We developed a dynamic forecasting model for Zika virus (ZIKV), based on real-time online search data from Google Trends (GTs). It was designed to provide Zika virus disease (ZVD) surveillance and detection for Health Departments, and predictive numbers of infection cases, which would allow them sufficient time to implement interventions. In this study, we found a strong correlation between Zika-related GTs and the cumulative numbers of reported cases (confirmed, suspected and total cases; p<0.001). Then, we used the correlation data from Zika-related online search in GTs and ZIKV epidemics between 12 February and 20 October 2016 to construct an autoregressive integrated moving average (ARIMA) model (0, 1, 3) for the dynamic estimation of ZIKV outbreaks. The forecasting results indicated that the predicted data by ARIMA model, which used the online search data as the external regressor to enhance the forecasting model and assist the historical epidemic data in improving the quality of the predictions, are quite similar to the actual data during ZIKV epidemic early November 2016. Integer-valued autoregression provides a useful base predictive model for ZVD cases. This is enhanced by the incorporation of GTs data, confirming the prognostic utility of search query based surveillance. This accessible and flexible dynamic forecast model could be used in the monitoring of ZVD to provide advanced warning of future ZIKV outbreaks.
Many educational institutions have adopted e-learning under COVID-19 pandemic to maintain school teaching activities. Most teachers were encouraged to use online instruction in early February 2020. Thus, whether online learning changes students’ learning habits and replaces traditional physical teaching methods, online learning has become a keen topic. Based on the push–pull–mooring model, we proposed a comprehensive research model and explored the impact of online learning during COVID-19 pandemic on students’ attitude and behavioral intention. We found that push effects (perceived security risk, learning convenience, and service quality), pull effects (usefulness, ease of use, teacher’s teaching attitude, task-technology fit), and mooring effects (switching cost, habit) had significantly influence the switching intentions of users from physical course to online learning platforms. The findings of this study will bring more insights into e-learning during an epidemic crisis.
In response to the Covid-19 pandemic, online learning has been carried out in many countries with different types of online learning models being promoted and implemented. In the global pandemic continues, the education environment is forced to change from traditional classroom or blended teaching mode to online learning teaching model. With the outbreak of COVID-19, China was the first to announce that online courses are to be implemented in February 2020. In China, whether online learning can replace traditional offline teaching has become a topic worth discussing. Therefore, this study investigates university students in China by questionnaires and discussions of this topic. The study is based on the Push–Pull Mooring model. Based on 854 valid responses collected from an online survey questionnaire, structural equation modeling was employed to examine the research model. The results show that push effects (Perceived security risk, Learning convenience, and Service quality), pull effects (Usefulness, Ease of use, Teacher's Teaching Attitude, Task-technology Fit), and mooring effects (habit) all significantly influence users' switching intentions from offline to online learning platform. Finally, this study explores whether push–pull–mooring can be a reference for promoting and implementing online learning courses in Chinese colleges and universities in the future after the pandemic.
We developed a dynamic forecasting model for Zika virus (ZIKV), based on real-time online search data from Google Trends (GTs). It was designed to provide Zika virus disease (ZVD) surveillance and detection for Health Departments, and predictive numbers of infection cases, which would allow them sufficient time to implement interventions. In this study, we found a strong correlation between Zika-related GTs and the cumulative numbers of reported cases (confirmed, suspected and total cases; p<0.001). Then, we used the correlation data from Zika-related online search in GTs and ZIKV epidemics between 12 February and 20 October 2016 to construct an autoregressive integrated moving average (ARIMA) model (0, 1, 3) for the dynamic estimation of ZIKV outbreaks. The forecasting results indicated that the predicted data by ARIMA model, which used the online search data as the external regressor to enhance the forecasting model and assist the historical epidemic data in improving the quality of the predictions, are quite similar to the actual data during ZIKV epidemic early November 2016. Integer-valued autoregression provides a useful base predictive model for ZVD cases. This is enhanced by the incorporation of GTs data, confirming the prognostic utility of search query based surveillance. This accessible and flexible dynamic forecast model could be used in the monitoring of ZVD to provide advanced warning of future ZIKV outbreaks.
In order to enable online learning to continue developing when the COVID-19 pandemic passes, this study aimed to identify the critical factors that affected the use of e-learning by university students during the pandemic. These critical factors will help to increase the efficiency of future development and deployment of online learning systems. Through a literature review, this study employed the technology acceptance model, social support, and task–technology fit as the theoretical basis to establish the framework of the online learning environment with regards to the technology acceptance model in the context of emergency management. A questionnaire survey was administered to students in universities that had implemented online teaching during the pandemic, and 552 valid responses were collected. The survey explored the factors affecting the willingness of higher education institution students to continue using online learning, and the following conclusions were drawn. (1) The easier an online learning platform was to navigate, the better it was perceived by the students, and thus the students were more willing to use it. (2) Ease of use and usefulness were associated with the teachers’ choice of platform and their ability to achieve a satisfactory fit between the course design and platform navigation, which thereby affected the students’ learning outcomes and attitude towards use. (3) The positive attitude of teachers towards teaching increased the students’ perceived ease of use of online learning. (4) During the pandemic, family support—a major support for teachers in online teaching—enhanced teachers’ attitudes towards, and willingness to provide, online teaching. A high level of support showed that the parents urged the students to learn and complete online learning tasks as instructed by the teachers, implying that family support could affect the students’ habits towards, adaptation to, and identification of online learning. The study results provide insights into the factors affecting the willingness of teachers and students to continue using e-learning platforms.
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