Utilization of the TRAIN Learning Network for Online Disaster Medicine and Public Health Training During the COVID-19 Pandemic.

OBJECTIVE: The coronavirus disease 2019 (COVID-19) pandemic dramatically accelerated a growing trend toward online and asynchronous education and professional training, including in the disaster medicine and public health sector. This study analyzed the impact of the COVID-19 pandemic on the growth of the TRAIN Learning Network (TRAIN) for the year 2020 and evaluated pandemic-related changes in use patterns by disaster and public health professionals. METHODS: The TRAIN database was queried to determine the change in the number of registered users, total courses completed, and courses completed related to COVID-19 during 2020. RESULTS: In 2020, a total of 755,222 new users joined the platform - nearly 3 times the average added annually over the preceding 5 y (2015-2019). TRAIN users completed 3,259,074 training courses in 2020, more than double the average number of training courses that were completed annually from 2015-2019. In addition, 17.8% of all newly added disaster and public health training courses in 2020 were specifically related to COVID-19. CONCLUSION: Online education provided by TRAIN is a critical tool for just-in-time disaster health training following a disaster event or public health emergency, including in a global health crisis such as a pandemic.

Endemic mycoses in immunocompromised hosts.

Endemic mycoses remain a significant cause of morbidity and mortality among immunocompromised patients. As the number of immunosuppressed individuals increases worldwide, the incidence of endemic mycoses is also expected to rise. In immunocompromised patients, endemic mycoses can present in atypical fashion, cause more severe and/or disseminated disease, and result in higher mortality. Despite several noteworthy advances over the past decade, significant challenges remain with regard to the prevention, diagnosis, and therapy of endemic mycoses in immunocompromised hosts. This review highlights important developments related to the epidemiology, diagnosis, treatment, and prevention of commonly encountered endemic mycoses. We also discuss emerging topics, knowledge gaps, and areas of future research.

Using network theory to identify the causes of disease outbreaks of unknown origin.

The identification of undiagnosed disease outbreaks is critical for mobilizing efforts to prevent widespread transmission of novel virulent pathogens. Recent developments in online surveillance systems allow for the rapid communication of the earliest reports of emerging infectious diseases and tracking of their spread. The efficacy of these programs, however, is inhibited by the anecdotal nature of informal reporting and uncertainty of pathogen identity in the early stages of emergence. We developed theory to connect disease outbreaks of known aetiology in a network using an array of properties including symptoms, seasonality and case-fatality ratio. We tested the method with 125 reports of outbreaks of 10 known infectious diseases causing encephalitis in South Asia, and showed that different diseases frequently form distinct clusters within the networks. The approach correctly identified unknown disease outbreaks with an average sensitivity of 76 per cent and specificity of 88 per cent. Outbreaks of some diseases, such as Nipah virus encephalitis, were well identified (sensitivity = 100%, positive predictive values = 80%), whereas others (e.g. Chandipura encephalitis) were more difficult to distinguish. These results suggest that unknown outbreaks in resource-poor settings could be evaluated in real time, potentially leading to more rapid responses and reducing the risk of an outbreak becoming a pandemic.