Authors:
Vladimir Estivill-Castro
1
;
Enrique Hernández-Jiménez
2
;
3
and
David F. Nettleton
1
Affiliations:
1
Department of Information and Communications Technology (DTIC), Universitat Pompeu Fabra, Spain
;
2
Institut d’Investigació Biomèdica de Bellvitge, Barcelona, Spain
;
3
Loop Diagnostics S.L., Barcelona, Spain
Keyword(s):
Simulation of Biological Systems, Case Studies, Immune System Response, Model Construction, Rule-induction, Machine Learning.
Abstract:
The exceptionally high virulence of COVID-19 and the patients’ precondition seem to constitute primary factors in how pro-inflammatory cytokines production evolves during the course of an infection. We present a System Dynamics Model approach for simulating the patient reaction using two key control parameters (i) virulence, which can be “moderate” or “high” and (ii) patient precondition, which can be “healthy”, “not so healthy” or “serious preconditions”. In particular, we study the behaviour of Inflammatory (M1) Alveolar Macrophages, IL6 and Active Adaptive Immune system as indicators of the immune system response, together with the COVID viral load over time. The results show that it is possible to build an initial model of the system to explore the behaviour of the key attributes involved in the patient condition, virulence and response. The model suggests aspects that need further study so that it can then assist in choosing the correct immunomodulatory treatment, for instance t
he regime of application of an Interleukin 6 (IL-6) inhibitor (tocilizumab) that corresponds to the projected immune status of the patients. We introduce machine learning techniques to corroborate aspects of the model and propose that a dynamic model and machine learning techniques could provide a decision support tool to ICU physicians.
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