Application of probabilistic models for extreme values to the COVID-2019 epidemic daily dataset.
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ABSTRACT:
SUBMITTER: Canton Enriquez D
PROVIDER: S-EPMC8719919 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Background and objectivesCoronavirus disease 2019 (COVID-19) is currently raging all over the world. COVID-19 can be transmitted among humans. To control the spread of the epidemic and meet the learning needs of students, Chinese universities have used a variety of multimedia forms to conduct online teaching. However, the influence of different teaching methods on the learning effects of nursing students has not been conclusive, and few studies have directly compared the differences in the effects of different teaching methods. Therefore, it is necessary to evaluate the impact of different teaching methods on students' learning effects.MethodsA total of 119 participants from the graduating class of 2022 performed 4 validated classes (fundamental nursing, medical nursing, surgical nursing, and paediatric nursing) through network teaching. A total of 126 participants from the graduating class of 2021 used the traditional teaching method and were enrolled as a control group. All participants completed a questionnaire, which was designed by the school for teaching evaluation, and took a test after the courses.ResultsA total of 245 nursing undergraduates were ultimately enrolled. The analysis of the academic performance and the student evaluations from the four courses showed no significant difference between the network teaching group and the traditional teaching group for nursing undergraduates (all P > 0.05).ConclusionThrough the convenience of network teaching, teachers and students can choose the time and place of both their teaching and learning independently. Moreover, such use effectively prevents the spread of the epidemic. After the epidemic concludes, the continued use of the network teaching method can help improve teaching efficiency by being combined with the traditional teaching method.
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Project description:BackgroundVarious mathematical models were published to predict the epidemiological consequences of the COVID-19 pandemic. This systematic review has studied the initial epidemiological models.MethodsArticles published from January to June 2020 were extracted from databases using search strings and those peer-reviewed with full text in English were included in the study. They were analysed as to whether they made definite predictions in terms of time and numbers, or contained only mathematical assumptions and open-ended predictions. Factors such as early vs. late prediction models, long-term vs. curve-fitting models and comparisons based on modelling techniques were analysed in detail.ResultsAmong 56,922 hits in 05 databases, screening yielded 434 abstracts, of which 72 articles were included. Predictive models comprised over 70% (51/72) of the articles, with susceptible, exposed, infectious and recovered (SEIR) being the commonest type (mean duration of prediction being 3 months). Common predictions were regarding cumulative cases (44/72, 61.1%), time to reach total numbers (41/72, 56.9%), peak numbers (22/72, 30.5%), time to peak (24/72, 33.3%), hospital utilisation (7/72, 9.7%) and effect of lockdown and NPIs (50/72, 69.4%). The commonest countries for which models were predicted were China followed by USA, South Korea, Japan and India. Models were published by various professionals including Engineers (12.5%), Mathematicians (9.7%), Epidemiologists (11.1%) and Physicians (9.7%) with a third (32.9%) being the result of collaborative efforts between two or more professions.ConclusionThere was a wide diversity in the type of models, duration of prediction and the variable that they predicted, with SEIR model being the commonest type.
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Project description: Not available
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