Predicting the number of new suspected or confirmed cases of novel coronavirus disease 2019 (COVID-19) is crucial in the prevention and control of the COVID-19 outbreak. Social media search indexes (SMSI) for dry cough, fever, chest distress, coronavirus, and pneumonia were collected from 31 December 2019 to 9 February 2020. The new suspected cases of COVID-19 data were collected from 20 January 2020 to 9 February 2020. We used the lagged series of SMSI to predict new suspected COVID-19 case numbers during this period. To avoid overfitting, five methods, namely subset selection, forward selection, lasso regression, ridge regression, and elastic net, were used to estimate coefficients. We selected the optimal method to predict new suspected COVID-19 case numbers from 20 January 2020 to 9 February 2020. We further validated the optimal method for new confirmed cases of COVID-19 from 31 December 2019 to 17 February 2020. The new suspected COVID-19 case numbers correlated significantly with the lagged series of SMSI. SMSI could be detected 6–9 days earlier than new suspected cases of COVID-19. The optimal method was the subset selection method, which had the lowest estimation error and a moderate number of predictors. The subset selection method also significantly correlated with the new confirmed COVID-19 cases after validation. SMSI findings on lag day 10 were significantly correlated with new confirmed COVID-19 cases. SMSI could be a significant predictor of the number of COVID-19 infections. SMSI could be an effective early predictor, which would enable governments’ health departments to locate potential and high-risk outbreak areas.

Previous studies have investigated how customer purchase intention is influenced by live streaming. However, no study has investigated the effect of service marketing mix (7Ps) on consumer shopping behavior from sellers’ perspectives. The present study is designed to shed light on the relationships among the 7Ps and the customers’ purchase intention through watching the broadcasters’ show. An integrative marketing-oriented model is proposed and tested using data collected from 330 customers (including 237 shoppers for apparel and 93 customers for seafood) through Facebook live shopping platforms. The research results reveal that promotion, placement, and physical evidence have positive effects on customers’ purchase intention. In addition, the watching intention had a positive effect on purchase intention. It is also found that watching intention has a full mediation effect on the relationship between the 7Ps marketing mix and the purchase intention. The implications of the findings and issues for future research are also discussed.

The purpose of this paper is to present an integrated model for live streaming shopping (LSS) to examine the antecedents and consequences of consumers’ attitudes. Based on the stimulus–organism–response (S-O-R) paradigm, this study investigates marketing stimuli effects within a live streaming commerce context on viewers’ cognitive and affective states by drawing from their unified knowledge of technology acceptance model (TAM), utilitarian gratification theory (UGT) and the theory of reasoned action (TRA) underpinnings and their subsequent responses. Data collected from 402 live streaming shoppers of several reputable retailers in Mainland China were used to empirically test the proposed framework via structural equation modelling. The theoretical framework found support for most hypothesized relationships. The research results confirm that the 7Ps marketing mix strategies as the stimuli factors positively influence the consumers’ cognitive and emotional states represented by perceived usefulness, perceived value, information gratification, entertainment gratification, and social interaction. These factors have been found to be the direct predictors of consumers’ attitudes toward LSS. Furthermore, consumers’ response in terms of their attitudes influences their intention to watch the show and subsequently affect their purchase intention. This paper bridges a gap in LSS studies, contributing to develop a more comprehensive framework and provide a better understanding of the factors that influence the the formation of consumers’ attitudes, and the influence of the formed attitudes on the intention to engage in LSS before actually making a purchase decision. The findings enable managers to determine how resources should be allocated to improve platform’s competence.

The purpose of this study is to understand the relationship among leisure physical activity, sedentary lifestyle, physical fitness, and happiness in healthy elderly adults aged over 65 years old in Taiwan. Data were recruited from the National Physical Fitness Survey in Taiwan, which was proposed in the Project on the Establishment of Physical Fitness Testing Stations by the Sports Administration of the Ministry of Education. Participants were recruited from fitness testing stations set up in 22 counties and cities from October 2015 to May 2016. A total of 20,111 healthy older adults aged 65–102 years were recruited as research participants. The fitness testing procedure was described to all participants, who were provided with a standardized structured questionnaire. Participants’ data included sex, city or county of residence, living status (living together with others or living alone), education level, and income. Physical fitness testing was conducted in accordance with The Fitness Guide for Older Adults published by the Sports Administration of the Ministry of Education. The testing involved cardiorespiratory endurance, muscle strength, muscle endurance, flexibility, balance, and body composition. The t-test was used to evaluate the differences between continuous and grade variables under the two classification variables of sex, city or county of residence, and living status. We used the MARS (multivariate adaptive regression splines) model to analyze the effects of physical fitness variables and leisure physical activity variables on happiness. Among healthy elderly adults, sex, age, living status, body mass index, and leisure physical activity habits proved to be related to happiness. Aerobic endurance (2-min step test), muscular strength and endurance (30-s arm curl and 30-s chair stand tests), flexibility (back stretch and chair sit-and-reach tests), and balance ability (8-foot up-and-go tests and one-leg stance with eyes open tests) were found to be related to happiness. The results of this study indicate that increased physical activity and intensity, as well as physical fitness performance, are associated with improved happiness.

Coronary artery bypass surgery grafting (CABG) is a commonly efficient treatment for coronary artery disease patients. Even if we know the underlying disease, and advancing age is related to survival, there is no research using the one year before surgery and operation-associated factors as predicting elements. This research used different machine-learning methods to select the features and predict older adults’ survival (more than 65 years old). This nationwide population-based cohort study used the National Health Insurance Research Database (NHIRD), the largest and most complete dataset in Taiwan. We extracted the data of older patients who had received their first CABG surgery criteria between January 2008 and December 2009 (n = 3728), and we used five different machine-learning methods to select the features and predict survival rates. The results show that, without variable selection, XGBoost had the best predictive ability. Upon selecting XGBoost and adding the CHA2DS score, acute pancreatitis, and acute kidney failure for further predictive analysis, MARS had the best prediction performance, and it only needed 10 variables. This study’s advantages are that it is innovative and useful for clinical decision making, and machine learning could achieve better prediction with fewer variables. If we could predict patients’ survival risk before a CABG operation, early prevention and disease management would be possible.