Objective:To investigate the current situation of cytomegalovirus (CMV) infection in infants in Lishui, and summarize the related factors of CMV infection, evaluate its influence on the growth and development of infants, and provide evidence for the prevention and control of CMV infection.Methods:In this study, 2 254 cases of infants admitted in pediatric ward in Lishui Maternal and Child Health Hospital, Qingtian County People′s Hospital, Suichang County People′s Hospital, Qingyuan County People′s Hospital from January 1, 2015 to December 31, 2017 with integral clinical data were selected. All the babies were followed up from the time when they were born to 1 year old. The serum CMV antibody and the urine CMV-DNA were screened, the general situation and clinical features of CMV infection were summarized, and the relevant factors of infants CMV infection were analyzed and screened by the single factor and multiple factors analysis. They were followed up to 1 year old to clarify the influence of CMV infection on the growth and development of infants.Results:From 2015 to 2017, the total positive infection rate of CMV-IgM in infants under 1 year old in Lishui was 10.43%(235/2 254), and CMV-IgM positive infection decreased year by year. The positive rate of CMV-IgG did not change significantly with time. The positive rate of CMV-IgM was the highest at 1—3 months, and up to 15.29% (61/399). The positive rate of CMV-IgM decreased with the age of the babies. The positive rate of CMV-IgG increased with the age of the babies. The positive rate of CMV-IgM in infants showed no significant difference in gender ( P>0.05). The positive rate of CMV-IgM was higher in men than that in women [65.43% (810/1 238) vs. 55.51% (564/1 016)], and there was significant difference ( P<0.05). The gestational age of the infected group was lower than that of the non-infected group [(37.41 ± 1.63) weeks vs. (38.97 ± 0.97) weeks], and the breast-feeding rate of the infected group was higher than that of the non-infected group [57.87%(136/235) vs. 40.00%(40/100)], and there were significant differences ( P<0.05). Thrombocytopenia, the increase of transaminase, necrotizing enterocolitis of newborn, and hepatosplenomegaly of infected group is higher that of the non-infected group [18.72%(44/235) vs. 1.00% (1/100), 29.36% (69/235) vs. 13.00% (13/100), 26.81% (63/235) vs. 10.00% (10/100), 9.79% (23/235) vs. 0], and there were significant differences ( P<0.05). Gestational age and breast-feeding were possible risk factors for CMV infection in infants under 1 year old ( P<0.05). There was no significant difference in height, weight, head circumference and intelligence score between the infected group and the non-infected group at the age of 1 year ( P>0.05). The total abnormal rate of hearing development and the abnormal detection rate of B-ultrasound in the infected group were higher than those in the non-infected group [13.62%(64/470) vs. 1.00%(2/200), 6.38%(15/235) vs. 0], and there were significant differences ( P<0.05). Conclusions:The CMV active infection rate of infants under 1 year old in Lishui is relatively high and decreases year by year. It decreases with the prolongation of birth time, and there is no gender difference. Gestational age and breast-feeding are the risk factors for active CMV infection in infants. CMV infection affects the hearing development and the brain development of infants under 1 year old, which is the main cause of hepatitis. It is necessary to pay attention to the prevention of CMV infection, strengthen maternal perinatal health care, and strengthen the screening of CMV infection in high-risk groups.

Objective: To evaluate the current status of the prevention and control of coronavirus disease (COVID-19) outbreak in China, establish a predictive model to evaluate the effects of the current prevention and control strategies, and provide scientific information for decision- making departments. Methods: Based on the epidemic data of COVID-19 openly accessed from national health authorities, we estimated the dynamic basic reproduction number R₀(t) to evaluate the effects of the current COVID-19 prevention and control strategies in all the provinces (municipalities and autonomous regions) as well as in Wuhan and the changes in infectivity of COVID-19 over time. Results: For the stability of the results, 24 provinces (municipality) with more than 100 confirmed COVID-19 cases were included in the analysis. At the beginning of the outbreak, the R₀(t) showed unstable trend with big variances. As the strengthening of the prevention and control strategies, R₀(t) began to show a downward trend in late January, and became stable in February. By the time of data analysis, 18 provinces (municipality) (75%) had the R₀(t)s less than 1. The results could be used for the decision making to free population floating conditionally. Conclusions Dynamic R₀(t) is useful in the evaluation of the change in infectivity of COVID-19, the prevention and control strategies for the COVID-19 outbreak have shown preliminary effects, if continues, it is expected to control the COVID-19 outbreak in China in near future.

Objectives: Fitting and forecasting the trend of COVID-19 epidemics. Methods: Based on SEIR dynamic model, considering the COVID-19 transmission mechanism, infection spectrum and prevention and control procedures, we developed SEIR^{+ CAQ} dynamic model to fit the frequencies of laboratory confirmed cases obtained from the government official websites. The data from January 20, 2020 to February 7, 2020 were used to fit the model, while the left data between February 8-12 were used to evaluate the quality of forecasting. Results: According to the cumulative number of confirmed cases between January 29 to February 7, the fitting bias of SEIR^{+ CAQ} model for overall China (except for cases of Hubei province), Hubei province (except for cases of Wuhan city) and Wuhan city was less than 5%. For the data of subsequent 5 days between February 8 to 12, which were not included in the model fitting, the prediction biases were less than 10%. Regardless of the cases diagnosed by clinical examines, the numbers of daily emerging cases of China (Hubei province not included), Hubei Province (Wuhan city not included) and Wuhan city reached the peak in the early February. Under the current strength of prevention and control, the total number of laboratory- confirmed cases in overall China will reach 80 417 till February 29, 2020, respectively. Conclusions The proposed SEIR^{+ CAQ} dynamic model fits and forecasts the trend of novel coronavirus pneumonia well and provides evidence for decision making.