Coronavirus disease-2019 (COVID-19) pandemic has affected millions of people since December 2019. Summarizing the development of COVID-19 and assessing the effects of control measures are very critical to China and other countries. A logistic growth curve model was employed to compare the development of COVID-19 before and after the emergency response took effect. We found that the number of confirmed cases peaked 9–14 days after the first detection of an imported case, but there was a peak lag in the province where the outbreak was concentrated. Results of the growth curves indicated that the fitted cumulative confirmed cases were close to the actual observed cases, and the R2 of all models was above 0.95. The average growth rate decreased by 44.42% nationally and by 32.5% outside Hubei Province. The average growth rate in the 12 high-risk areas decreased by 29.9%. The average growth rate of cumulative confirmed cases decreased by approximately 50% after the emergency response. Areas with frequent population migration have a high risk of outbreak. The emergency response taken by the Chinese government was able to effectively control the COVID-19 outbreak. Our study provides references for other countries and regions to control the COVID-19 outbreak.

The WHO has described coronavirus disease 2019 (COVID-19) as a pandemic due to the speed and scale of its transmission. Without effective interventions, the rapidly increasing number of COVID-19 cases would greatly increase the burden of clinical treatments. Identifying the transmission sources and pathways is of vital importance to block transmission and allocate limited public health resources. According to the relationships among cases, we constructed disease transmission network graphs for the COVID-19 epidemic through a visualization technique based on individual reports of epidemiological data. We proposed an analysis strategy of the transmission network with the epidemiological data in Tianjin and Chengdu. The transmission networks showed different transmission characteristics. In Tianjin, an imported case of COVID-19 can produce an average of 2.9 secondary infections and ultimately produce as many as 4 generations of infections, with a maximum of 6 cases being generated before the imported case is identified. In Chengdu, 45 noninformative cases and 24 cases with vague exposure information made accurate information about the transmission network difficult to provide. The proposed analysis framework of visualized transmission networks can trace the transmission source and contacts, assess the current situation of transmission and prevention, and provide evidence for the global response and control of the COVID-19 pandemic.

Hand, foot and mouth disease (HFMD) is a growing threat to children's health, causing a serious public health burden in China. The relationships between associated meteorological factors and HFMD have been widely studied. However, the HFMD burden due to relative humidity from the perspective of attributable risk has been neglected. This study investigated the humidity-HFMD relationship in three comprehensive perspectives, humidity-HFMD relationship curves, effect modification and attributable risks in the Sichuan Basin between 2011 and 2017. We used multistage analyses composed of distributed lag nonlinear models (DLNMs), a multivariate meta-regression model and the calculations of attributable risk to quantify the humidity-HFMD association. We observed a J-shaped pattern for the pooled cumulative humidity-HFMD relationship, which presented significant heterogeneity relating to the geographical region and number of primary school students. Overall, 27.77% (95% CI 25.24–30.02%) of HFMD infections were attributed to humidity. High relative humidity resulted in the greatest burden of HFMD infections. The proportion of high humidity-related HFMD in the southern basin was higher than that in the northern basin. The findings provide evidence from multiple perspectives for public health policy formulation and health resource allocation to develop priorities and targeted policies to ease the HFMD burden associated with humidity.

Background Epidemiological studies have investigated the short-term effects of meteorological factors and air pollution on the incidence of hand, foot, and mouth disease (HFMD). Several meteorological indicators, such as relative humidity and the diurnal temperature range (DTR), significantly modify the relationship between short-term exposure to temperature and HFMD incidence. However, it remains unclear whether (and how) long-term air pollution levels modify the short-term relationships of HFMD incidence with meteorological factors and air pollution. Methods We obtained daily data on meteorological factors, air pollutants, and HFMD counts in children from 21 prefecture-level cities in Sichuan Province in Southwest China from 2015 to 2017. First, we constructed a distributed lag nonlinear model (DLNM) at each prefecture-level site to evaluate the short-term impacts of meteorological variables and air pollutants on HFMD incidence. Then, we assessed the pooled effects of the exposures and incorporated long-term city-specific air pollutant indicators as meta-predictors to examine their potential modification effects by performing multivariate meta-regression models. Results We found that long-term SO2 and CO concentrations significantly modified the short-term relationships between climatic variables and HFMD incidence. Specifically, high concentrations of CO (P = 0.027) and SO2 (P = 0.039) reduced the risk of HFMD at low temperatures. The relationship between relative humidity and HFMD incidence was weakened at high SO2 concentrations (P = 0.024), especially when the relative humidity was below the median level. When the minimum relative humidity (32%) was compared to the median relative humidity (77%), the risk ratio (RR) was 0.77 (95% CI: 0.51–1.17) in the 90th percentile of SO2 (19.6 μg/m3) and 0.41 (95% CI: 0.27–0.64) in the 10th percentile of SO2 (10.6 μg/m3). Conclusion Our results indicated that long-term SO2 and CO levels modified the short-term associations between HFMD incidence in children and meteorological variables. These findings may inform health authorities to optimize targeted public health policies including reducing ambient air pollution and reinforcing self-protective actions to weaken the adverse health impacts of environmental factors on HFMD incidence.

Background Hand, foot, and mouth disease (HFMD) is a serious threat among children in China. Some studies have found that air pollution is associated with HFMD incidence, but the results showed heterogeneity. In this study, we aimed to explore the heterogeneity of associations between air pollutants and the number of HFMD cases and to identify significant socioeconomic effect modifiers. Methods We collected daily surveillance data on HFMD cases in those aged less than 15 years, air pollution variables and meteorological variables from 2015 to 2017 in the basin area of Sichuan Province. We also collected socioeconomic indicator data. We conducted a two-stage multicity time-series analysis. In the first stage, we constructed a distributed lag nonlinear model (DLNM) to obtain cumulative exposure-response curves between each air pollutant and the numbers of HFMD cases for every city. In the second stage, we carried out a multivariable meta-regression to merge the estimations in the first stage and to identify significant socioeconomic effect modifiers. Results We found that PM10, NO2 and O3 concentrations were associated with the number of HFMD cases. An inverted V-shaped association between PM10 and the number of HFMD cases was observed. The overall NO2-HFMD association was a hockey-stick shape. For the relationships of PM10, SO2, NO2, O3 and CO with HFMD counts, approximately 58.5%, 48.4%, 51.0%, 55.6% and 52.5% of the heterogeneity could be explained, respectively. The proportion of primary school students, population density, urbanization rate, number of licensed physicians and number of hospital beds explained part of the heterogeneity and modified the relationships. Conclusion Our study explored the heterogeneity of associations between air pollutants and HFMD counts. The proportion of primary school students, population density, urbanization rate, number of licensed physicians and number of hospital beds could modify the relationships. The results can serve as a reference for relevant public health decision making.