The influence of social network on COVID-19 vaccine hesitancy among healthcare workers: a cross-sectional survey in Chongqing, China.

The factors that lead to coronavirus disease 2019 (COVID-19) vaccine hesitancy among health-care workers (HCWs) are unclear. We aimed to identify the factors that influence HCWs' hesitancy, especially the influence of their social network. Using an online platform, we surveyed HCWs in Chongqing, China, in January 2021 to understand the factors that influence the COVID-19 vaccine hesitancy among HCWs. Proportional allocation stratified sampling method was used to recruit respondents. Multivariable logistic regression and social network analysis (SNA) were used to analyze the influence factors. A total of 5247 HCWs were included and 23.3% of them were vaccine-hesitant. Participants were more hesitant if they had chronic diseases (OR = 1.411, 95% CI: 1.146-1.738), worked in tertiary hospitals (OR = 1.546, 95% CI: 1.231-1.942), and reported a history of vaccine hesitancy (OR = 1.637, 95% CI: 1.395-1.920) and refusal toward other vaccines (OR = 2.433, 95% CI: 2.067-2.863). The participants with a social network to communicate COVID-19 immunization were less hesitant (OR = 0.850, 95% CI: 0.728-0.993). Several influential members with social networks were found in SNA. Most of these influential members in the networks were department leaders who were willing to get COVID-19 vaccines (P < .05). Hesitant subgroups among Chinese HCWs were linked to the lack of a social network to communicate COVID-19 immunization. Our findings may lead to tailored interventions to enhance COVID-19 vaccine uptake among HCWs by targeting key members in social network.

Spatiotemporal analysis of COVID-19 outbreaks in Wuhan, China.

Few study has revealed spatial transmission characteristics of COVID-19 in Wuhan, China. We aimed to analyze the spatiotemporal spread of COVID-19 in Wuhan and its influence factors. Information of 32,682 COVID-19 cases reported through March 18 were extracted from the national infectious disease surveillance system. Geographic information system methods were applied to analysis transmission of COVID-19 and its influence factors in different periods. We found decrease in effective reproduction number (Rt) and COVID-19 related indicators through taking a series of effective public health measures including restricting traffic, centralized quarantine and strict stay-at home policy. The distribution of COVID-19 cases number in Wuhan showed obvious global aggregation and local aggregation. In addition, the analysis at streets-level suggested population density and the number of hospitals were associated with COVID-19 cases number. The epidemic situation showed obvious global and local spatial aggregations. High population density with larger number of hospitals may account for the aggregations. The epidemic in Wuhan was under control in a short time after strong quarantine measures and restrictions on movement of residents were implanted.

Epidemiological characteristics and the entire evolution of coronavirus disease 2019 in Wuhan, China.

BACKGROUND: Coronavirus Disease 2019 (COVID-19) spread rapidly around the world. We aimed to describe the epidemiological characteristics and the entire evolution of COVID-19 in Wuhan, and to evaluate the effect of non-pharmaceutical intervention by the government. METHODS: The information of COVID-19 cases until Mar 18, 2020 in Wuhan were collected from the national infectious disease surveillance system in Hubei province. RESULTS: A total of 49,973 confirmed cases were reported until Mar 18, 2020 in Wuhan. Among whom, 2496 cases died and the overall mortality was 5.0%. Most confirmed cases (25,619, 51.3%) occurred during Jan 23 to Feb 4, with a spike on Feb 1 (new cases, 3374). The number of daily new cases started to decrease steadily on Feb 19 (new cases, 301) and decreased greatly on Mar 1 (new cases, 57). However, the mortality and the proportion of severe and critical cases has been decreasing over time, with the lowest of 2.0 and 10.1% during Feb 16 to Mar 18, 2020, respectively. The percentage of severe and critical cases among all cases was 19.6%, and the percentage of critical and dead cases aged over 60 was 70.1 and 82.0%, respectively. CONCLUSION: The number of new cases has dropped significantly after the government taking the isolation of four types of personnel and the community containment for 14 days. Our results indicate that the mortality and proportion of severe and critical cases gradually decreased over time, and critical and dead cases are more incline to be older individuals.