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Varicella is an acute respiratory infectious diseases, with high transmissibility and quick dissemination. In this study, an SEIR (susceptible-exposed-infected-recovered) dynamic model was established to explore the optimal prevention and control measures according to the epidemiological characteristics about varicella outbreak in a school in a central city of China. Berkeley Madonna 8.3.18 and Microsoft Office Excel 2010 software were employed for the model simulation and data management, respectively. The result showed that the simulated result of SEIR model agreed well with the reported data when 𝛽 (infected rate) equal to 0.067. Models showed that the cumulative number of cases was only 13 when isolation adopted when the infected individuals were identified (assuming isolation rate was up to 100%); the cumulative number of cases was only two and the TAR (total attack rate) was 0.56% when the vaccination coefficient reached 50%. The cumulative number of cases did not change significantly with the change of efficiency of ventilation and disinfection, but the peak time was delayed; when δ (vaccination coefficient) = 0.1, 𝑚 (ventilation efficiency) = 0.7 or δ = 0.2, 𝑚 = 0.5 or δ = 0.3, 𝑚 = 0.1 or δ = 0.4 and above, the cumulative number of cases would reduce to one case and TAR would reduce to 0.28% with combined interventions. Varicella outbreak in school could be controlled through strict isolation or vaccination singly; combined interventions have been adopted when the vaccination coefficient was low.

Background Recently, attention has focused on the impact of global climate change on infectious diseases. Storm flooding is an extreme weather phenomenon that not only impacts the health of the environment but also worsens the spread of pathogens. This poses a significant challenge to public health security. However, there is still a lack of research on how different levels of storm flooding affect susceptible enteric infectious diseases over time. Methods Data on enteric infectious diseases, storm flooding events, and meteorology were collected for Changsha, Hunan Province, between 2016 and 2020. The Wilcoxon Rank Sum Test was used to identify the enteric infectious diseases that are susceptible to storm flooding. Then, the lagged effects of different levels of storm flooding on susceptible enteric infectious diseases were analyzed using a distributed lag nonlinear model. Results There were eleven storm flooding events in Changsha from 2016 to 2020, concentrated in June and July. 37,882 cases of enteric infectious diseases were reported. During non-flooding days, the daily incidence rates of typhoid/paratyphoid and bacillary dysentery were 0.3/100,000 and 0.1/100,000, respectively. During flooding days, the corresponding rates increased to 2.0/100,000 and 0.8/100,000, respectively. The incidence rates of both diseases showed statistically significant differences between non-flooding and flooding days. Correlation analysis shows that the best lags for typhoid/paratyphoid and bacillary dysentery relative to storm flooding events may be 1 and 3 days. The results of the distributed lag nonlinear model showed that typhoid/paratyphoid had the highest cumulative RR values of 2.86 (95% CI: 1.71–4.76) and 8.16 (95% CI: 2.93–22.67) after 4 days of general flooding and heavy flooding, respectively; and bacillary dysentery had the highest cumulative RR values of 1.82 (95% CI: 1.40–2.35) and 3.31 (95% CI: 1.97–5.55) after 5 days of general flooding and heavy flooding, respectively. Conclusions Typhoid/paratyphoid and bacillary dysentery are sensitive enteric infectious diseases related to storm flooding in Changsha. There is a lagging effect of storm flooding on the onset of typhoid/paratyphoid and bacillary dysentery, with the best lagging periods being days 1 and 3, respectively. The cumulative risk of typhoid/paratyphoid and bacillary dysentery was highest at 4/5 days lag, respectively. The higher of storm flooding, the higher the risk of disease, which suggests that the authorities should take appropriate preventive and control measures before and after storm flooding.

Background Mumps is an acute respiratory infectious disease with obvious regional and seasonal differences. Exploring the impact of climate factors on the incidence of mumps and predicting its incidence trend on this basis could effectively control the outbreak and epidemic of mumps. Methods Considering the great differences of climate in the vast territory of China, this study divided the Chinese mainland into seven regions according to the administrative planning criteria, data of Mumps were collected from the China Disease Prevention and Control Information System, ARIMA model and ARIMAX model with meteorological factors were established to predict the incidence of mumps. Results In this study, we found that precipitation, air pressure, temperature, and wind speed had an impact on the incidence of mumps in most regions of China and the incidence of mumps in the north and southwest China was more susceptible to climate factors. Considering meteorological factors, the average relative error of ARIMAX model was 10.87%, which was lower than ARIMA model (15.57%). Conclusions Meteorology factors were the important factors which can affect the incidence of mumps, ARIMAX model with meteorological factors could better simulate and predict the incidence of mumps in China, which has certain reference value for the prevention and control of mumps.

Purpose Diabetic women appear to have adverse pregnancy outcomes. Although there were two meta-analyzes that examined the association between health care and adverse pregnancy outcomes, their results were limited because they only included congenital anomaly and perinatal mortality, and they did not clarify the detailed situations of diabetes and health care. This meta-analysis aims to completely evaluate the effects of health care in improving adverse pregnancy outcomes among diabetic mothers. Methods CNKI, EMBASE, Web of Science, and PubMed databases were searched for eligible studies up to December 2017, without any restrictions. Relevant cohort studies characterizing the relationship between health care and adverse pregnancy outcomes were selected for inclusion in the meta-analysis. We also screened the reference list of relevant studies. The fixed-effect models or random-effect models were used to calculate the risk estimates. The potential sources of heterogeneity were explored by stratified and sensitivity analyzes. Results Twenty-one studies with 6685 cases were included in our analysis. Health care was associated with significantly decreased risk of congenital anomaly (RR 0.237; 95% CI 0.166–0.338), perinatal death (RR 0.457; 95% CI 0.294–0.712), large for gestational age (LGA) (RR 0.794; 95% CI 0.640–0.986), and neonatal hypoglycemia (RR 0.672; 95% CI 0.486–0.929). Publication bias was not found in most results, with the exception of congenital anomaly and small for gestational age (SGA). Conclusion Health care is associated with decreased risk of congenital anomaly, perinatal death, LGA, neonatal hypoglycemia.

Chickenpox is a common acute and highly contagious disease in childhood; moreover, there is currently no targeted treatment. Carrying out an early warning on chickenpox plays an important role in taking targeted measures in advance as well as preventing the outbreak of the disease. In recent years, the infectious disease dynamic model has been widely used in the research of various infectious diseases. The logistic differential equation model can well demonstrate the epidemic characteristics of epidemic outbreaks, gives the point at which the early epidemic rate changes from slow to fast. Therefore, our study aims to use the logistic differential equation model to explore the epidemic characteristics and early-warning time of varicella. Meanwhile, the data of varicella cases were collected from first week of 2008 to 52nd week of 2017 in Changsha. Finally, our study found that the logistic model can be well fitted with varicella data, besides the model illustrated that there are two peaks of varicella at each year in Changsha City. One is the peak in summer–autumn corresponding to the 8th–38th week; the other is in winter–spring corresponding to the time from the 38th to the seventh week next year. The 'epidemic acceleration week' average value of summer–autumn and winter–spring are about the 16th week (ranging from the 15th to 17th week) and 45th week (ranging from the 44th to 47th week), respectively. What is more, taking warning measures during the acceleration week, the preventive effect will be delayed; thus, we recommend intervene during recommended warning weeks which are the 15th and 44th weeks instead.

This study retrospectively analyzed the spatio-temporal distribution and spatial clustering of scarlet fever in mainland China from 2004 to 2017. In recent years, the incidence of scarlet fever is increasing. Previous studies on the spatial distribution of scarlet fever in China are mainly focused at the provincial and municipal levels, and there is few systematic report on the spatial and temporal distribution characteristics of scarlet fever on the national level. Based on the incidence information of scarlet fever in mainland China between 2004 and 2017 collected from the China Center for Disease Control, this paper systematically explored the Spatio-temporal distribution of scarlet fever by three methods, contains spatial autocorrelation analysis, Spatio-temporal scanning analysis, and trend surface analysis. The results demonstrate that the incidence of scarlet fever varies by seasons, which is in line with double-peak distribution.The first peak generally occurs from May to June and the second one from November to December, while February and August is the lowest period of incidence. Trend surface analysis indicates that the incidence of scarlet fever in northern China is higher than the south, slightly higher in western compared to the east, and lower in the central part. Additionally, the results show that the clustering regions of scarlet fever centrally distributed in the northeast, northwest, north china and some provinces in the east, such as Zhejiang, Shanghai, Shandong, and Jiangsu.       

This paper considers the problem of decentralized global stabilization via output feedback for a class of uncertain nonlinear systems with unknown output functions, in which the nonlinear terms are interconnected by unmeasurable states and the inputs of each subsystem. First, we design the controller for each nominal subsystem without the perturbing nonlinearities. Then, we apply the homogeneous domination approach to design scaled homogeneous observers and controllers with an appropriate choice of gain to render the nonlinear system globally asymptotically stable. Finally, two simulation examples are shown to illustrate the effectiveness of the proposed scheme.

In this study, we analysed the relationship between meteorological factors and the number of patients with coronavirus disease 2019 (COVID-19). The study period was from 12 April 2020 to 13 October 2020, and daily meteorological data and the daily number of patients with COVID-19 in each state of the United States were collected. Based on the number of COVID-19 patients in each state of the United States, we selected four states (California, Florida, New York, Texas) for analysis. One-way analysis of variance ( ANOVA), scatter plot analysis, correlation analysis and distributed lag nonlinear model (DLNM) analysis were used to analyse the relationship between meteorological factors and the number of patients with COVID-19. We found that the significant influencing factors of the number of COVID-19 cases differed among the four states. Specifically, the number of COVID-19 confirmed cases in California and New York was negatively correlated with AWMD (P < 0.01) and positively correlated with AQI, PM2.5 and TAVG (P < 0.01) but not significantly correlated with other factors. Florida was significantly correlated with TAVG (positive) (P < 0.01) but not significantly correlated with other factors. The number of COVID-19 cases in Texas was only significantly negatively associated with AWND (P < 0.01). The influence of temperature and PM2.5 on the spread of COVID-19 is not obvious. This study shows that when the wind speed was 2 m/s, it had a significant positive correlation with COVID-19 cases. The impact of meteorological factors on COVID-19 may be very complicated. It is necessary to further explore the relationship between meteorological factors and COVID-19. By exploring the influence of meteorological factors on COVID-19, we can help people to establish a more accurate early warning system.

Descriptions of the flying organs and generic characteristics of the genus Zheng, Wei & Jiang are currently imprecise. is reviewed and compared with allied genera. A re-description is undertaken and a determination key is provided to . Zha & Wen, from the Guizhou Karst Region, China, is described and illustrated with photographs. Observations on the ecology and habits of the new species are recorded. Four current species of Hancock are transferred to . Variations of the flying organs and tegminal sinus in the Tetrigidae are discussed, which will help to describe them accurately.

A new species of the genus Thoradonta (Orthoptera, Tetrigidae), Thoradonta varispina Zha & Sheng, sp. n., was found in Lengshuihe Nature Preserve, Jinsha, Guizhou, China. It is introduced with a description and photographs and compared with similar taxa. Ecology, habits, and morphological variation of the new species are discussed and illustrated. Generic characteristics of Thoradonta are updated and an updated key to all known species of Thoradonta is given.