Developing a dynamic modeling framework for surveillance, prediction, and real-time resource allocation to reduce health disparities during Covid-19 and future pandemics

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Project Summary Black, Hispanic, and rural Americans are twice as likely to die from Coronavirus Disease 2019 (Covid-19). These health disparities have been fueled by inadequate access to essential resources throughout the pandemic. Such inequities are not unique to Covid-19. Over the past century, emerging infectious diseases have significantly perpetuated health disparities in underserved communities. The interconnected pathways leading to these disparities, including heterogeneous disease epidemiology, sociodemographic characteristics, and treatment access and uptake, remain understudied. Mobile health clinics (MHC) are an effective and versatile tool for reducing health disparities through timely delivery of interventions to medically underserved populations. However, the inability to effectively identify and prioritize high-risk communities has posed daunting challenges for MHC decision makers and has led to suboptimal allocation strategies. To help improve the efficiency of these field-level interventions and reduce health disparities during Covid-19 and future pandemics, our proposal seeks to develop a modeling toolkit to improve infectious disease surveillance and prediction in underserved populations and prioritize the delivery of essential resources to high-risk communities in real time. Our innovative, multilevel modeling framework will utilize statistical models, machine learning, compartment-based and agent-based models to reduce health disparities through 1) establishing a real-time data system feed for infectious disease surveillance and estimation of disease epidemiology in underserved communities 2) identifying at-risk populations for allocation of essential resources, 3) evaluating the complex interplay between sociodemographic and clinical characteristics, infectious disease epidemiology, modifiable health barriers, and intervention uptake in order to improve emergency planning during the Covid-19 pandemic and future health emergencies, and 4) establishing a modeling toolkit to inform delivery of essential resources to underserved communities in real-time. This will be accomplished through real-time integration of infectious disease outcome data, demographic, socioeconomic, and clinical characteristics, vaccine hesitancy surveys, community-level contextual factors, and data on structural barriers to health care for estimation of key input parameters in the dynamic simulation modeling framework. The framework we propose will be generalizable to other infectious diseases, where model inputs will be disease and location dependent for swift translation to other public health problems. To demonstrate the utility of our toolkit, our modeling framework will focus on delivery of Covid-19 mobile vaccination clinics to underserved populations in South Carolina (SC). Our proposal will improve pandemic planning by developing the modeling infrastructure for disease surveillance and understanding of infectious disease epidemiology in underserved communities, ultimately improving timely delivery of essential resources to those of greatest need. Covid-19 has claimed nearly 1 million American lives and has hospitalized over 4 million individuals through February 2022. Utilization of this toolkit by public health decision makers can prevent thousands of future Covid-19 deaths. Through adaptation of input data sources, our modeling framework is easily translatable to other infectious diseases and geographic regions and has potential to save many more lives in future pandemics.