This article pursues a new approach to the design of feedback control laws mitigating the spread of human infectious diseases. The control inputs of vaccination, isolation, and outing/gathering regulation are derived as the gradient of a positive definite function of all the population variables of the SIQR model. Lyapunov functions in which susceptible and infected populations are decoupled are known to be non-self-sufficient for proving stability of the SIQR model as well as other variants of the SIR model. This fact has been hampering Lyapunov-based control design of infectious disease models. This article demonstrates that a popular decoupled function can still serve as a control Lyapunov function as long as isolation is made state-dependent. Techniques realizing this novel idea are presented and shown to allow the Lyapunov function to establish a robustness guarantee in terms of input-to-state stability with respect to immigration and newborn perturbation on an arbitrarily large domain. The effectiveness of the proposed simultaneous controller is illustrated through numerical simulations performed with a parameter set of COVID-19.