Robust Pandemic Control Synthesis with Formal Specifications: A Case Study on COVID-19 Pandemic
This work addresses the challenge of robust pandemic control for public health and economic stability, but it is incremental as it applies existing formal methods to specific pandemic models.
The authors tackled the problem of identifying effective control strategies for pandemics like COVID-19 by developing an iterative approach to synthesize optimal control strategies using metric temporal logic specifications, with simulation results showing that the approach can generate control inputs satisfying these specifications robustly against uncertainties.
Pandemics can bring a range of devastating consequences to public health and the world economy. Identifying the most effective control strategies has been the imperative task all around the world. Various public health control strategies have been proposed and tested against pandemic diseases (e.g., COVID-19). We study two specific pandemic control models: the susceptible, exposed, infectious, recovered (SEIR) model with vaccination control; and the SEIR model with shield immunity control. We express the pandemic control requirement in metric temporal logic (MTL) formulas. We then develop an iterative approach for synthesizing the optimal control strategies with MTL specifications. We provide simulation results in two different scenarios for robust control of the COVID-19 pandemic: one for vaccination control, and another for shield immunity control, with the model parameters estimated from data in Lombardy, Italy. The results show that the proposed synthesis approach can generate control inputs such that the time-varying numbers of individuals in each category (e.g., infectious, immune) satisfy the MTL specifications with robustness against initial state and parameter uncertainties.