No abstract

In this work we observe a number of cases supporting the possible correlation between the administration of BCG tuberculosis vaccine and the severity of Covid-19 effects in the population proposed in the earlier works [1]. Based on the early preliminary analysis of the publicly available data we propose a number of arguments and observations providing further support for the correlation hypothesis and make an observation that the effectiveness of the protection effect of BCG immunization, if confirmed, may depend to a significant extent on the age of administration, with the early age inoculation more effective for the lasting protection.

We present an updated time-adjusted dataset and conclusions at Covid19 Time Zero + 5 month (04.06.2020). The conclusions of the original analysis reviewed and mostly maintained at this time point. With the data accumulated to date a statistical significance of the BCG immunization correlation hypothesis is evaluated with the conclusion that it has achieved the level of confidence. Several specific cases are discussed with respect to the induced immunity hypothesis.

No abstract

In this work we observe a number of cases supporting the possible correlation between the administration of BCG tuberculosis vaccine and the severity of Covid-19 effects in the population proposed in the earlier works [1]. Based on the early preliminary analysis of the publicly available data we propose a number of arguments and observations providing further support for the correlation hypothesis and make an observation that the effectiveness of the protection effect of BCG immunization, if confirmed, may depend to a significant extent on the age of administration, with the early age inoculation more effective for the lasting protection.

In this study we investigate information processing in deep neural network models. We demonstrate that unsupervised training of autoencoder models of certain class can result in emergence of compact and structured internal representation of the input data space that can be correlated with higher level categories. We propose and demonstrate practical possibility to detect and measure this emergent information structure by applying unsupervised clustering in the activation space of the focal hidden layer of the model. Based on our findings we propose a new approach to training neural network models based on emergent in unsupervised training information landscape, that is iterative, driven by the environment, requires minimal supervision and with intriguing similarities to learning of biologic systems. We demonstrate its viability with originally developed method of spontaneous concept learning that yields good classification results while learning new higher level concepts with very small amounts of supervised training data.

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Based on a subset of Covid-19 Wave 1 cases at a time point near TZ+3m (April, 2020), we perform an analysis of the influencing factors for the epidemics impacts with several different statistical methods. The consistent conclusion of the analysis with the available data is that apart from the policy and management quality, being the dominant factor, the most influential factors among the considered were current or recent universal BCG immunization and the prevalence of smoking.