The model was trained with data recorded between March 1 and August 31, 2020, and tested on data recorded between September 1 and October 31, 2020.
 
  The model reported sensitivities of 81%, 92%, and 90% for California, Indiana, and Iowa, respectively. The precision in each state was above 85% while specificity and accuracy scores were generally >95%.
 
  Our parsimonious model provides a generalizable and simple alternative approach to outbreak prediction. This methodology can be applied to diverse regions to help state officials and hospitals with resource allocation and to guide risk management, community education, and mitigation strategies.
 Our parsimonious model provides a generalizable and simple alternative approach to outbreak prediction. This methodology can be applied to diverse regions to help state officials and hospitals with resource allocation and to guide risk management, community education, and mitigation strategies.
  Telehealth plays a key role in supporting health care systems and influencing methods of health care delivery. Government laws and medical operating protocols have been largely modified to provide remote care to reduce social contact and ensure a safer patient environment. In the Kingdom of Saudi Arabia (KSA), the Ministry of Health (MOH) introduced several forms of telemedicine as alternatives to face-to-face consultations in clinical settings.
 
  This study aimed to assess the awareness and utilization of telehealth services before and during the COVID-19 outbreak in the KSA.
 
  In this longitudinal study, we compared the awareness and utilization of 937-telephone health services (ie, a toll-free telephone service to provide medical and administrative health care services at any time for the population) before and during the COVID-19 outbreak in the KSA. Using a convenience sampling technique, a validated web-based questionnaire was distributed on social media platforms (Facebook, Twitter, and WhatsApp) at of the barriers and facilitators to the use of 937-telephone health services for different groups of the population.
 Our findings indicate significant increases in the awareness and utilization of 937-telephone health services during the early days of the COVID-19 pandemic, suggesting an increase in public acceptance of the service and providing evidence of an equitable telemedicine service for the population. Further studies are needed to provide a deeper understanding of the barriers and facilitators to the use of 937-telephone health services for different groups of the population.
  The changing pattern of anxiety and stress experienced by pregnant women during the COVID-19 pandemic is unknown.
 
  We aimed to examine the sources of anxiety and stress in pregnant women in Japan during the COVID-19 pandemic.
 
  We performed content analysis of 1000 questions posted on the largest social website in Japan (Yahoo! Chiebukuro) from January 1 to May 25, 2020 (end date of the national state of emergency). The Gwet AC1 coefficient was used to verify interrater reliability.
 
  A total 12 categories were identified. Throughout the study period, anxiety related to going outdoors appeared most frequent, followed by anxiety regarding employment and infection among family and friends. Following the declaration of the state of national emergency at the peak of the infection, infection-related anxiety decreased, whereas anxiety about social support and mood disorders increased. Stress regarding relationships appeared frequent throughout the pandemic.
 
  The sources of anxiety and stress in pregnant women in Japan changed during the pandemic. Our results suggest the need for rapid communications in the early phase of a pandemic as well as long-term psychosocial support to provide optimal support to pregnant women in Japan. Health care professionals should understand the changing pattern of requirements among pregnant women.
 The sources of anxiety and stress in pregnant women in Japan changed during the pandemic. Our results suggest the need for rapid communications in the early phase of a pandemic as well as long-term psychosocial support to provide optimal support to pregnant women in Japan. Health care professionals should understand the changing pattern of requirements among pregnant women.
  COVID-19 is a highly contagious and highly pathogenic disease caused by a novel coronavirus, SARS-CoV-2, and it has become a pandemic. As a vulnerable population, university students are at high risk during the epidemic, as they have high mobility and often overlook the severity of the disease because they receive incomplete information about the epidemic. In addition to the risk of death from infection, the epidemic has placed substantial psychological pressure on the public. In this respect, university students are more prone to psychological problems induced by the epidemic compared to the general population because for most students, university life is their first time outside the structure of the family, and their mental development is still immature. Internal and external expectations and academic stress lead to excessive pressure on students, and unhealthy lifestyles also deteriorate their mental health. The outbreak of COVID-19 was a significant social event, and it could potentially have a great imlts are beneficial for identifying groups of university students who are at risk for possible mental health issues so that universities and families can prevent or intervene in the development of potential mental health issues at the early stage of their development.[This corrects the article DOI 10.2196/17095.].Many deep-learning methods have been developed for fault diagnosis. However, due to the difficulty of collecting and labeling machine fault data, the datasets in some practical applications are relatively much smaller than the other big data benchmarks. In addition, the fault data come from different machines.  https://www.selleckchem.com/products/bx-795.html  Therefore, on some occasions, fault diagnosis is a multidomain problem with small data, where satisfactory transfer performance is difficult to obtain and has been rarely explored from the few-shot learning viewpoint. Different from the existing deep transfer learning solutions, a novel transfer relation network (TRN), combining a few-shot learning mechanism and transfer learning, is developed in this study. Specifically, the fault diagnosis problem has been treated as a similarity metric-learning problem instead of solely feature weighted classification. A feature net and a relation net have been, respectively, constructed for feature extraction and relation computation. The Siamese structure has been borrowed to extract the features of the source and the target domain samples with shared weights.