Speaker
Description
Pandemics are expected to emerge more frequently due to population growth and climate change. Alongside traditional vaccines, broad-spectrum medical countermeasures offer potential to control diverse, unknown pathogens. We developed a hybrid mathematical framework to evaluate these interventions, combining an individual-based network branching process for early stochastic outbreaks with a deterministic SIR-type compartmental model for large-scale transmission. The model incorporates key pathogen traits (basic reproductive number, incubation, symptom ratio, and severity) and simulates antivirals with multi-faceted mechanisms affecting transmission, susceptibility, and mortality.
Applying this to profiles like Influenza H1N1 and SARS-CoV-2, we found that optimal distribution strategies are highly sensitive to pathogen kinetics and the epidemic phase. For pathogens with high transmissibility and short generation times, early ring-administration often fails to contain the outbreak. However, during widespread transmission, shifting focus to high-risk groups substantially reduces hospitalization and severity. Our results demonstrate that a "one-size-fits-all" approach to pandemic preparedness is inadequate. Effective stockpiling and deployment require dynamically tailored strategies that balance early containment with late-stage mitigation, rigorously accounting for pathogen-specific kinetics and the evolving scale of transmission.
