[Association between physical exercise and non-alcoholic fatty liver disease in people infected with hepatitis B virus].

Objective: To investigate the association between physical exercise and non-alcoholic fatty liver disease (NAFLD) in people infected with HBV. Methods: The information about the 3 813 participants infected with HBV, including the prevalence of NAFLD, prevalence of physical exercise and other covariates, were collected from the National Science and Technology Major Project of China during 2016-2020. The logistic regression model was used to evaluate the association between physical exercise and NAFLD in HBV infected patients, and subgroup analysis was performed to identify the effect modifiers. Results: A total of 2 259 HBV infected participants were included in the final analysis and 454 (20.10%) had NAFLD. After adjusting for covariates, we found that moderate physical exercise was a protective factor for NAFLD (OR=0.66, 95%CI: 0.46-0.94). Subgroup analysis suggested that the protective effect of moderate physical exercise on NAFLD might be stronger in women (OR=0.61, 95%CI: 0.36-1.01), those <45 years old (OR=0.24, 95%CI: 0.06-0.80), those who had low education level (OR=0.16, 95%CI: 0.04-0.49), those who had low annual income (OR=0.39, 95%CI: 0.16-0.89 for <30 000 yuan RMB; OR=0.64, 95%CI: 0.40-1.00 for 30 000-80 000 yuan RMB), those who had hypertension (OR=0.45, 95%CI: 0.21-0.88), those with BMI ≥24.0 kg/m2 (OR=0.66, 95%CI: 0.43-1.01), those who had more daily fruit or vegetable intake (OR=0.61, 95%CI: 0.38-0.97), those who had more daily meat intake (OR=0.49, 95%CI: 0.23-0.97), and those who had no smoking history (OR=0.66, 95%CI: 0.45-0.95) or passive smoking exposure (OR=0.61, 95%CI: 0.37-0.97). Conclusions: Among HBV infected patients, moderate physical exercise was negatively associated with the prevalence of NAFLD. Women, young people, those who had low education level, those who had low annual income, those with hypertension, those with high BMI, those who had more daily fruit or vegetable and meat intakes, and those who had no smoking history or passive smoking exposure might be more sensitive to the protective effect.

[Progress of researches on medical big data analytics technology].

The use of the big data analytics technology to collect, summarize and analyze medical big data is effective to precisely mine and explore the underlying information, which greatly facilitates medical science research and clinical practices. Currently, the medical big data analytics technology mainly includes artificial intelligence, databases and programming languages, which have been widely employed in medical imaging, disease risk prediction, disease control, healthcare management, follow-up, and drug and therapy development. This review summarizes the currently available medical big data analytics technologies and their applications, with aims to facilitate the related studies.

[Epidemiological characteristics of local outbreak of COVID-19 caused by SARS-CoV-2 Delta variant in Liwan district, Guangzhou].

Objective: To analyze the epidemiological characteristics of a local outbreak of COVID-19 caused by SARS-CoV-2 B.1.617.2(Delta) variant in Liwan district, Guangzhou, and provide evidence for the further prevention and control of the Delta variant of COVID-19. Methods: From May 21 to June 18, 2021, the incidence data of COVID-19 caused by Delta variant were obtained from National Notifiable Disease Report System of Chinese Disease Prevention and Control Information System and Liwan District Center for Disease Control and Prevention of Guangzhou.Frequency analysis (proportions), histograms, and percentage stacked area plots were used to describe the epidemiological characteristics of the outbreaks. The incubation period and time-varying reproduction numbers (Rt) estimations were used for the further analysis. Results: By June 18, 2021, a total of 127 COVID-19 cases caused by Delta variant was reported in Liwan district. The youngest case was aged 2 years and the oldest was aged 85 years. There were 18.9% (24/127) aged <18 years, 43.3% (55/127) aged 18-59 years, and 37.8% (48/127) aged ≥60 years, the male to female ratio of the cases was 1∶1.35 (54∶73). The cases were mainly retired people (32.3%, 41/127), the jobless or unemployed (18.1%, 23/127), and students (16.5%, 21/127). The infections mainly occurred in Baihedong (70.1%, 89/127) and Zhongnan street (23.6%, 30/127) communities in the southern area of Liwan district. The median incubation period of the Delta variant infection was 6 days (range: 1-15 days). The clinical classification were mainly common type (64.6%, 82/127). The basic reproduction number (R0) was 5.1, Rt which once increased to 7.3. The transmissions mainly occurred in confined spaces, such as home (26.8%), restaurant (29.1%), neighborhood (3.9%), and market (3.1%), the household clustering was predominant. Close contacts tracing (66.1%) and community screening (33.1%) were the main ways to find the infections. Conclusion: The COVID-19 outbreak caused by Delta variant in Liwan district of Guangzhou was highly contagious, with the obvious characteristics of household clustering and high proportions of cases in adults aged 18-59 years and elderly people aged ≥60 years.

[Estimating the distribution of COVID-19 incubation period by interval-censored data estimation method].

Objectives: The COVID-19 has been the public health issues of global concern, but the incubation period was still under discussion. This study aimed to estimate the incubation period distribution of COVID-19. Methods: The exposure and onset information of COVID-19 cases were collected from the official information platform of provincial or municipal health commissions. The distribution of COVID-19 incubation period was estimated based on the Log- normal, Gamma and Weibull distribution by interval-censored data estimation method. Results: A total of 109 confirmed cases were collected, with an average age of 39.825 years. The median COVID-19 incubation period based on Log-normal, Gamma, and Weibull distribution were 4.958 (P(25)-P(75): 3.472-7.318) days, 5.083 (P(25)-P(75): 3.511-7.314) days, and 5.695 (P(25)-P(75): 3.675-7.674) days, respectively. Gamma distribution had the largest log-likelihood result. Conclusions: The distribution of COVID-19 incubation period followed the Gamma distribution, and the interval-censored data estimation method can be used to estimate the incubation period distribution.

[Using Markov Chain Monte Carlo methods to estimate the age-specific case fatality rate of COVID-19].

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 (P(25)-P(75): 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.

[Fitting and forecasting the trend of COVID-19 by SEIR(+CAQ) dynamic model].

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.

[Estimating the basic reproduction number of COVID-19 in Wuhan, China].

Objective: The number of confirmed and suspected cases of the COVID-19 in Hubei province is still increasing. However, the estimations of the basic reproduction number of COVID-19 varied greatly across studies. The objectives of this study are 1) to estimate the basic reproduction number (R(0)) of COVID-19 reflecting the infectiousness of the virus and 2) to assess the effectiveness of a range of controlling intervention. Methods: The reported number of daily confirmed cases from January 17 to February 8, 2020 in Hubei province were collected and used for model fit. Four methods, the exponential growth (EG), maximum likelihood estimation (ML), sequential Bayesian method (SB) and time dependent reproduction numbers (TD), were applied to estimate the R(0). Results: Among the four methods, the EG method fitted the data best. The estimated R(0) was 3.49 (95%CI: 3.42-3.58) by using EG method. The R(0) was estimated to be 2.95 (95%CI: 2.86-3.03) after taking control measures. Conclusions: In the early stage of the epidemic, it is appropriate to estimate R(0) using the EG method. Meanwhile, timely and effective control measures were warranted to further reduce the spread of COVID-19.