Overview

Our research is at the forefront of infectious disease modeling and analytics. We are committed to developing innovative analytical methods, tools, data streams and platforms that enhance our understanding of infectious diseases and inform public health strategies. Our interdisciplinary team collaborates across Emory University, leveraging expertise from the Rollins School of Public Health, the School of Medicine, and the College of Arts and Sciences.

Advances in Wastewater Epidemiology

The wastewater team is led by Dr. Katia Koelle and builds upon Dr. Marlene Wolfe’s leadership as a Program Director of Wastewater SCAN, a national wastewater monitoring system that informs public health responses, along with Dr. Andrew Wang’s work on spatial mapping of sewer networks and Dr. Anne Piantadosi’s expertise in sample sequencing.

  • Sample Sequencing:
    The wastewater team is analyzing banked samples from Wastewater SCAN and the Georgia Department of Public Health. This analysis provides insights into infectious disease signals present in wastewater, which informs disease modeling efforts and contributes to more accurate predictions.
  • Machine Learning for Forecasting:
    Researchers are leveraging machine learning techniques and combining wastewater data with other surveillance data streams to improve the ability to generate real-time estimates of disease spread and enhance forecasting methods.

Examining Social Contact Patterns

Under the leadership of Dr. Kristin Nelson and Dr. Sharia Ahmed, the Engaging Atlantans to Guide Effective Disease (ENGAGED) project is a joint effort between CIDMATH and Kaiser Permanente Georgia (KPGA). This initiative aims to study social contact patterns among Atlanta residents, generating critical data for modeling the spread of both endemic and emerging pathogens in the U.S.

  • Methodology:
    • This is a prospective cohort study of KPGA members. Six consecutive monthly surveys will be filled out on a scheduled basis by study participants to record their social contacts from the previous day.
    • Additionally, event-triggered social contact surveys will be deployed if a patient is seen at Kaiser Permanente for an acute respiratory illness (ARI) or acute gastroenteritis (AGE).
  • Data Integration:
    • Survey responses will be linked to both respondents’ KPGA electronic health records and data on vaccinations received.

Novel Methods for Vaccine Evaluation

Dr. Elizabeth Rogawski McQuade is spearheading developing methods for the Vaccine Evaluation project using a target-trial emulation approach (designing observational studies in a way that replicates the rigor of a clinical trial). Key collaborators include Dr. David Benkeser, Dr. Natalie Dean, Dr. Veronika Zarnitsyna, and Dr. Razieh Nabi.

  • Formalizing Observational Vaccine Effectiveness
    • This research aims to develop new metrics for how well a vaccine works in real-life settings that are comparable to similar metrics for studies that are based on clinical trials. This work will allow for a wider variety of vaccine studies to be brought together to assess the performance of a vaccine.
  • Estimating Vaccine Effectiveness Without Matching
    • Previous studies of vaccine effectiveness have relied on comparing vaccinated/unvaccinated subjects with similar characteristics (“matching”), which is not always reproducible and is statistically inefficient. The new methods to which this research will lead are meant to allow for higher-level machine learning analyses without needing to match study participants.
  • Application to Real-World Data
    • The research team will apply these new methods to an observational cohort of patients enrolled in KPGA.

Machine Learning Models for Disease Forecasting

Dr. Max Lau is pioneering machine learning approaches to forecast disease trends.

  • Adaptability:
    • Well-designed machine learning models can be flexibly adapted to different disease systems and novel, emerging infections.
  • Forecasts:
    • These models generate accurate short- to medium-term forecasts that enhance our ability to inform public health planning.