Glioblastoma remains one of the most lethal brain cancers. Combination therapy using CAR-T cells and oncolytic viruses shows promise, yet the mechanisms underlying their synergy remain poorly understood. We develop mathematical models to analyze IL-13Rα2-targeting CAR-T cells and the oncolytic virus C134 using patient-derived glioblastoma data. We propose a minimal model framework for...
Systemic HIV infection is typically established following mucosal exposure, but the earliest events stretching from viral dissemination from those mucosal tissues to body-wide (systemic) infection remain incompletely defined. Using tissue-level SIV RNA data from rhesus macaques (an animal model for HIV) we broadly aim to map paths of infection through tissues as infection is established, to...
Norovirus is a leading cause of acute gastroenteritis globally, yet community infection burden remains poorly quantified due to asymptomatic transmission and clinical underreporting. Wastewater-based surveillance provides a community-level signal of infection prevalence, but translating viral RNA concentrations into epidemiological estimates is nontrivial, and requires explicit modeling of the...
Classical compartmental models often overestimate the size of a pandemic due to the assumption of a homogeneous population. At the early stage of an outbreak, individuals with higher mobility are more likely to become infected, resulting in an inflated estimate of the final pandemic size. In this talk, we introduce a heterogeneous compartmental model in which each individual has different...
The integration of novel data streams with mathematical models plays an increasingly important role in understanding disease dynamics and informing intervention strategies. This minisymposium focuses on challenges and opportunities arising from limited observability, data quality, and model complexity in biological and epidemiological systems. Topics include inference and identifiability under...
