COVID-19 and Kawasaki Like Disease: The Known-Known, the Unknown-Known and the Unknown-Unknown

In the end of April nearly 100 cases of children aged between 6 month and 9 years with Kawasaki like disease have been reported (mostly in Europe) probably linked to COVID-19. We aim to sum up the known data about this new entity based on published data (in a case report, a serie of 8 cases and in newspapers and society statement) and using our knowledge of classical Kawasaki disease. It seems to be a post infectious disease with an onset between 2-4 weeks after the infection, probably in genetically predisposed children aged between 6 month to 17 years. A very rough estimation of incidence based on data from Bergamo, Italy, and New York State and a lot assumption is between 0.011% (95% CI:0.009-0.014%) - 0.31% (95% CI: 0.2-0.47%) of infected children. Clinical signs overlaps with Kawasaki disease in some children, but another feature is prominent gastrointestinal manifestations. For the 9 detailed patients most had incomplete presentation for Kawasaki disease (with a mean 1.7 (+/-1.2) criteria per patient for the 5 non fever criterion) and only one had a classical form. In some cases, presentation is closer to toxic shock syndrome or isolated myocarditis. Persistent fever seems to be constant and biological exploration are consistent with inflammation (elevated CRP, ferritin and D-Dimers). Management is described as supportive and children seem to improve rapidly, but can require cardiac or respiratory support. To date there is one death. Paediatricians and general practitioners need to be aware of these possible evolution following COVID-19 infection. However it seems to be rare and children are probably still spared from most morbidities and mortality linked to COVID-19 infection .There are need of published detailed cohorts to better delineate these entities.

Could the unknown unknown be good or bad news? The role of valence in decision making under ambiguity

A number of self-serving biases have recently been explained by asymmetric belief updating under risk which asserts that humans are quick to learn from positive but not negative information. However, risky decisions in real life are often made under ambiguity where only partial information is available about distribution of risks. We demonstrate that under ambiguity, belief updating is not asymmetric but a flexible process of skepticism towards the valence of partially observable facts. When ambiguity size was tractable, belief updating was sensitive to valence: if the information was promising, ambiguity attitude decreased, skeptically balancing the promising prospects of available evidence against the hazards of what might be hidden from the view. Conversely, when the information was disappointing, attitude toward ambiguity increased, cautiously encouraging the participant to be more adventurous than what the available information guaranteed. These results go contradict the predictions from optimistic learning under risk and suggest that belief updating is sensitive to the state of our knowledge and ignorance.

Identification and characterization of "Black Swans" in technological events in Brazil

“Black swan” events represent a critical issue in risk analysis. Events with extremely low probability of occurrence are in general discarded from the risk analysis process. This paper aims to identify and characterize four accidents that occurred in Brazil into the following classes: “not a black swan”, “black swan: unknown-unknown”, “black swan: unknown-known” and “black Swan: not believed to occur”, by obtaining from experts the distribution of belief for the real probability of each class. Results showed that, throughout all cases analyzed, the class “black swan: unknown-unknown” was never reported, which means that none of the cases studied were a complete surprise to anyone. The method used was able to assign all accident events to the remaining classes. Probability distribution elicited from experts showed large disagreement among them, and the expected value was considered low. Nevertheless, the elicited distributions can be utilized in future risk analysis as a priori distribution in a Bayesian approach.

Making an unknown unknown a known unknown: Missing data in longitudinal neuroimaging studies

The analysis of longitudinal neuroimaging data within the massively univariate framework provides the opportunity to study empirical questions about neurodevelopment. Missing outcome data are an all-too-common feature of any longitudinal study, a feature that, if handled improperly, can reduce statistical power and lead to biased parameter estimates. The goal of this paper is to provide conceptual clarity of the issues and non-issues that arise from analyzing incomplete data in longitudinal studies with particular focus on neuroimaging data. This paper begins with a review of the hierarchy of missing data mechanisms and their relationship to likelihood-based methods, a review that is necessary not just for likelihood-based methods, but also for multiple-imputation methods. Next, the paper provides a series of simulation studies with designs common in longitudinal neuroimaging studies to help illustrate missing data concepts regardless of interpretation. Finally, two applied examples are used to demonstrate the sensitivity of inferences under different missing data assumptions and how this may change the substantive interpretation. The paper concludes with a set of guidelines for analyzing incomplete longitudinal data that can improve the validity of research findings in developmental neuroimaging research.