Validation of a risk prediction model for COVID-19: the PERIL prospective cohort study.

Aim: This study aims to perform an external validation of a recently developed prognostic model for early prediction of the risk of progression to severe COVID-19. Patients & methods/materials: Patients were recruited at their initial diagnosis at two facilities within Hamad Medical Corporation in Qatar. 356 adults were included for analysis. Predictors for progression of COVID-19 were all measured at disease onset and first contact with the health system. Results: The C statistic was 83% (95% CI: 78%-87%) and the calibration plot showed that the model was well-calibrated. Conclusion: The published prognostic model for the progression of COVID-19 infection showed satisfactory discrimination and calibration and the model is easy to apply in clinical practice.d.

A qualitative assessment of medical students' readiness for virtual clerkships at a Qatari university during the COVID-19 pandemic.

BACKGROUND: This study aims to qualitatively examine the readiness of medical students to change to virtual clerkship (VC) during the pandemic, from both the faculty and students' perspectives. METHODS: A qualitative study was conducted based on the framework of readiness to change. Focus group discussions with students, and semi-structured interviews with clinical faculty members were done using appropriate online platforms. Transcripts were then analyzed using inductive-deductive approach. RESULTS: Twelve themes emerged which are (1) Perceptions about the university's decision and its communication to students, (2) A Perceived lack of clinical experience, (3) Students' role as members of the medical team facing the pandemic, (4) Student safety, (5) Quality and design of VC and the skills it offered, (6) Belief in own ability to succeed in the VC, (7) Confidence that VC would reach its goals, (8) New enhanced learning approaches, (9) Preparing students for new types of practice in the future (10) Acquired skills, 11) Academic support and communication with faculty and college, and 12) Psychological support. Medical students showed limited readiness to undertake a virtual clerkship and not play their role as healthcare professionals during the pandemic. They perceived a huge gap in gaining clinical skills virtually and asked for a quick return to training sites. CONCLUSION: Medical students were not ready for virtual clerkships. There will be a need to integrate novel learning modalities such as patient simulations and case-based learning in order to meet future demands of the medical profession and enhance the efficiency of virtual clerkships.

Mathematical modeling of the SARS-CoV-2 epidemic in Qatar and its impact on the national response to COVID-19.

BACKGROUND: Mathematical modeling constitutes an important tool for planning robust responses to epidemics. This study was conducted to guide the Qatari national response to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic. The study investigated the epidemic's time-course, forecasted health care needs, predicted the impact of social and physical distancing restrictions, and rationalized and justified easing of restrictions. METHODS: An age-structured deterministic model was constructed to describe SARS-CoV-2 transmission dynamics and disease progression throughout the population. RESULTS: The enforced social and physical distancing interventions flattened the epidemic curve, reducing the peaks for incidence, prevalence, acute-care hospitalization, and intensive care unit (ICU) hospitalizations by 87%, 86%, 76%, and 78%, respectively. The daily number of new infections was predicted to peak at 12 750 on May 23, and active-infection prevalence was predicted to peak at 3.2% on May 25. Daily acute-care and ICU-care hospital admissions and occupancy were forecast accurately and precisely. By October 15, 2020, the basic reproduction number R0 had varied between 1.07-2.78, and 50.8% of the population were estimated to have been infected (1.43 million infections). The proportion of actual infections diagnosed was estimated at 11.6%. Applying the concept of Rt tuning, gradual easing of restrictions was rationalized and justified to start on June 15, 2020, when Rt declined to 0.7, to buffer the increased interpersonal contact with easing of restrictions and to minimize the risk of a second wave. No second wave has materialized as of October 15, 2020, five months after the epidemic peak. CONCLUSIONS: Use of modeling and forecasting to guide the national response proved to be a successful strategy, reducing the toll of the epidemic to a manageable level for the health care system.