We present the hypothesis to the scientific community actively designing clinical trials and recommending public health guidelines to control the pandemic that – “Tetanus vaccination may be contributing to reduced severity of the COVID-19 infection” – and urge further research to validate or invalidate the effectiveness of the tetanus toxoid vaccine against COVID-19. This hypothesis was revealed by an explainable artificial intelligence system unleashed on open public biomedical datasets. As a foundation for scientific rigor, we describe the data and the artificial intelligence system, document the provenance and methodology used to derive the hypothesis and also gather potentially relevant data/evidence from recent studies. We conclude that while correlations may not be reason for causation, correlations from multiple sources is more than a serendipitous co-incidence that is worthy of further and deeper investigation.
In this paper, we describe a Python-based framework for the rapid prototyping of scientific applications. A case study was performed using a problem specification developed for Marmot, a project at the Los Alamos National Laboratory aimed at re-factoring standard physics codes into reusable and extensible components. Components were written in Python, ZPL, Fortran, and C++ following the Marmot component design. We evaluate our solution both qualitatively and quantitatively by comparing it to a single-language version written in C.
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