M. Elizabeth Halloran, MD, MPH, DSc

2020-2025
Project Title: Quantifying the Epidemiological Impact of Targeted Indoor Residual Spraying on Aedes-borne Diseases (TIRS) in Merida, Yucatan, Mexico
Source of Support: NIH/NIAID
Grant Type: U01 AI148069
Role: Consortium PI
Goal: We will conduct a two-arm, parallel, unblinded, cluster randomized controlled trial to quantify the overall efficacy of TIRS in reducing the burden of laboratory-confirmed arbovirus (ABV) clinical disease (primary endpoint). The trial will be conducted in the city of Mérida, Yucatán State, Mexico (population ~1 million), where we will prospectively follow 4,600 children aged 2-15 years at enrollment, distributed in 50 clusters of 5x5 city blocks each. Clusters will be randomly allocated (n= 25 per arm) using covariate-constrained randomization. A “fried egg” design, in which all blocks of the 5x5 cluster receive the intervention, but all sampling to evaluate the epidemiological and entomological endpoints will occur in the “yolk,” the center 3x3 city blocks of each cluster. TIRS will be implemented as a preventive application 1-2 months prior to the ABV season. Active monitoring for symptomatic ABV illness will occur through weekly household visits and enhanced surveillance. Annual serological surveys will be performed after each transmission season and entomological evaluations of Ae. aegypti indoor abundance and ABV infection rates monthly during the period of active surveillance. Epidemiological and entomological evaluation will continue for up to three transmission seasons.

2023-2028
Project Title: EPISTORM – Center for Advanced Epidemic Analytics and Predictive Modeling Technology
Source of Support: CDC Insight Net (Subcontract from Northeastern University)
Grant Type: 1NU38FT00013
Role: Consortium PI
Goal: Our proposed application will develop innovative methodologies to integrate advanced statistical and analytical frameworks and machine intelligence with mechanistic modeling techniques, identifying new approaches that improve local, state, and regional forecasting and modeling capabilities and analytics tools. The proposed activities involve integrating novel data sources—including high-resolution mobility, airline travel, genomic and wastewater surveillance data—with agent-based, statistical, and deep learning forecasting models to increase the accuracy of outbreak analytic products. Importantly, our approach will consider population heterogeneities/disparities and will deliver outbreak analytic tools for rural/underserved populations and for diseases/locations with low prevalence. Finally, we will identify best practices for how to transfer and maintain the needed methodology, technical expertise, and data sources, and establish a comprehensive training program for public health workforce and emergency response decision makers (which includes embedding co-op students into public health/healthcare delivery agencies and developing collabathons).

2023-2028
Project Title: EPISTORM – Center for Advanced Epidemic Analytics and Predictive Modeling Technology
Source of Support: CDC Insight Net (Subcontract from Northeastern University)
Grant Type: 1NU38FT00013
Role: Consortium PI
Goal: This network focuses on training, analytical tool development, and advancing the analysis and use of data about infectious disease spread. The network brings together more than 100 academic and private entities, and state, tribal, local, and territorial (STLT) health departments in a collaborative partnership, The network is structured into three categories: Innovators, Integrators, and Implementers, who develop, test, and implement forward-thinking outbreak analytic solutions for CDC, health departments, and other decision-makers across the country. The tools and methods that Insight Net develops will aid in anticipating infectious disease outbreaks, empowering public health leaders to make more informed decisions and take action to protect their communities during public health emergencies.