Abstract
Complex systems are inherently multilevel and multiscale systems. The infectious disease system is considered a complex system resulting from the interaction between three sub-systems (host, pathogen, and environment) organized into a hierarchical structure, ranging from the cellular to the macro-ecosystem level, with multiscales. Therefore, to describe infectious disease phenomena that change through time and space and at different scales, we built a model framework where infectious disease must be considered the set of biological responses of human hosts to pathogens, with biological pathways shared with other pathologies in an ecological interaction context. In this paper, we aimed to design a framework for building a disease model for COVID-19 based on current literature evidence. The model was set up by identifying the molecular pathophysiology related to the COVID-19 phenotypes, collecting the mechanistic knowledge scattered across scientific literature and bioinformatic databases, and integrating it using a logical/conceptual model systems biology. The model framework building process began from the results of a domain-based literature review regarding a multiomics approach to COVID-19. This evidence allowed us to define a framework of COVID-19 conceptual model and to report all concepts in a multilevel and multiscale structure. The same interdisciplinary working groups that carried out the scoping review were involved. The conclusive result is a conceptual method to design multiscale models of infectious diseases. The methodology, applied in this paper, is a set of partially ordered research and development activities that result in a COVID-19 multiscale model.
Highlights
To construct a large-scale comprehensive view of biological systems, several smaller models may need to be integrated
We performed the modular design of the COVID-19 model framework, biologically based
The working group that previously carried out the COVID-19 domain-based scoping review translated the results of this review into modular logic of this model framework, as below reported:
Summary
To construct a large-scale comprehensive view of biological systems, several smaller models may need to be integrated. This can be difficult to accomplish, as models can exhibit significant inner variation due to the different expertise of modelers or perspectives of model. During the ongoing COVID-19 pandemic, the scientific community consider computer modeling as a possible solving factor given the enormous uncertainty about the evolution of the pandemic. In this context, epidemiological models can be critical planning tools for policymakers, clinicians, and public health practitioners [1].
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