Developing a dynamic modeling framework for surveillance, prediction, and real-time resource allocation to improve health outcomes during infectious disease outbreaks - Individuals in low-resource communities were twice as likely to die from Coronavirus Disease 2019 (Covid-19). These poor health outcomes were fueled by inadequate access to essential resources throughout the pandemic. Such outcomes are not unique to Covid-19. Over the past century, emerging infectious diseases have significantly perpetuated poor health outcomes in low-resource communities. The interconnected pathways leading to these outcomes, including heterogeneous disease epidemiology, sociodemographic characteristics, and treatment uptake, remain understudied. Mobile health clinics (MHC) are aneffective and versatile tool for improving health outcomes through timely delivery of interventions to populations with insufficient medical resources. However, the inability to effectively identify and prioritize communities with high disease burden 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 improve health outcomes during infectious disease outbreaks, our proposal seeks to develop a modeling toolkit to improve infectious disease surveillance and prediction in low-resource settings and prioritize the delivery of essential resources to high-burden communities in real time. Our innovative, multilevel modeling framework will utilize statistical models, geospatial models, machine learning, compartment-based and agent-based models to improve health outcomes through 1) establishing a real-time data system feed for infectious disease surveillance and estimation of disease epidemiology across communities 2) identifying high-burden populations for allocation of essential resources, 3) evaluating the complex interplay between sociodemographic and clinical characteristics, infectious disease epidemiology, healthcare availability, and intervention uptake in order to improve emergency planning and response during infectious disease outbreaks and future health emergencies, and 4) establishing a modeling toolkit to inform delivery of essential resources to communities in real-time. This will be accomplished through real-time integration of infectious disease outcome data, sociodemographic, clinical characteristics, and community-level healthcare availability and other contextual factors 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 MHCs to South Carolina communities for Covid-19, influenza, RSV, HCV, and HIV screening and treatment. Our proposal will improve emergency planning by developing the modeling infrastructure for community-level disease surveillance and epidemiology, ultimately improving timely delivery of essential resources to those of greatest need. Covid-19 alone had claimed nearly 1 million American lives and hospitalized over 4 million individuals through February 2022. Utilization of this toolkit by public health decision makers can prevent thousands of future infectious disease-related deaths. Our modeling framework is translatable to all infectious diseases and geographic regions and has potential to save many more lives during infectious disease outbreaks and future health emergencies.
