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BackgroundThe COVID-19 pandemic has reached 40 million confirmed cases worldwide. Given its rapid progression, it is important to examine its origins to better understand how people’s knowledge, attitudes, and reactions have evolved over time. One method is to use data mining of social media conversations related to information exposure and self-reported user experiences. ObjectiveThis study aims to characterize the knowledge, attitudes, and behaviors of social media users located at the initial epicenter of the outbreak by analyzing data from the Sina Weibo platform in Chinese. MethodsWe used web scraping to collect public Weibo posts from December 31, 2019, to January 20, 2020, from users located in Wuhan City that contained COVID-19–related keywords. We then manually annotated all posts using an inductive content coding approach to identify specific information sources and key themes including news and knowledge about the outbreak, public sentiment, and public reaction to control and response measures. ResultsWe identified 10,159 COVID-19 posts from 8703 unique Weibo users. Among our three parent classification areas, 67.22% (n=6829) included news and knowledge posts, 69.72% (n=7083) included public sentiment, and 47.87% (n=4863) included public reaction and self-reported behavior. Many of these themes were expressed concurrently in the same Weibo post. Subtopics for news and knowledge posts followed four distinct timelines and evidenced an escalation of the outbreak’s seriousness as more information became available. Public sentiment primarily focused on expressions of anxiety, though some expressions of anger and even positive sentiment were also detected. Public reaction included both protective and elevated health risk behavior. ConclusionsBetween the announcement of pneumonia and respiratory illness of unknown origin in late December 2019 and the discovery of human-to-human transmission on January 20, 2020, we observed a high volume of public anxiety and confusion about COVID-19, including different reactions to the news by users, negative sentiment after being exposed to information, and public reaction that translated to self-reported behavior. These findings provide early insight into changing knowledge, attitudes, and behaviors about COVID-19, and have the potential to inform future outbreak communication, response, and policy making in China and beyond.

BackgroundThe coronavirus disease (COVID-19) pandemic, which began in Wuhan, China in December 2019, is rapidly spreading worldwide with over 1.9 million cases as of mid-April 2020. Infoveillance approaches using social media can help characterize disease distribution and public knowledge, attitudes, and behaviors critical to the early stages of an outbreak. ObjectiveThe aim of this study is to conduct a quantitative and qualitative assessment of Chinese social media posts originating in Wuhan City on the Chinese microblogging platform Weibo during the early stages of the COVID-19 outbreak. MethodsChinese-language messages from Wuhan were collected for 39 days between December 23, 2019, and January 30, 2020, on Weibo. For quantitative analysis, the total daily cases of COVID-19 in Wuhan were obtained from the Chinese National Health Commission, and a linear regression model was used to determine if Weibo COVID-19 posts were predictive of the number of cases reported. Qualitative content analysis and an inductive manual coding approach were used to identify parent classifications of news and user-generated COVID-19 topics. ResultsA total of 115,299 Weibo posts were collected during the study time frame consisting of an average of 2956 posts per day (minimum 0, maximum 13,587). Quantitative analysis found a positive correlation between the number of Weibo posts and the number of reported cases from Wuhan, with approximately 10 more COVID-19 cases per 40 social media posts (P

Harmful algal blooms (HABs) resulting from eutrophication pose a major threat to ecosystems and human health, necessitating effective control measures. Allelochemicals have shown their importance in slowing down algal proliferation due to their proven efficacy and low ecological impacts. In this study, allelopathy tea polyphenols (TPs) and β-cyclodextrin were combined to prepare slow-release algicidal microcapsules, and the diversity of microbial community in the algal inhibition process was analyzed. Results showed that TP slow-release microcapsules had strong algicidal activity. When against Microcystis aeruginosa within 20 d, their constant inhibitory rate was up to 99% compared to the control group. Microbial diversity decreased with an increase in algae density, and the species richness and diversity of algae increased under the stress of TP slow-release microcapsules. The redundancy analysis showed that the environmental factors with impacts on the abundance and diversity of bacterial communities in descending order were dissolved oxygen, pH, and temperature. This study provides a theoretical basis for the application of TP slow-release microcapsules to actual water.

In this paper, the principal component regression (PCR) estimators for regression parameters were studied in a linear regression model. After discussing the advantages and disadvantages of the classical PCR, we put forward three versions of hybrid PCR estimators. For the first two versions, we obtained the corresponding optimal solutions under the prediction error sum of squares (PRESS) criterion, while for the last one we offered two methods for obtaining the solution. In order to examine their practicality and generalizability, we considered two real-world examples and conducted a simulation study, which took into account varying degrees of multicollinearity. The numerical experiment revealed that the new estimators could substantially improve the least squares (LS) and classical PCR estimators under the PRESS criterion.