39 research outputs found
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The authorial delusion: counting lady Macbeth’s children
In 1933, literary critic L. C. Knights published a caustic essay against the notion cultivated by certain of his colleagues, predominantly A. C. Bradley, that Shakespeare is a ‘great creator of characters’. Knights (1973) regarded the examination of isolated particles such as ‘character’ as disorientating, alleging that an analysis of this sort obscures the greater merit of language. Knight’s polemic essentially stands in the threshold of the dissention between formalists and realists: the former consider the examination of the fictional narrative as anything but a textual construct a scholarly faux pas; the latter regard the referential relationship between text and the world as a foundation for the creation of fiction. This is a pseudo-dilemma. The notion that literature is denuded of its artistic merit once it is defined by its constituent artefacts is disorienting, for it completely bypasses the dynamics of its creation. Put differently, a post-event analysis can exist as a standalone act, albeit it cannot challenge or dismiss the foundational principles of the event’s creation process
App-based COVID-19 syndromic surveillance and prediction of hospital admissions in COVID Symptom Study Sweden
The app-based COVID Symptom Study was launched in Sweden in April 2020 to contribute to real-time COVID-19 surveillance. We enrolled 143,531 study participants (≥18 years) who contributed 10.6 million daily symptom reports between April 29, 2020 and February 10, 2021. Here, we include data from 19,161 self-reported PCR tests to create a symptom-based model to estimate the individual probability of symptomatic COVID-19, with an AUC of 0.78 (95% CI 0.74–0.83) in an external dataset. These individual probabilities are employed to estimate daily regional COVID-19 prevalence, which are in turn used together with current hospital data to predict next week COVID-19 hospital admissions. We show that this hospital prediction model demonstrates a lower median absolute percentage error (MdAPE: 25.9%) across the five most populated regions in Sweden during the first pandemic wave than a model based on case notifications (MdAPE: 30.3%). During the second wave, the error rates are similar. When we apply the same model to an English dataset, not including local COVID-19 test data, we observe MdAPEs of 22.3% and 19.0% during the first and second pandemic waves, respectively, highlighting the transferability of the prediction model