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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) detection platforms currently report qualitative results. However, technology based on RT-PCR allows for calculation of viral load, which is associated with transmission risk and disease severity in other viral illnesses.1 Viral load in COVID-19 might correlate with infectivity, disease phenotype, morbidity, and mortality. To date, no studies have assessed the association between viral load and mortality in a large patient cohort.2, 3, 4 To our knowledge, we are the first to report on SARS-CoV-2 viral load at diagnosis as an independent predictor of mortality in a large hospitalised cohort (n=1145).

"As readers know, I'm very dubious about assigning agency to generational cohorts like "Millennials," or "Boomers." Where, after all, are their offices on K Street? And if the Powers That Be need to find enough "Millennials" willing to help them continue their project of world domination, that they will do. However, it does seem that the the series of military debacles following the invasion of Iraq has dented our sense of American Exceptionalism, and has begun to shape public opinion - for the better, so far as I am concerned. A cautionary note would be "Millennial" support for globalization (which has, after all, brought us the iPhone, along with the figure of the selfie-taking backpacker). If I were a nimble 1%-er, I'd be perfectly happy to discard American Exceptionalism as an ideology, as long as I could control the global supply chain (and had a high-altitude, remote pied-a-terre from which to manage the free movement of my capital)."

The analysis of questionnaires often involves representing the high-dimensional responses in a low-dimensional space (e.g., PCA, MCA, or t-SNE). However questionnaire data often contains categorical variables and common statistical model assumptions rarely hold. Here we present a non-parametric approach based on Fisher Information which obtains a low-dimensional embedding of a statistical manifold (SM). The SM has deep connections with parametric statistical models and the theory of phase transitions in statistical physics. Firstly we simulate questionnaire responses based on a non-linear SM and validate our method compared to other methods. Secondly we apply our method to two empirical datasets containing largely categorical variables: an anthropological survey of rice farmers in Bali and a cohort study on health inequality in Amsterdam. Compare to previous analysis and known anthropological knowledge we conclude that our method best discriminates between different behaviours, paving the way to dimension reduction as effective as for continuous data.