Identify the natural and socio-economic influencing factors of the new coronavirus pneumonia（COVID-19） incidence rates in Chinese cities

This study explored the effects of both natural and socio-economic factors, such as city size and healthcare capacity, on the spreading of COVID-19 in China's urban population from January 1 to March 5, 2020. Several statistical models and machine learning methods were used to identify the key determinants of the incidence rate of COVID-19. Based on the interpretable machine learning framework, possible nonlinear relationships between incidences and key impact factors were explored. The results showed that the incidence rate of COVID-19 in cities was influenced by several factors simultaneously. Among the factors, the population inflow rate from Wuhan was the factor that showed the highest correlation coefficient（0.43）, followed by the population growth rate（0.38）. Population migration size, city size and healthcare capacity were the key influencing factors. Nonlinear relationships existed between the key influencing factors and incidence rates. To be specific, the inflow rate from Wuhan had a S-shaped relationship and reaches an asymptote after 2%;the population density had an approximately linear relationship;the per capita GDP showed an evident inverted U curve with the per capita GDP over 100,000 yuan as the inflection point. City development needs to pay more attention to population density control and economic growth in order to bring more health benefits.

Rapid isothermal amplification and portable detection system for SARS-CoV-2.

The COVID-19 pandemic provides an urgent example where a gap exists between availability of state-of-the-art diagnostics and current needs. As assay protocols and primer sequences become widely known, many laboratories perform diagnostic tests using methods such as RT-PCR or reverse transcription loop mediated isothermal amplification (RT-LAMP). Here, we report an RT-LAMP isothermal assay for the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus and demonstrate the assay on clinical samples using a simple and accessible point-of-care (POC) instrument. We characterized the assay by dipping swabs into synthetic nasal fluid spiked with the virus, moving the swab to viral transport medium (VTM), and sampling a volume of the VTM to perform the RT-LAMP assay without an RNA extraction kit. The assay has a limit of detection (LOD) of 50 RNA copies per µL in the VTM solution within 30 min. We further demonstrate our assay by detecting SARS-CoV-2 viruses from 20 clinical samples. Finally, we demonstrate a portable and real-time POC device to detect SARS-CoV-2 from VTM samples using an additively manufactured three-dimensional cartridge and a smartphone-based reader. The POC system was tested using 10 clinical samples, and was able to detect SARS-CoV-2 from these clinical samples by distinguishing positive samples from negative samples after 30 min. The POC tests are in complete agreement with RT-PCR controls. This work demonstrates an alternative pathway for SARS-CoV-2 diagnostics that does not require conventional laboratory infrastructure, in settings where diagnosis is required at the point of sample collection.

Smartphone-based multiplex 30-minute nucleic acid test of live virus from nasal swab extract.

Rapid, sensitive and specific detection and reporting of infectious pathogens is important for patient management and epidemic surveillance. We demonstrated a point-of-care system integrated with a smartphone for detecting live virus from nasal swab media, using a panel of equine respiratory infectious diseases as a model system for corresponding human diseases such as COVID-19. Specific nucleic acid sequences of five pathogens were amplified by loop-mediated isothermal amplification on a microfluidic chip and detected at the end of reactions by the smartphone. Pathogen-spiked horse nasal swab samples were correctly diagnosed using our system, with a limit of detection comparable to that of the traditional lab-based test, polymerase chain reaction, with results achieved in ∼30 minutes.

Exploring College Student’s Perspectives on Global Mobility during the COVID-19 Pandemic Recovery

At the time of writing, more than 22 million cases of COVID-19 have been reported worldwide, and at least 770 thousand deaths. Under the pressure of the pandemic, promoting global mobility has become an emerging issue in higher education settings. Although various methods of enhancing student mobility have been implemented, little research has as yet confirmed the pandemic challenges for students. This study investigates the global mobility of Chinese college students and the factors influencing their travel decisions. A self-designed questionnaire, consisting of 15 critical indicators of mobile capabilities, intentions, and implementation decisions, was administered to collect data from 2226 participants. The Minitab and Amos software were used to conduct exploratory factor analysis (EFA) and to detect latent relationships among the data with structural equation modeling (SEM). The SEM and logistic regression model provide a clear picture of the relations among the variables, and show that international intention is the key indicator of global mobility implementation under pressure.