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Systems Biology aims to understand biological emergence from the interactions of biomolecules, e.g. by integrating the knowledge about these interactions into a computer model and thereby reconstructing biological behavior in silico.   On this level, personalized physiological behavior (e.g. Long Covid) emerges from interactions between biomolecules (virus and immune cells), like in many other systems biological models.

However, when talking about epidemics, there is also a second dimension of emergence.  Interactions between susceptible, recovered, immune, or infected individuals lead to the emergence of epidemics. The chance of an individual being infected is now state-dependent on the spread of the virus in the population.

Moreover, there is also a third, level of emergence, where interactions between various players (researcher, clinical data manager, medical doctors, etc) collecting and processing different pieces of data lead to the emergence of the project. The scientific concept is evolving during the study and thus the project itself is a Complex Adaptive System. The data collection process becomes state-dependent and data becomes live in a sense that they change with the evolution of the whole system. So, data should become not only Findable, Accessible, Interoperable, and Reusable (FAIR) but also Emergable (FAIRE).

In the presentation, I will analyze the lessons from our COVID-19 modeling, where the model of epidemics was performed along with COVID–19–related clinical data management in the ORCHESTRA EU Horizon 2020 project .

Based on the System's Biological COVID modeling and ORCHESTRA clinical data management, I will present the next steps, which could facilitate our preparations for the next epidemics.