The outbreak of coronavirus disease 2019 (COVID-19), caused by the virus SARS-CoV-2, has been rapidly increasing in the United States. Boroughs of New York City, including Queens county, turn out to be the epicenters of this infection. According to the data provided by the New York State Department of Health, most of the cases of new COVID-19 infections in New York City have been found in the Queens county where 42,023 people have tested positive, and 3221 people have died as of 20 April 2020. Person-to-person transmission and travels were implicated in the initial spread of the outbreaks, but factors related to the late phase of rapidly spreading outbreaks in March and April are still uncertain. A few previous studies have explored the links between air pollution and COVID-19 infections, but more data is needed to understand the effects of short-term exposures of air pollutants and meteorological factors on the spread of COVID-19 infections, particularly in the U.S. disease epicenters. In this study, we have focused on ozone and PM2.5, two major air pollutants in New York City, which were previously found to be associated with respiratory viral infections. The aim of our regression modeling was to explore the associations among ozone, PM2.5, daily meteorological variables (wind speed, temperature, relative humidity, absolute humidity, cloud percentages, and precipitation levels), and COVID-19 confirmed new cases and new deaths in Queens county, New York during March and April 2020. The results from these analyses showed that daily average temperature, daily maximum eight-hour ozone concentration, average relative humidity, and cloud percentages were significantly and positively associated with new confirmed cases related to COVID-19; none of these variables showed significant associations with new deaths related to COVID-19. The findings indicate that short-term exposures to ozone and other meteorological factors can influence COVID-19 transmission and initiation of the disease, but disease aggravation and mortality depend on other factors.
In the field of diagnostic studies, the area under the ROC curve (AUC) serves as an overall measure of a biomarker/diagnostic test's accuracy. Youden index, defined as the overall correct classification rate minus one at the optimal cut-off point, is another popular index. For continuous biomarkers of binary disease status, although researchers mainly evaluate the diagnostic accuracy using AUC, for the purpose of making diagnosis, Youden index provides an important and direct measure of the diagnostic accuracy at the optimal threshold and hence should be taken into consideration in addition to AUC. Furthermore, AUC and Youden index are generally correlated. In this paper, we initiate the idea of evaluating diagnostic accuracy based on AUC and Youden index simultaneously. As the first step toward this direction, this paper only focuses on the confidence region estimation of AUC and Youden index for a single marker. We present both parametric and non-parametric approaches for estimating joint confidence region of AUC and Youden index. We carry out extensive simulation study to evaluate the performance of the proposed methods. In the end, we apply the proposed methods to a real data set.
Engaging social media key opinion leaders to facilitate health education about vaccination in their tweets may allow reaching a wider audience online.
Background Information and emotions towards public health issues could spread widely through online social networks. Although aggregate metrics on the volume of information diffusion are available, we know little about how information spreads on online social networks. Health information could be transmitted from one to many (i.e. broadcasting) or from a chain of individual to individual (i.e. viral spreading). The aim of this study is to examine the spreading pattern of Ebola information on Twitter and identify influential users regarding Ebola messages. Methods Our data was purchased from GNIP. We obtained all Ebola-related tweets posted globally from March 23, 2014 to May 31, 2015. We reconstructed Ebola-related retweeting paths based on Twitter content and the follower-followee relationships. Social network analysis was performed to investigate retweeting patterns. In addition to describing the diffusion structures, we classify users in the network into four categories (i.e., influential user, hidden influential user, disseminator, common user) based on following and retweeting patterns. Results On average, 91% of the retweets were directly retweeted from the initial message. Moreover, 47.5% of the retweeting paths of the original tweets had a depth of 1 (i.e., from the seed user to its immediate followers). These observations suggested that the broadcasting was more pervasive than viral spreading. We found that influential users and hidden influential users triggered more retweets than disseminators and common users. Disseminators and common users relied more on the viral model for spreading information beyond their immediate followers via influential and hidden influential users. Conclusions Broadcasting was the dominant mechanism of information diffusion of a major health event on Twitter. It suggests that public health communicators can work beneficially with influential and hidden influential users to get the message across, because influential and hidden influential users can reach more people that are not following the public health Twitter accounts. Although both influential users and hidden influential users can trigger many retweets, recognizing and using the hidden influential users as the source of information could potentially be a cost-effective communication strategy for public health promotion. However, challenges remain due to uncertain credibility of these hidden influential users. Electronic supplementary material The online version of this article (10.1186/s12889-019-6747-8) contains supplementary material, which is available to authorized users.
In practice, usually multiple biomarkers are measured on the same subject for disease diagnosis. Combining these biomarkers into a single score could improve diagnostic accuracy. Many researchers have addressed the problem of finding the optimal linear combination based on maximizing the area under ROC curve (AUC). Actually, such combined score might have less than optimal property at the diagnostic threshold. In this paper, we propose the idea of using Youden index as an objective function for searching the optimal linear combination. The combined score directly achieves the maximum overall correct classification rate at the diagnostic threshold corresponding to Youden index; in other words, it is the optimal linear combination score for making the disease diagnosis. We present both empirical and numerical searching methods for the optimal linear combination. We carry out extensive simulation study to investigate the performance of the proposed methods. Additionally, we empirically compare the optimal overall classification rates between the proposed combination based on Youden index and the traditional one based on AUC and demonstrate a significant gain in diagnostic accuracy for the proposed combination. In the end, we apply the proposed methods to a real data set.
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