Session

DescriptionA rapid response to the initial phase of the COVID-19 pandemic was hampered by decentralized data collection, analysis, and the novelty of the virus itself; vital metrics for virus characteristics, such as its transmissibility and virulence, were unknown. Moreover, the disease progression was spatially heterogeneous; different regions experienced waves at varying times and with differing intensities. To mitigate these challenges and better inform public health officials for the next pandemic, we are developing methods to assimilate real-world data into biologically informed agent-based models, facilitating biopreparedness at scale in near-real time. These models will allow for population stratification along multiple comorbidities or socio demographic factors across diverse geospatial regions by incorporating decentralized data in a mathematically private way. By incorporating this data from varied populations across a region, these models will assist public health agencies in mitigating an emerging outbreak and effectively managing hospital capacity. We will highlight different computational methods designed to address these key bioprepardness challenges in this minisymposium.