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 assess the risk of COVID-19 foreign imports cases to China.Methods:We collected epidemic data (cumulative daily confirmed cases in each country, cumulative confirmed imported cases), demographic data (population density, population) and information on potential source groups of tourists (the daily estimated number of overseas Chinese, overseas Chinese students, overseas workers, foreign students coming to China and flight passengers) and the global health security index (GHS) to assess and predict risk of imported cases for recent (February 1 st to April 25 th) and future (after April 26 th). Results:Strong positive correlation was found among variables including the number of imported cases, cumulative confirmed cases, attack rate, number of overseas Chinese, number of overseas Chinese students, number of foreign students coming to China, number of flight passengers and GHS. In the recent risk analysis, imported cases of Russian were the highest, followed by United Kingdom, United States, France and Spain. In the future risk prediction, 44 countries including United States and Singapore are evaluated as potential high-risk countries in the future through the attack rate index of each country and the estimated average number of daily passengers.Conclusion:The risk assessment of COVID-19 imported cases can be used to identify high-risk areas in recent and future, and might be helpful to strengthen the prevention and control of the epidemic and ultimately overcome the epidemic.

Objective:To infer the start time of the resurgent COVID-19 epidemic in Xinfadi wholesale market in Beijing in June 2020 and evaluate the effect of comprehensive prevention and control measures in this epidemic.Methods:SEIR dynamics model was used to fit daily onset infections to search the start date of this resurgent COVID-19 epidemic in Beijing. The number of cumulative infections from June 12 to July 1 in Beijing were fitted considering different levels of control strength.Results:The current reemerged COVID-19 epidemic in Beijing probably started between May 22 and May 28 (cumulative probability: 95 %), with the highest probability on May 25 (23 %). The R0 of the current reemerged COVID-19 epidemic was 4.22 (95 %CI: 2.88-7.02). Dynamic model fitting suggested that by June 11, the cumulative number of COVID-19 cases would reached 99 (95 %CI: 77-121), which was in line with the actual situation, and without control, by July 1, the cumulative number of COVID-19 cases would reach 65 090 (95 %CI: 39 068-105 037). Since June 12, comprehensive prevention and control measures have been implemented in Beijing, as of July 1, compared with uncontrolled situation, the number of infections had been reduced by 99 %, similar to the fitting result of a 95 % reduction of the transmission rate. The sensitivity analysis showed consistent results. Conclusions:For the emergent outbreak of COVID-19, the dynamics model can be used to infer the start time of the transmission and help tracing the source of epidemic. The comprehensive prevention and control measures taken in Beijing have quickly blocked over 95 % of the transmission routes and reduced 99 % of the infections, containing the sudden epidemic timely and effectively, which have value in guiding the prevention and control of the epidemic in the future.

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:The COVID-19 epidemic has swept all over the world. Estimates of its case fatality rate were influenced by the existing confirmed cases and the time distribution of onset to death, and the conclusions were still unclear. This study was aimed to estimate the age-specific case fatality rate of COVID-19.Methods:Data on COVID-19 epidemic were collected from the National Health Commission and China CDC. The Gamma distribution was used to fit the time from onset to death. The Markov Chain Monte Carlo simulation was used to estimate age-specific case fatality rate.Results:The median time from onset to death of COVID-19 was M=13.77 ( P25- P75: 9.03-21.02) d. The overall case fatality rate of COVID-19 was 4.1 % (95 %CI: 3.7 %-4.4 %) and the age-specific case fatality rate were 0.1 %, 0.4 %, 0.4 %, 0.4 %,0.8 %, 2.3 %, 6.4 %, 14.0 and 25.8 % for 0-, 10-, 20-, 30-, 40-, 50-, 60-, 70- and ≥80 years group, respectively. Conclusions:The Markov Chain Monte Carlo simulation method adjusting censored is suitable for case fatality rate estimation during the epidemic of a new infectious disease. Early identification of the COVID-19 case fatality rate is helpful to the prevention and control of the epidemic.

Background::Non-smokers account for a large proportion of lung cancer patients, especially in Asia, but the attention paid to them is limited compared with smokers. In non-smokers, males display a risk for lung cancer incidence distinct from the females—even after excluding the influence of smoking; but the knowledge regarding the factors causing the difference is sparse. Based on a large multicenter prospective cancer screening cohort in China, we aimed to elucidate the interpretable sex differences caused by known factors and provide clues for primary and secondary prevention.Methods::Risk factors including demographic characteristics, lifestyle factors, family history of cancer, and baseline comorbidity were obtained from 796,283 Chinese non-smoking participants by the baseline risk assessment completed in 2013 to 2018. Cox regression analysis was performed to assess the sex difference in the risk of lung cancer, and the hazard ratios (HRs) that were adjusted for different known factors were calculated and compared to determine the proportion of excess risk and to explain the existing risk factors.Results::With a median follow-up of 4.80 years, 3351 subjects who were diagnosed with lung cancer were selected in the analysis. The lung cancer risk of males was significantly higher than that of females; the HRs in all male non-smokers were 1.29 (95% confidence interval [CI]: 1.20-1.38) after adjusting for the age and 1.38 (95% CI: 1.28-1.50) after adjusting for all factors, which suggested that known factors could not explain the sex difference in the risk of lung cancer in non-smokers. Known factors were 7% (|1.29-1.38|/1.29) more harmful in women than in men. For adenocarcinoma, women showed excess risk higher than men, contrary to squamous cell carcinoma; after adjusting for all factors, 47% ([1.30-1.16]/[1.30-1]) and 4% ([7.02-6.75]/[7.02-1])) of the excess risk was explainable in adenocarcinoma and squamous cell carcinoma. The main causes of gender differences in lung cancer risk were lifestyle factors, baseline comorbidity, and family history.Conclusions::Significant gender differences in the risk of lung cancer were discovered in China non-smokers. Existing risk factors did not explain the excess lung cancer risk of all non-smoking men, and the internal causes for the excess risk still need to be explored; most known risk factors were more harmful to non-smoking women; further exploring the causes of the sex difference would help to improve the prevention and screening programs and protect the non-smoking males from lung cancers.

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.