FinTech platforms are one of the most important elements in the rapidly digitized world’s economy. This study investigates the factors that affected individuals’ acceptance of FinTech services, especially on internet wealth management platforms. The current research extends the unified theory of acceptance and use of technology (UTAUT) published by MIS Quarterly with integrated financial consumption attributes (i.e., perceived value and perceived risk) alongside the core construct of UTAUT. Data were collected from an established survey company. The empirical results indicate that perceived value, perceived risk, and social influence are strongly related to individuals’ FinTech adoption intention, whereas performance expectancy, effort expectancy, and perceived risk affect individuals’ perceived value, which in turn influences adoption intention. The proposed FinTech adoption model could contribute to information technology (IT) adoption research by extending the UTAUT in which individuals’ performance expectancy and effort expectancy affect their adoption intention indirectly through perceived value. Finally, the implications of the proposed new model for future research and FinTech practice are discussed.

Cost-sensitive learning methods guaranteeing privacy are becoming crucial nowadays in many applications where increasing use of sensitive personal information is observed. However, there has no optimal learning scheme developed in the literature to learn cost-sensitive classifiers under constraint of enforcing differential privacy. Our approach is to first develop a unified framework for existing cost-sensitive learning methods by incorporating the weight constant and weight functions into the classical regularized empirical risk minimization framework. Then, we propose two privacy-preserving algorithms with output perturbation and objective perturbation methods, respectively, to be integrated with the cost-sensitive learning framework. We showcase how this general framework can be used analytically by deriving the privacy-preserving cost-sensitive extensions of logistic regression and support vector machine. Experimental evidence on both synthetic and real data sets verifies that the proposed algorithms can reduce the misclassification cost effectively while satisfying the privacy requirement. A theoretical investigation is also conducted, revealing a very interesting analytic relation, i.e., that the choice of the weight constant and weight functions does not only influence the Fisher-consistent property (population minimizer of expected risk with a specific loss function leads to the Bayes optimal decision rule) but also interacts with privacy-preserving levels to affect the performance of classifiers significantly.

There is limited evidence concerning the association between air pollution and different outpatient visits in moderately polluted areas. This paper investigates the effects of moderate-level air pollution on outpatient visits associated with six categories of clinic department. We analyzed a total of 1,340,791 outpatient visits for the pediatric, respiratory, ear-nose-throat (ENT), cardiovascular, ophthalmology, and orthopedics departments from January 2016 to December 2018. A distributed lag nonlinear model was used to analyze the associations and was fitted and stratified by age and season (central heating season and nonheating season). We found ${\mathrm{SO}}_2$ had the largest effect on pediatrics visits (RR = 1.105 (95%CI: 1.090, 1.121)). Meanwhile, ${\mathrm{PM}}_{2.5}$ and ${\mathrm{SO}}_2$ had greater effects on ENT visits for people under 50 years old. The results showed a strong association between ${\mathrm{O}}_3$ and cardiovascular outpatient visits in the nonheating season (RR = 1.273, 95% CI: 1.189,1.358). The results showed every 10 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$ increase in ${\mathrm{SO}}_2$ was associated with a lower number of respiratory outpatient visits. Significant different associations were observed in ${\mathrm{PM}}_{2.5}$, ${\mathrm{NO}}_2$, CO, and ${\mathrm{O}}_3$ on ophthalmology visits between the heating and nonheating seasons. Although no significant association has been found in existing studies, our findings showed ${\mathrm{PM}}_{2.5}$ and ${\mathrm{NO}}_2$ were significantly related to orthopedic outpatient visits for people under 60 (RR = 1.063 (95%CI: 1.032, 1.095), RR = 1.055 (95%CI: 1.011, 1.101)). This study also found that the effect-level concentrations of air pollutants for some clinic departments were lower than the national standards, which means that people should also pay more attention when the air quality is normal.

Real-time online data sources have contributed to timely and accurate forecasting of influenza activities while also suffered from instability and linguistic noise. Few previous studies have focused on unofficial online news articles, which are abundant in their numbers, rich in information, and relatively low in noise. This study examined whether monitoring both official and unofficial online news articles can improve influenza activity forecasting accuracy during influenza outbreaks. Data were retrieved from a Chinese commercial online platform and the website of the Chinese National Influenza Center. We modeled weekly fractions of influenza-related online news articles and compared them against weekly influenza-like illness (ILI) rates using autoregression analyses. We retrieved 153,958,695 and 149,822,871 online news articles focusing on the south and north of mainland China separately from 6 October 2019 to 17 May 2020. Our model based on online news articles could significantly improve the forecasting accuracy, compared to other influenza surveillance models based on historical ILI rates (p = 0.002 in the south; p = 0.000 in the north) or adding microblog data as an exogenous input (p = 0.029 in the south; p = 0.000 in the north). Our finding also showed that influenza forecasting based on online news articles could be 1–2 weeks ahead of official ILI surveillance reports. The results revealed that monitoring online news articles could supplement traditional influenza surveillance systems, improve resource allocation, and offer models for surveillance of other emerging diseases.

Background: The SARS-COV-2 virus and its variants pose extraordinary challenges for public health worldwide. Timely and accurate forecasting of the COVID-19 epidemic is key to sustaining interventions and policies and efficient resource allocation. Internet-based data sources have shown great potential to supplement traditional infectious disease surveillance, and the combination of different Internet-based data sources has shown greater power to enhance epidemic forecasting accuracy than using a single Internet-based data source. However, existing methods incorporating multiple Internet-based data sources only used real-time data from these sources as exogenous inputs but did not take all the historical data into account. Moreover, the predictive power of different Internet-based data sources in providing early warning for COVID-19 outbreaks has not been fully explored. Objective: The main aim of our study is to explore whether combining real-time and historical data from multiple Internet-based sources could improve the COVID-19 forecasting accuracy over the existing baseline models. A secondary aim is to explore the COVID-19 forecasting timeliness based on different Internet-based data sources. Methods: We first used core terms and symptom-related keyword-based methods to extract COVID-19–related Internet-based data from December 21, 2019, to February 29, 2020. The Internet-based data we explored included 90,493,912 online news articles, 37,401,900 microblogs, and all the Baidu search query data during that period. We then proposed an autoregressive model with exogenous inputs, incorporating real-time and historical data from multiple Internet-based sources. Our proposed model was compared with baseline models, and all the models were tested during the first wave of COVID-19 epidemics in Hubei province and the rest of mainland China separately. We also used lagged Pearson correlations for COVID-19 forecasting timeliness analysis. Results: Our proposed model achieved the highest accuracy in all 5 accuracy measures, compared with all the baseline models of both Hubei province and the rest of mainland China. In mainland China, except for Hubei, the COVID-19 epidemic forecasting accuracy differences between our proposed model (model i) and all the other baseline models were statistically significant (model 1, t198=–8.722, P<.001; model 2, t198=–5.000, P<.001, model 3, t198=–1.882, P=.06; model 4, t198=–4.644, P<.001; model 5, t198=–4.488, P<.001). In Hubei province, our proposed model's forecasting accuracy improved significantly compared with the baseline model using historical new confirmed COVID-19 case counts only (model 1, t198=–1.732, P=.09). Our results also showed that Internet-based sources could provide a 2- to 6-day earlier warning for COVID-19 outbreaks. Conclusions: Our approach incorporating real-time and historical data from multiple Internet-based sources could improve forecasting accuracy for epidemics of COVID-19 and its variants, which may help improve public health agencies' interventions and resource allocation in mitigating and controlling new waves of COVID-19 or other relevant epidemics.