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This paper presents a new mathematical feedback model to demonstrate how direct observations of the epidemiological compartments of population could be mapped to inputs, such that the social spread of the disease is asymptotically subdued. Details of the stabilization and robustness are included. This is a pivotal restructuring of modelling the control of corona virus from the current models in use world-wide which do not utilize feedback of functions of epidemiological compartments of population to construct the inputs. Although several vaccines have received Emergency Use Authorization (EUA) massive vaccination would take several years to reach herd immunity in most countries. Furthermore, the period of efficacy of the vaccination may be approximately one year only resulting in an unending vaccination. Even during the vaccination, there would be an urgent need to control the spread of the virus. When herd immunity is reached and vaccination is discontinued, there would be new surges of the disease. These surges of disease are not possible in appropriately designed stable feedback models. However, extensive testing, contact tracing, and medical treatment of those found infected, must be maintained.

This paper presents a pivotal restructuring of modeling the control of COVID-19 even when massive vaccination is in progress. A new closed loop mathematical model to demonstrate how direct observations of the epidemiological compartments of population could be mapped to inputs, such that the social spread of the disease is asymptotically subdued. Mathematical details of the stabilization and robustness are included. A new engineered closed loop model is designed to control the spread of COVID-19 or its variants—that is, one input directly increases the time-rate of the compartment of population free of virus, and the other input directly changes the time-rate of the susceptible compartment of population. Both inputs have collateral opposite influences on the time-rate of the infected compartment of population. The loop is closed around the new input-output model and designed so that the outputs reach the desired asymptotes. New surges of disease spread are not possible in appropriately designed stable closed loop models. However, extensive testing, contact tracing, and medical treatment of those found infected, must be maintained.

This paper presents a new mathematical feedback model to demonstrate how direct observations of the epidemiological compartments of population could be mapped to inputs, such that the social spread of the disease is asymptotically subdued. Details of the stabilization and robustness are included. This is a pivotal restructuring of modelling the control of corona virus from the current models in use world-wide which do not utilize feedback of functions of epidemiological compartments of population to construct the inputs. Although several vaccines have received Emergency Use Authorization (EUA) massive vaccination would take several years to reach herd immunity in most countries. Furthermore, the period of efficacy of the vaccination may be approximately one year only resulting in an unending vaccination. Even during the vaccination, there would be an urgent need to control the spread of the virus. When herd immunity is reached and vaccination is discontinued, there would be new surges of the disease. These surges of disease are not possible in appropriately designed stable feedback models. However, extensive testing, contact tracing, and medical treatment of those found infected, must be maintained.