What we can learn from the exported cases in detecting disease outbreaks - a case study of the COVID-19 epidemic.

PURPOSE: Early warning in the travel origins is crucial to prevent disease spreading. When travel origins have delays in reporting disease outbreaks, the exported cases could be used to estimate the epidemic. METHODS: We developed a Bayesian model to jointly estimate the epidemic prevalence and detection delay using the exported cases and their arrival and detection dates. We used simulation studies to discuss potential biases generated by the exported cases. We proposed a hypothesis testing framework to determine the epidemic severity. RESULTS: We applied the method to the early phase of the COVID-19 epidemic of Wuhan, United States, Italy, and Iran and found that the indicators estimated from the exported cases were consistent with the domestic data under certain scenarios. The exported cases could generate various biases if not modeled properly. We presented the required number of exported cases for determining different severity levels of the outbreak. CONCLUSIONS: The exported case data is a good addition to the domestic data but also has its drawbacks. Utilizing the diagnosis resources from all countries, we advocate that countries work collaboratively to strengthen the global infectious disease surveillance system.

Automating document classification with distant supervision to increase the efficiency of systematic reviews: A case study on identifying studies with HIV impacts on female sex workers.

There remains a limited understanding of the HIV prevention and treatment needs among female sex workers in many parts of the world. Systematic reviews of existing literature can help fill this gap; however, well-done systematic reviews are time-demanding and labor-intensive. Here, we propose an automatic document classification approach to a systematic review to significantly reduce the effort in reviewing documents and optimizing empiric decision making. We first describe a manual document classification procedure that is used to curate a pertinent training dataset and then propose three classifiers: a keyword-guided method, a cluster analysis-based method, and a random forest approach that utilizes a large set of feature tokens. This approach is used to identify documents studying female sex workers that contain content relevant to either HIV or experienced violence. We compare the performance of the three classifiers by cross-validation in terms of area under the curve of the receiver operating characteristic and precision and recall plot, and found random forest approach reduces the amount of manual reading for our example by 80%; in sensitivity analysis, we found that even trained with only 10% of data, the classifier can still avoid reading 75% of future documents (68% of total) while retaining 80% of relevant documents. In sum, the automated procedure of document classification presented here could improve both the precision and efficiency of systematic reviews and facilitate live reviews, where reviews are updated regularly. We expect to obtain a reasonable classifier by taking 20% of retrieved documents as training samples. The proposed classifier could also be used for more meaningfully assembling literature in other research areas and for rapid documents screening with a tight schedule, such as COVID-related work during the crisis.

Psychological Stress Risk Factors, Concerns and Mental Health Support Among Health Care Workers in Vietnam During the Coronavirus Disease 2019 (COVID-19) Outbreak.

Introduction: Coronavirus disease 2019 (COVID-19) has significantly affected health care workers (HCWs), including their mental health. However, there has been limited evidence on this topic in the Vietnamese context. Therefore, this study aimed to explore COVID-19-related, psychological stress risk factors among HCWs, their concerns and demands for mental health support during the pandemic period. Methods: We employed a cross-sectional study design with convenience sampling. An online, self-administered questionnaire was used and distributed through social media among medical and non-medical HCWs from April 22 to May 12, 2020. HCWs were categorized either as frontline or non-frontline. We measured the prevalence of psychological stress using the Impact of Event Scale-Revised (IES-R) instrument. Multivariate binary logistic regression analysis was performed to identify risk factors associated with psychological stress among HCWs. Results: Among the 774 enrolled participants, 761 (98.3%) eligible subjects were included in the analysis. Most respondents were females (58.2%), between 31 and 40 years of age (37.1%), lived in areas where confirmed COVID-19 cases had been reported (61.9%), medical HCWs (59.9%) and practiced being at the frontline (46.3%). The prevalence of stress was 34.3%. We identified significant risk factors such as being frontline HCWs (odds ratio [OR] = 1.77 [95% confidence interval [CI]: 1.17-2.67]), perceiving worse well-being as compared to those before the COVID-19 outbreak [OR = 4.06 (95% CI: 2.15-7.67)], and experiencing chronic diseases [OR = 1.67 (95% CI: (1.01-2.77)]. Majority (73.9%) were concerned about testing positive for COVID-19 and exposing the infection to their families. Web-based psychological interventions that could provide knowledge on managing mental distress and consulting services were highly demanded among HCWs. Conclusion: The prevalence of psychological stress among HCWs in Vietnam during the COVID-19 pandemic was high. There were also significant risk factors associated with it. Psychological interventions involving web-based consulting services are highly recommended to provide mental health support among HCWs.