergence and co-circulation of diverse IBV serotypes and genotypes with distinct pathogenicity worldwide pose a serious challenge to the development of effective intervention measures. In this study, we report the epidemic trends of IBV in China from 2019 to 2023 and a comparative analysis on the antigenic characteristics and pathogenicity of isolates among major prevalent lineages. Phylogenetic and recombination analyses based on the nucleotide sequences of the spike (S) 1 gene clustered a total of 205 isolates into twelve distinct lineages, with GI-19 as a predominant lineage (61.77 ± 4.56%) exhibiting an overall increasing trend over the past five years, and demonstrated that a majority of the variants were derived from gene recombination events. Further characterization of the growth and pathogenic properties of six representative isolates from different lineages classified four out of the six isolates as nephropathogenic types with mortality rates in one-day-old SPF chickens varying from 20-60%, one as a respiratory type with weak virulence, and one as a naturally occurring avirulent strain. Taken together, our findings illuminate the epidemic trends, prevalence, recombination, and pathogenicity of current IBV strains in China, providing key information for further strengthening the surveillance and pathogenicity studies of IBV.

A review: Data visualization for COVID-19

As COVID-19 raged across the globe, it seriously disrupted social health security, daily work, and the rest of the people, which aroused great panic among all classes. These facts extensively affected the regional circulation and economic growth of the epidemic area. Data visualization, an advanced technology, can help humans understand epidemic transmission trends, identify high-risk areas of outbreak, and evaluate prevention and control policies. Previous essays immediately established databases to organize and build datasets. Other researchers built new visualization models to analyze the causes of virus transmission and pointed out the low efficiency of the existing COVID-19 system response. Although various research and data visualization methods have been used on COVID-19, there has not been a comprehensive, systematic essay that covers the entire span of COVID-19 and its data visualization usage. Therefore, this paper will provide a new systematic and comprehensive perspective to explore the analysis of data visualization to solve COVID-19 data challenges and provide research methods and applications for COVID-19. To be specific, this paper first introduces the characteristics and challenges of COVID-19 epidemic data people may face during the data visualization process, which includes data complexity problems, data timeliness problems, data regional problems, and data quality problems. Then, this paper discusses data visualization methods used by predecessors in previous research. After that, this paper describes the urgent data visualization analysis requirements of COVID-19, including regional differences in virus spread, the influence of temperature on the virus, transmission trends, and death rates for different genders and ages, etc. Finally, this paper reviews applications of data visualization in COVID-19 analysis, followed by earlier-stage prevention, mid-term containment, and later-term reduction of impact. This paper provides an important reference for further research and application of data visualization technology in COVID-19 prevention and control, which brings new ideas for strengthening future epidemic surveillance and data visualization for global epidemic prevention and control.