Modeling the heterogeneity in COVID-19's reproductive number and its impact on predictive scenarios.

The correct evaluation of the reproductive number R for COVID-19 is central in the quantification of the potential scope of the pandemic and the selection of an appropriate course of action. In most models, R is modeled as a constant - effectively averaging out the inherent variability of the transmission process due to varying individual contact rates, population densities, or temporal factors amongst many. Yet, due to the exponential nature of epidemic growth, the error due to this simplification can be rapidly amplified, and its extent remains unknown. How can this intrinsic variability be percolated into epidemic models, and its impact, better quantified? We study this question here through a Bayesian perspective that captures at scale the heterogeneity of a population and environmental conditions, creating a bridge between the traditional agent-based and compartmental approaches. We use our model to simulate the spread as well as the impact of different social distancing strategies on real COVID-19 data, and highlight the significant impact of the heterogeneity. We emphasize that the contribution of this paper focuses on discussing the importance of the impact of R's heterogeneity on uncertainty quantification from a statistical viewpoint, rather than developing new predictive models.

Deep generative modeling for volume reconstruction in cryo-electron microscopy.

Advances in cryo-electron microscopy (cryo-EM) for high-resolution imaging of biomolecules in solution have provided new challenges and opportunities for algorithm development for 3D reconstruction. Next-generation volume reconstruction algorithms that combine generative modelling with end-to-end unsupervised deep learning techniques have shown promise, but many technical and theoretical hurdles remain, especially when applied to experimental cryo-EM images. In light of the proliferation of such methods, we propose here a critical review of recent advances in the field of deep generative modelling for cryo-EM reconstruction. The present review aims to (i) provide a unified statistical framework using terminology familiar to machine learning researchers with no specific background in cryo-EM, (ii) review the current methods in this framework, and (iii) outline outstanding bottlenecks and avenues for improvements in the field.

Similarities in extracorporeal membrane oxygenation management across intensive care unit types in the United States: An analysis of the Extracorporeal Life Support Organization Registry.

BACKGROUND: Extracorporeal membrane oxygenation (ECMO) use in the United States occurs often in cardiothoracic ICUs (CTICU). It is unknown how it varies across ICU types. METHODS: We identified 10 893 ECMO runs from the Extracorporeal Life Support Organization (ELSO) Registry across 2018 and 2019. Primary outcome was ECMO case volume by ICU type (CTICU vs. non-CTICU). Adjusting for pre-ECMO characteristics and case mix, secondary outcomes were on-ECMO physiologic variables by ICU location stratified by support type. RESULTS: CTICU ECMO occurred in 65.1% and 55.1% (2018 and 2019) of total runs. A minority of total runs related to cardiac surgery procedures (CTICU: 21.7% [2018], 18% [2019]; non-CTICU: 11.2% [2018], 13% [2019]). After multivariate adjustment, non-CTICU ECMO for cardiac support associated with lower 4- and 24-h circuit flow (3.9 liters per minute [LPM] vs. 4.1 LPM, p < 0.0001; 4.1 LPM vs. 4.3 LPM, p < 0.0001); for respiratory support, lower on-ECMO mean fraction of inspired oxygen ([Fi O2 ], 67% vs. 69%, p = 0.02) and lower respiratory rate (14 vs. 15, p < 0.0001); and, for extracorporeal cardiopulmonary resuscitation (ECPR), lower ECMO flow rates at 24 h (3.5 LPM vs. 3.7 LPM, p = 0.01). CONCLUSIONS: ECMO mostly remains in CTICUs though a minority is associated with cardiac surgery. Statistically significant but clinically minor differences in on-ECMO metrics were observed across ICU types.

Predicting COVID-19 Transmission to Inform the Management of Mass Events: Model-Based Approach.

BACKGROUND: Modelling COVID-19 transmission at live events and public gatherings is essential to controlling the probability of subsequent outbreaks and communicating to participants their personalized risk. Yet, despite the fast-growing body of literature on COVID-19 transmission dynamics, current risk models either neglect contextual information including vaccination rates or disease prevalence or do not attempt to quantitatively model transmission. OBJECTIVE: This paper attempted to bridge this gap by providing informative risk metrics for live public events, along with a measure of their uncertainty. METHODS: Building upon existing models, our approach ties together 3 main components: (1) reliable modelling of the number of infectious cases at the time of the event, (2) evaluation of the efficiency of pre-event screening, and (3) modelling of the event's transmission dynamics and their uncertainty using Monte Carlo simulations. RESULTS: We illustrated the application of our pipeline for a concert at the Royal Albert Hall and highlighted the risk's dependency on factors such as prevalence, mask wearing, and event duration. We demonstrate how this event held on 3 different dates (August 20, 2020; January 20, 2021; and March 20, 2021) would likely lead to transmission events that are similar to community transmission rates (0.06 vs 0.07, 2.38 vs 2.39, and 0.67 vs 0.60, respectively). However, differences between event and background transmissions substantially widened in the upper tails of the distribution of the number of infections (as denoted by their respective 99th quantiles: 1 vs 1, 19 vs 8, and 6 vs 3, respectively, for our 3 dates), further demonstrating that sole reliance on vaccination and antigen testing to gain entry would likely significantly underestimate the tail risk of the event. CONCLUSIONS: Despite the unknowns surrounding COVID-19 transmission, our estimation pipeline opens the discussion on contextualized risk assessment by combining the best tools at hand to assess the order of magnitude of the risk. Our model can be applied to any future event and is presented in a user-friendly RShiny interface. Finally, we discussed our model's limitations as well as avenues for model evaluation and improvement.

Variability in the analysis of a single neuroimaging dataset by many teams.

Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses1. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset2-5. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed.