Epidemiological characteristics and dynamic transmissions of COVID-19 pandemics in Chinese mainland: A trajectory clustering perspective analysis.

BACKGROUND: The corona virus disease 2019 (COVID-19) pandemic has spread to more than 210 countries and regions around the world, with different characteristics recorded depending on the location. A systematic summarization of COVID-19 outbreaks that occurred during the "dynamic zero-COVID" policy period in Chinese mainland had not been previously conducted. In-depth mining of the big data from the past two years of the COVID-19 pandemics must be performed to clarify their epidemiological characteristics and dynamic transmissions. METHODS: Trajectory clustering was used to group epidemic and time-varying reproduction number (Rt) curves of mass outbreaks into different models and reveal the epidemiological characteristics and dynamic transmissions of COVID-19. For the selected single-peak epidemic curves, we constructed a peak-point judgment model based on the dynamic slope and adopted a single-peak fitting model to identify the key time points and peak parameters. Finally, we developed an extreme gradient boosting-based prediction model for peak infection cases based on the total number of infections on the first 3, 5, and 7 days of the initial average incubation period. RESULTS: (1) A total of 7 52298 cases, including 587 outbreaks in 251 cities in Chinese mainland between June 11, 2020, and June 29, 2022, were collected, and the first wave of COVID-19 outbreaks was excluded. Excluding the Shanghai outbreak in 2022, the 586 remaining outbreaks resulted in 1 25425 infections, with an infection rate of 4.21 per 1 00000 individuals. The number of outbreaks varied based on location, season, and temperature. (2) Trajectory clustering analysis showed that 77 epidemic curves were divided into four patterns, which were dominated by two single-peak clustering patterns (63.3%). A total of 77 Rt curves were grouped into seven patterns, with the leading patterns including four downward dynamic transmission patterns (74.03%). These curves revealed that the interval from peak to the point where the Rt value dropped below 1 was approximately 5 days. (3) The peak-point judgment model achieved a better result in the area under the curve (0.96, 95% confidence interval = 0.90-1.00). The single-peak fitting results on the epidemic curves indicated that the interval from the slow-growth point to the sharp-decline point was approximately 4-6 days in more than 50% of mass outbreaks. (4) The peak-infection-case prediction model exhibited the superior clustering results of epidemic and Rt curves compared with the findings without grouping. CONCLUSION: Overall, our findings suggest the variation in the infection rates during the "dynamic zero-COVID" policy period based on the geographic division, level of economic development, seasonal division, and temperature. Trajectory clustering can be a useful tool for discovering epidemiological characteristics and dynamic transmissions, judging peak points, and predicting peak infection cases using different patterns.

Bioelectrical impedance analysis versus quantitative computer tomography and anthropometry for the assessment of body composition parameters in China.

Obesity, especially abdominal obesity, is correlated to increased risk of cardiovascular morbidity and mortality. It is urgent to search a simply method to predict visceral fat area (VFA). Herein, we evaluated the correlation of waist circumference (WC) measured by anthropometry and bioelectrical impedance analysis (BIA), and VFA estimated by BIA or measured by quantitative computed tomography (QCT) in China. The mean body mass index (BMI) was 25.09 ± 3.31 kg/m2 and the mean age was 49.16 ± 9.19 years in 2754 subjects. VFA-BIA were significantly smaller than VFA-QCT in both BMI and age subgroups between male and female (p < 0.001). High correlation was observed for WC between BIA and manually (r = 0.874 for all, r = 0.865 for male and r = 0.806 for female) and for VFA between BIA and QCT (r = 0.512 for all). The intraclass correlation coefficient (ICC) showed the perfect agreement between BIA and manually to measure WC (ICC = 0.832 for all, 0.845 for male and 0.697 for female) and implied a good reliability for VFA between BIA and QCT with women among subgroups (ICC = 0.623 for all, ICC = 0.634 for age < 50 years and ICC = 0.432 for BMI > 24 kg/m2), whereas the good reliability was lost in men (ICC = 0.174). The kappa analysis showed a moderate consistency for VFA measured by BIA and QCT (Kappa = 0.522 with age < 50 years, 0.565 with age ≥ 50 years in male; Kappa = 0.472 with age < 50 years, 0.486 with age ≥ 50 years in female). In addition, BIA to estimate VFA (r = 0.758 in male, r = 0.727 in female, P < 0.001) has a stronger correlation with VFA measured by QCT than BMI and WC according to gender categories. Furthermore, ROC analysis showed the cut-off point of VFA measured by BIA for predicting visceral obesity was: 101.90 cm2, 119.96 cm2 and 118.83 cm2 and the Youden's index was 0.577, 0.577 and 0.651, respectively and the Kappa value was 0.532, 0.536 and 0.611 in unadjusted model, model 1 and model 2. In conclusion, being non-invasive and free of radiation, BIA can be used as a safe and convenient tool to estimate VFA in female; especially for monitoring the VFA of the same person, the BIA has superiority to a certain extent. However, the consistency is not most ideal between BIA and QCT. When using BIA to assess whether a person is visceral obesity, we must take into consideration age, BMI and WC. Therefore, we established a regression formula to reflect VFA-QCT by VFA-BIA, age, BMI, and WC. In addition, a more accurate formula is needed to match the CT data in China.