OPTIMIZATION OF QUARANTINE AND ISOLATION POLICIES USING CONTROL THEORY IN PANDEMIC SCENARIOS

Imagine a pandemic where infection curves flatten not by forceful lockdowns, but through real-time algorithms that optimize policy responses. This study evaluated how control theory applications-spanning household adherence tracking, quarantine logistics, and adaptive feedback mechanisms-enhanced infection control outcomes in Ghana between 2020 and 2024. Using 105 time-series observations sourced from the Ghana Health Service, WHO, and other global datasets, the research applied descriptive statistics, correlation matrices, and multiple regression analysis. Key findings show that quarantine system efficiency (β = -0.293, p = 0.037) and policy/social constraints (β = -0.618, p = 0.001) significantly influenced infection control outcomes, while the total variance explained was R² = 0.209 and the overall correlation coefficient was r = -0.366 for policy constraints. Results revealed a 62.5% reduction in community transmission, a 54% decline in peak ICU demand, and an 8.9-fold increase in adaptive policy deployment. These outcomes demonstrate the transformative potential of control theory in real-time epidemic management. The study recommends scaling digital dashboards, integrating AI-based risk scoring, investing in quarantine infrastructure, and aligning policy thresholds with mobility data to institutionalize dynamic pandemic containment strategies.