Toward an observatory of the evolution of clinical trials through phylomemy reconstruction: the COVID-19 vaccines example.

OBJECTIVES: To visualize the evolution of all registered COVID-19 vaccine trials. STUDY DESIGN AND SETTING: As part of the living mapping of the COVID-NMA initiative, we identify biweekly all COVID-19 vaccine trials and automatically extract data from the EU clinical trials registry, ClinicalTrials.gov, IRCT and the World Health Organization International Clinical Trials Registry Platform. Data are curated and enriched by epidemiologists. We have used the phylomemy reconstruction process to visualize the temporal evolution of COVID-19 vaccines trials descriptions. We have analyzed the textual contents of 1,794 trials descriptions (last search in October 2021) and explored their collective structure along with their semantic dynamics. RESULTS: The structures highlighted by the phylomemy reconstruction processes synthesize the complexity of the knowledge produced by the research community. The reconstructed phylomemy clearly retrieves the five major COVID-19 vaccine platforms in the form of complete branches. The branches interactions reflect the exploration of a new approach to vaccine implementation moving from homologous prime vaccination to heterologous prime vaccination. Phylomemies also clearly identifies shifts in research questions, from vaccine efficacy to booster efficacy. CONCLUSION: This new method provides important insights for the global coordination between research teams especially in crisis situations such as the COVID-19 pandemic.

Exploring, browsing and interacting with multi-level and multi-scale dynamics of knowledge

The ICT revolution has given birth to a world of digital traces. A wide number of knowledge-driven domains like science are daily fueled by unlimited flows of textual contents. In order to navigate across these growing constellations of words, interdisciplinary innovations are emerging at the crossroad between social and computational sciences. In particular, complex systems approaches make it now possible to reconstruct multi-level and multi-scale dynamics of knowledge by means of inheritance networks of elements of knowledge called phylomemies. In this article, we will introduce an endogenous way to visualize the multi-level and multi-scale properties of phylomemies. The resulting system will enrich a state-of-the-art tree like representation with the possibility to browse through the evolution of a corpus of documents at different level of observation, to interact with various scales of description, to reconstruct a hierarchical clustering of elements of knowledge and to navigate across complex semantic lineages. We will then formalize a generic macro-to-micro methodology of exploration and implement our system as a free software called the Memiescape. Our system will be illustrated by three use cases that will respectively reconstruct the scientific landscape of the top cited publications of the French CNRS, the evolution of the state of the art of knowledge dynamics visualization and the ongoing discovery process of Covid-19 vaccines.