Using Geotagged Social Media Data to Explore Sentiment Changes in Tourist Flow: A Spatiotemporal Analytical Framework

Understanding sentiment changes in tourist flow is critical in designing exciting experiences for tourists and promoting sustainable tourism development. This paper proposes a novel analytical framework to investigate the tourist sentiment changes between different attractions based on geotagged social media data. Our framework mainly focuses on visualizing the detailed sentiment changes of tourists and exploring the valuable spatiotemporal pattern of the sentiment changes in tourist flow. The tourists were first identified from social media users. Then, we accurately evaluated the tourist sentiment by constructing a Chinese sentiment dictionary, grammatical rule, and sentiment score. Based on the location information of social media data, we built and visualized the tourist flow network. Last, to further reveal the impact of attractions on the sentiment of tourist flow, the positive and negative sentiment profiles were generated by mining social media texts. We took Beijing, a famous tourist destination in China, as a case study. Our results revealed the following: (1) the temporal trend of tourist sentiment has seasonal characteristics and is significantly influenced by government control policies against COVID-19;(2) due to the impact of the attraction's historical background, some tourist flows with highly decreased sentiment strength are linked to attractions;(3) on the long journey to the attraction, the sentiment strength of tourists decreases;and (4) bad traffic conditions can significantly decrease tourist sentiment. This study highlights the methodological implications of visualizing sentiment changes during collective tourist movement and provides comprehensive insight into the spatiotemporal pattern of tourist sentiment. [ABSTRACT FROM AUTHOR] Copyright of ISPRS International Journal of Geo-Information is the property of MDPI Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Early prediction model for progression and prognosis of severe patients with coronavirus disease 2019.

ABSTRACT: Coronavirus disease 2019 (COVID-19) has been a rampant worldwide health threat and we aimed to develop a model for early prediction of disease progression.This retrospective study included 124 adult inpatients with COVID-19 who presented with severe illness at admission and had a definite outcome (recovered or progressed to critical illness) during February 2020. Eighty-four patients were used as training cohort and 40 patients as validation cohort. Logistic regression analysis and receiver operating characteristic curve (ROC) analysis were used to develop and evaluate the prognostic prediction model.In the training cohort, the mean age was 63.4 ±â€Š1.5 years, and male patients (48, 57%) were predominant. Forty-three (52%) recovered, and 41 (49%) progressed to critical. Decreased lymphocyte count (LC, odds ratio [OR] = 4.40, P = .026), elevated lactate dehydrogenase levels (LDH, OR = 4.24, P = .030), and high-sensitivity C-reactive protein (hsCRP, OR = 1.01, P = .025) at admission were independently associated with higher odds of deteriorated outcome. Accordingly, we developed a predictive model for disease progression based on the levels of the 3 risk factors (LC, LDH, and hsCRP) with a satisfactory performance in ROC analysis (area under the ROC curve [AUC] = 0.88, P < .001) and the best cut-off value was 0.526 with the sensitivity and specificity of 75.0% and 90.7%, respectively. Then, the model was internally validated by leave-one-out cross-validation with value of AUC 0.85 (P < .001) and externally validated in another validation cohort (26 recovered patients and 14 progressed patients) with AUC 0.84 (P < .001).We identified 3 clinical indicators of risk of progression and developed a severe COVID-19 prognostic prediction model, allowing early identification and intervention of high-risk patients being critically illness.

Triglyceride to High-Density Lipoprotein Cholesterol Ratio is an Important Determinant of Cardiovascular Risk and Poor Prognosis in Coronavirus Disease-19: A Retrospective Case Series Study.

BACKGROUND: Acute myocardial injury and heart failure characterized by elevated cardiac troponin and decreased heart pump function are significant clinical features and prognostic factors of coronavirus disease-19 (COVID-19). Triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) ratio is an indicator of insulin resistance. This study aimed to explore the association of the TG/HDL-C ratio with cardiovascular risk and prognosis in COVID-19. METHODS: Ninety-eight laboratory-confirmed patients with COVID-19 admitted in a tertiary teaching hospital in Wuhan, China, were enrolled in this retrospective study. Regression models were used to investigate the association between TG/HDL-C ratio with myocardial injury, heart failure, severity, and mortality in COVID-19. RESULTS: Among the 98 patients, the mean age was 63.9±1.4 years, and male sex (58, 59%) was predominant. Forty-six patients (47%) were admitted to the intensive care unit (ICU), 32 (33%) and 46 (47%) patients suffered from myocardial injury and heart failure, respectively, and 36 (37%) patients died. The TG/HDL-C ratio was increased in patients with myocardial injury, heart failure, severe illness, and fatal outcome (P<0.05 for each). Baseline TG/HDL-C ratio significantly correlated with log transformed levels of plasma high-sensitivity cardiac troponin I (r=0.251, P=0.018), N-terminal brain natriuretic propeptide (r=0.274, P=0.008), glycated hemoglobin (r=0.239, P=0.038), and interleukin-6 (r=0.218, P=0.042). Multivariate logistic regression analysis showed that an increased TG/HDL-C ratio was independently associated with the risk of myocardial injury [odds ratio (OR)=2.73; P=0.013], heart failure (OR=2.64; P=0.019), disease severity (OR=3.01; P=0.032), and fatal outcome (OR=2.97; P=0.014). CONCLUSION: Increased TG/HDL-C ratio was independently associated with myocardial injury, heart failure, disease severity, and mortality in patients with COVID-19, and it may be a useful marker for early identification of patients with high risk and poor outcome.