RoboFTOCM: a smart robotic framework for pandemic management in human populations

Abstract

As the human population grows, innovations such as virtual patients, vaccines, biotechnological machines, and microneedles offer solutions to global health crises. Deploying smart robots and support systems can help governments reduce public spending on future respiratory viruses, such as a NextGen Respiratory Virus or NeoCorona Virus. This paper presents a robot-based finite-time optimal control model to combat a hypothetical infectious disease, Pandemic-X, capable of causing a global pandemic. The system is optimised for Pandemic-X, integrating vaccination, robotic control, and incidence-rate dynamics to enhance public health emergency responses. Our approach consolidates optimal control strategies, including vaccination and Computational Internet of Things Robotics (CIoTR), in the post-COVID-19 era. Two strategies are proposed: Pontryagin stochastic optimisation for managing disease spread and a CIoTR-based control approach. The model maximises the susceptible and recovered populations while minimising exposed, asymptomatic, and symptomatic cases. The robot operates in three power modes: i) ‘super-active’ for high-computation edge inferencing and vaccination, ii) ‘moderate’ for balanced fallback operations, and iii) ‘sleep’ for idle states. Results show strong alignment between simulated and real data in vaccination, infection reduction, and hospitalisation trends. These findings demonstrate that robotic optimal control strategies can effectively manage pandemic spread, reduce healthcare burdens, and minimise transmission risk.