Fundamental epidemiology

Reliably modeling the dynamics of vaccine preventable diseases involves understanding the fundamental epidemiology and having the right data sources. In addition to demographics like age, location, race, and ethnicity, the COVID-19 pandemic has highlighted the critical role of human behavior and contact patterns.  Our projects focus on innovative approaches for modeling vaccine preventable diseases using novel data streams, such as social mixing data and phylogenetics, coupled with modern techniques.

Keywords:
Computational epidemiology, disease dynamics, mathematical modeling, phylogenetic analysis, social mixing, statistical modeling

Core faculty:
Samuel Jenness, Katia Koelle, Max Lau , Ben Lopman, Kristin Nelson, Ymir Vigfusson

 

Example Projects

The purpose of this project is to study social mixing patterns in resource limited countries to better parameterize infectious disease models, and thus evaluate infectious disease interventions.

Funder:
NIH/NICHD

Affiliated Faculty:
Ben Lopman PhD, Kristin Nelson PhD, Samuel Jenness PhD

Collaborators:
Yale University; ISI Global; Christin Medical College (India); Universidad de Valle (Guatemala); CISM (Mozambique); Aga Khan University (Pakistan)

Selected Publications:

Website

The purpose of this project is to study social mixing patterns in corporate settings to better parameterize infectious disease models and thus evaluate infectious disease interventions using seasonal influenza as a proxy for a pandemic.

Funder:
CDC (Centers for Disease Control)

Affiliated Faculty:
Ben Lopman PhD, Kristin Nelson PhD, Samuel Jenness PhD

Collaborators:
Yale University 

Selected Publications: 

Website

This project aims to understand the transmission dynamics of COVID-19 using advanced modeling techniques.

Funder:
Emory COVID-19 Response Collaborative

Affiliated Faculty:
Max Lau PhD, Ben Lopman PhD, Kristin Nelson PhD

Collaborators:
Georgia Department of Public Health

Selected Publications:

Website