Infectious diseases like COVID-19 have remained a primary public and global health concern. Internet of Things (IoT) of networked robots and physiological intervention can be combined to identify and control the spread of the different variants of COVID-19 disease. With this approach, governments and healthcare institutions can plan for such diseases in the future. This paper presents a compact computational model (CCM) to identify and control different COVID-19 variants using IoT-networked robots. The CCM comprises seven physiological variables (PV) and robotic identification (RI) of infected individuals as alternative intervention strategies. The study uses Market Place Service Robots that correctly identify PV and RI for positively infected individuals. The conditions of the existence and the solution of the deterministic model are derived from a compact flow architecture that we develop. We show that the model has COVID-19-free equilibrium and endemic equilibrium. While PV with appropriate isolation and hospital treatment reduces the COVID-19 disease impact by 19% more than RI alone, the study also shows that combining two PV with RI minimises the impact better than PV or RI alone, by 36% and 43%, respectively. When the PV control parameters are increased, up to five, in the presence of IoT and RI, up to 99.99% improvement is seen. With all seven PV control parameters in the presence of IoT and RI, the proposed CCM guarantees an infection-free population.