[Discussion on early warning, prevention and control of emerging infectious diseases from a macroscopic perspective based on big data and effective distance model: enlightenment of COVID-19 epidemic data in China].

Objective: To provide a system for warning, preventing and controlling emerging infectious diseases from a macroscopic perspective, using the COVID-19 epidemic data and effective distance model. Methods: The dates of hospitalization/isolation treatment of the first confirmed cases of COVID-19 and the cumulative numbers of confirmed cases in different provinces in China reported as of 23 February, 2020 were collected. The Location Based Service (LBS) big data platform of "Baidu Migration" was employed to obtain the data of the proportion of the floating population from Wuhan to all parts of the country. Effective distance models and linear regression models were established to analyze the relationship between the effective distance and the arrival time of the epidemic as well as the number of cumulative confirmed cases at provincial and municipal levels. Results: The arrival time of the epidemic and the cumulative number of confirmed cases of COVID-19 had significant linear relationship at both provincial and municipal levels in China, and the regression coefficients of each linear model were significant (P<0.001). At the provincial level, the effective distance could explain about 71% of the variation of the model with arrival time along with around 90% of the variation for the model in the cumulative confirmed case magnitude; at the municipal level, the effective distance could explain about 66% of the variation for the model in arrival time, and about 85% of the variation of the model with the cumulative confirmed case magnitude. Conclusions: The fitting degree of the models are good. The LBS big data and effective distance model can be used to estimate the track, time and extent of epidemic spread to provide useful reference for early warning, prevention and control of emerging infectious diseases.

Inter- and Intra-individual Variability in the Pharmacokinetics of Agomelatine Tablets in Chinese Healthy Male Subjects.

Agomelatine is an antidepressant with a unique action mechanism differing from conventional antidepressants. The high inter- and intra-individual variability of agomelatine was previously reported, but no exact data values about the inter- and intra-individual variability in AUC and Cmax were mentioned. The current study aimed to determine and evaluate the inter- and intra-individual variability in AUC and Cmax of agomelatine tablets in Chinese healthy male subjects, providing useful information for designing bioequivalence studies of agomelatine. Each of 12 Chinese healthy male subjects received a 25-mg agomelatine tablet on 2 separate periods, and plasma samples were collected up to 24 h after dose and analyzed for agomelatine. Inter- and intra-individual variability in the pharmacokinetic parameters (Cmax, AUC0-t and AUC0-∞) of agomelatine was assessed. High variations in the plasma concentrations of agomelatine could be observed at each sampling time between the different subjects and in one subject on different periods. The inter-individual CVs of Cmax, AUC0-t and AUC0-∞ were 102.20%, 131.74% and 130.59%, respectively. The intra-individual CVs of Cmax, AUC0-t and AUC0-∞ were 84.34%, 49.61% and 50.83%, respectively. The results showed high inter- and intra-individual variability in the pharmacokinetics of agomelatine in Chinese healthy male subjects, and the intra-individual variability at CV>80% should be considered in the design of bioequivalence studies.