Introduction. Effectively modeling SARS-CoV-2/COVID-19 dynamics requires careful integration of population health (public health motivation) and recovery dynamics (medical interventions motivation). This manuscript proposes a minimal pandemic model, which conceptually separates complex adaptive systems (CAS) associated with social behavior and infrastructure (e.g., tractable input events modulating exposure) from idealized bio-CAS (e.g., the immune system). The proposed model structure extends

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... he classic simple SEIR (susceptible, exposed, infected, resistant/recovered) uni-causal compartmental model, widely used in epidemiology, into an 8th-order functional network SEI3R2S-Nrec model structure, with infection partitioned into three severity states (e.g., starts in I1, mostly asymptomatic, then I2 if notable symptoms, then I3 if should be hospitalized) connecting via a lattice of paths to two flux-partitioned resistant (R) states. Here Nrec (not recovered) represents a placeholder for better tying emerging SARS-CoV-2/COVID-19 medical research findings with those from epidemiology. This is viewed as the minimal model that could be applicable to both a population (public health motivation) and to recovery dynamics (medical interventions motivation). Methods. Borrowing from fuzzy logic, a given model represents a Universe of Discourse (UoD) that is based on assumptions. Nonlinear flux rates are implemented using the classic Hill function, widely used in the biochemical and pharmaceutical fields and intuitive for inclusion within differential equations. There is support for encounter input events that modulate ongoing E (exposures) fluxes via S>I1 and other I1/2/3 encounters, partitioned into a social/group (uSG(t)) behavioral subgroup (e.g., ideally using evolving science best-practices), and a smaller uTB(t) subgroup with added spreader lifestyle and event support. In addition to signal and flux trajectories (e.g., plotted over 300 days), key cumulative output metrics are calculated (e.g., for fluxes such as I3>D deaths, I1>I2 cases and I2>I3 hospital admittances). The model is available for use; an interactive web-based version will follow. Results. Default population results are provided for the USA as a whole, for three states in which this author has lived (Arizona, Wisconsin, Oregon), and for several special hypothetical cases of idealized UoDs (e.g., nursing home; healthy low-risk mostly on I1>R1 path, demonstrating reinfections). Often known events were included (e.g., pulses for holiday weekends and election; Trump/governor-inspired summer outbreak in Arizona). Runs were mildly tuned by the author, in two stages: i) mild model-tuning (e.g., for obesity), then ii) iterative input tuning to obtain similar overall March-thru-November curve shapes and appropriate cumulative numbers for cases (lower than I1>I2 flux), hospitalizations (~I3) and deaths (I3>D flux). Both curve shapes and cumulative metrics are consistent with available data and could be further refined by groups with more resources (human, computational, data access). It is hoped that its causal predictions might prove helpful, with the starter models offered to policymakers, medical professionals, and on the ground managers of science-based interventions. Discussion and Future Directions. These include: i) the sensitivity of the model and inputs; ii) possible next steps for this SEI3R2S-Nrec framework that may include treating key compartments as dynamic sub-model clusters, to better address compartment-specific forms of population diversity (an extension that bears similarity to how biochemical reaction dynamics can occur within compartments, here for E [host-parasite biophysics], I [infection diversity], and/or R [immune diversity]); iii) the models potential utility as a framework for applying optimal/feedback control engineering to help manage the ongoing pandemic response in the context of competing subcriteria that must evolve with new tools (e.g., more testing, vaccines with temporary immunity); and iv) ways in which the Nrec medical submodel could be expanded to provide refined estimates of the types of tissue damage, impairments and dysfunction that are known byproducts of the COVID-19 disease process.