Thriving in a Pandemic: Lessons Learned from a Resilient University Program Seen Through the CoI Lens (preprint)

In March 2020, college campuses underwent a sudden transformation to online learning due to the COVID-19 outbreak. To understand the impact of COVID-19 on students' expectations, this study conducted a three-year survey from ten core courses within the Project Management Center for Excellence at the University of Maryland. The study involved two main steps: 1) a statistical analysis to evaluate students' expectations regarding "student," "class," "instructor," and "effort;" and 2) a lexical salience-valence analysis (LSVA) through the lens of the Community of Inquiry (CoI) framework to show the changes of students' expectations. The results revealed that students' overall evaluations maintained relatively consistent amid the COVID-19 teaching period. However, there were significant shifts of the student expectations toward Cognitive, Social and Teaching Presence course elements based on LSVA results. Also, clear differences emerged between under-graduates and graduates in their expectations and preferences in course design and delivery. These insights provide practical recommendations for course instructors in designing effective online courses.

Can Social Media Data Be Utilized to Enhance Early Warning: Retrospective Analysis of the U.S. Covid-19 Pandemic (preprint)

ABSTRACT The U.S. needs early warning systems to help it contain the spread of infectious diseases. Conventional early warning systems use lab-test results or dynamic records to signal early warning signs. New early warning systems can supplement these data with indicators of public awareness like news articles and search queries. This study aims to explore the potential of utilizing social media data to enhance early warning of the COVID-19 outbreak. To demonstrate the feasibility, this study conducts a retrospective analysis and investigates more than 14 million related Twitter postings in the date range from January 20 to March 10, 2020. With the aid of natural language processing tools and machine learning classifiers, this study classifies each of these tweets into either a signal or a non-signal. In this study, a “signal” tweet implies that the user recognized the COVID-19 outbreak risk in the U.S. This study then proposes a parameter “signal ratio” to signal warning signs of the COVID-19 pandemic over periods. Results reveal that social media data and the signal ratio can detect the hazards ahead of the COVID-19 outbreak. This claim has been validated with a leading time of 16 days through the comparison to other referenced methods based on Google trends or media news.

Estimating the case fatality ratio of the COVID-19 epidemic in China (preprint)

Background: Corona Virus Disease 2019 (COVID-19) due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in Wuhan city and rapidly spread throughout China since late December 2019. Crude case fatality ratio (CFR) with dividing the number of known deaths by the number of confirmed cases does not represent the true CFR and might be off by orders of magnitude. We aim to provide a precise estimate of the CFR of COVID-19 using statistical models at the early stage of the epidemic. Methods: We extracted data from the daily released epidemic report published by the National Health Commission P. R. China from 20 Jan 2020, to 1 March 2020. Competing risk model was used to obtain the cumulative hazards for death, cure, and cure-death hazard ratio. Then the CFR was estimated based on the slope of the last piece in joinpoint regression model, which reflected the most recent trend of the epidemic. Results: As of 1 March 2020, totally 80,369 cases were diagnosed as COVID-19 in China. The CFR of COVID-19 were estimated to be 70.9% (95% CI: 66.8%-75.6%) during Jan 20-Feb 2, 20.2% (18.6%-22.1%) during Feb 3-14, 6.9% (6.4%-7.4%) during Feb 15-23, 1.5% (1.4%-1.6%) during Feb 24-March 1 in Hubei province, and 20.3% (17.0%-25.3%) during Jan 20-28, 1.9% (1.8%-2.1%) during Jan 29-Feb 12, 0.9% (0.8%-1.1%) during Feb 13-18, 0.4% (0.4%-0.5%) during Feb 19-March 1 in other areas of China, respectively. Conclusions: Based on analyses of public data, we found that the CFR in Hubei was much higher than that of other regions in China, over 3 times in all estimation. The CFR would follow a downwards trend based on our estimation from recently released data. Nevertheless, at early stage of outbreak, CFR estimates should be viewed cautiously because of limited data source on true onset and recovery time.