Speaker
Description
Existing single-scale HIV models often overlook conflicts between within-host and population-level treatment strategies. To resolve this, we develop a multi-scale model with infection-age structure to optimize antiretroviral therapy (ART) initiation timing and efficacy through multi-objective optimization. By reformulating initiation time as a control variable in the system, we establish the existence of Pareto-optimal solutions through variational analysis and provide a theoretical characterization of the optimal treatment schedule. Numerical analyses reveal that optimal ART strategies must dynamically adapt to evolving priorities between individual outcomes and population-level benefits: early high-efficacy ART maximizes individual benefits, while delayed initiation optimizes population outcomes under persistent post-treatment risks. Post-treatment behavioural shifts influence this balance: reduced sexual activity supports earlier initiation, while high-risk populations require precisely timed delayed administration to balance transmission control and treatment costs. This framework provides quantitative principles for reconciling scale-specific treatment priorities.
