Use of community SARS-CoV2 case counts and instantaneous reproductive number to predict short-term COVID-19 hospital admission volumes.

The SARS-CoV2 pandemic and high hospitalization rates placed a tremendous strain on hospital resources necessitating models to predict hospital volumes and the associated resource requirements. Complex epidemiologic models have been developed and published, but many require continued adjustment of input parameters. We developed a simplified model for short-term bed need predictions that self-adjusts to changing patterns of disease in the community and admission rates. The model utilizes public health data on community new case counts for SARS-CoV2 and projects anticipated hospitalization rates. The model was retrospectively evaluated after the second wave of SARS-CoV2 2 in New York (October 2020-April 2021) for its accuracy in predicting number of COVID-19 admissions at three, five, seven and 10 days into the future comparing predicted admissions with actual admissions for each day at a large integrated healthcare delivery network. Mean absolute percent error of the model was found to be low when evaluated across the entire health system, for a single region of the health system or for a single large hospital (6.1%-7.6% for 3-day predictions, 9.2%-10.4% for five-day predictions, 12.4%-13.2% for seven-day predictions, and 17.1-17.8% for 10-day predictions).

Automated Assessment in Programming Courses: A Case Study during the COVID-19 Era

The COVID-19 pandemic imposed in many countries, in the short term, the interruption of face-to-face teaching activities and, in the medium term, the existence of a "new normal", in which teaching methods should be able to switch from face-to-face to remote overnight. However, this flexibility can pose a great difficulty, especially in the assessment of practical courses with a high student-teacher ratio, in which the assessment tools or methods used in face-to-face learning are not ready to be adopted within a fully online environment. This article presents a case study describing the transformation of the assessment method of a programming course in higher education to a fully online format during the COVID-19 pandemic, by means of an automated student-centered assessment tool. To evaluate the new assessment method, we studied students"interactions with the tool, as well as students"perceptions, which were measured with two different surveys: one for the programming assignments and one for the final exam. The results show that the students"perceptions of the assessment tool were highly positive: if using the tool had been optional, the majority of them would have chosen to use it without a doubt, and they would like other courses to involve a tool like the one presented in this article. A discussion about the use of this tool in subsequent years in the same and related courses is also presented, analyzing the sustainability of this new assessment method.