Abstract:
|
COVID-19 has resulted in a global pandemic with over 44 million cases and 700K fatalities in the US, leading to major strains on the healthcare system and increases in health disparities. We develop models to provide insights on how to create pseudo-population and evaluate the impact of non-pharmaceutical interventions and vaccination, focusing on the impact of these policies among the marginalized population. We outline the generation of multi-level social networks using stochastic block models to represent communication patterns in pseudo-population in NYC with connections among individuals, households, and boroughs and show how to assign individual demographic and social attributes (e.g., age, frontline worker status, occupation, race, gender, socioeconomic status, and pre-existing health conditions) based on census and patient-level databases. We use these networks to account for community demographics and geography and model the spread of COVID-19 in NYC using SEIR models. Preliminary results show evidence of the importance of considering demographics and health disparities on epidemic trajectories and differences in disease burden in marginalized populations in NYC.
|