The research and modeling team at IDM is focused on providing support to disease eradication programs and other global health endeavors through a variety of modeling and statistical approaches. We build mechanistic agent-based models in order to understand model assumptions and input data, examine the effects of population-level and within-host phenomena, and stimulate the impact of combined interventions, especially for all phases of an eradication effort.

The Applied Math team focuses on developing new, fundamental methodology for use in computational epidemiology. These methods have the potential to dramatically increase computational efficiency, enable new forms of analysis, and provide a deep understanding of dynamical processes. For example, the team is investigating ways to improve rare event detection, how to more efficiently explore parameter space and calibrate models, and other mathematical processes such as fractional diffusion, dynamic mode decomposition, and Monte Carlo integration.

The Epidemiology team utilizes mathematical models and rigorous data analysis to understand the epidemiology, spatiotemporal patterns, clinical progression, and transmission routes of infectious diseases. Research in the Epidemiology team ranges from more theoretical pursuits, such as developing network models to better understand human interactions and disease transmission, to more applied pursuits, such as developing models for Pneumonia, enteric infections, and typhoid.

The HIV team utilizes modeling approaches and quantitative analysis to explore how interventions can act to reduce HIV burden and transmission in generalized epidemic settings. Research from the team directly supports governments, NGOs, and other researchers to facilitate burden reduction policy and programs. IDM’s Epidemiological MODeling software (EMOD) provides a framework used by the team to test the best ways create affordable antiretroviral treatment programs, to model epidemic trends and how they are impacted by imperfect data sets and varied human behavior, and to understand the drivers of HIV in an effort to better disrupt transmission.

The Malaria team is focused on using data-driven approaches to control and eliminate malaria. Models produced by the team help evaluate control efforts, and determine the best intervention strategies for specific locations. Research efforts combine surveillance data with IDM’s own Epidemiological MODeling software (EMOD) to produce implementable strategies most suitable to interrupt transmission. For example, the approaches employed by the team can evaluate the impact of currently in-development.

The Polio team is engaged in a large-scale effort to eradicate polio. Using computer simulations on disease transmission and advanced analytics, the team evaluates the impact of strategies currently employed to fight polio and evaluates the impact of novel strategies prior to their use in the field. Models developed by the Polio team focus on disease transmission dynamics and polio epidemiology, predicting risk and virus movement, evaluating vaccination programs, and understanding the complex nature of polio virus science and immunology.

The TB team utilizes multi-scale modeling tools to estimate the impact of control strategies for preventing, diagnosing, and treating tuberculosis. By leveraging the dynamic capabilities of IDM’s Epidemiological MODeling software (EMOD), the team explores a variety of policy-relevant topics, specifically regarding the impacts of interventions and what TB indicators may be most important for tracking epidemics. Research from the team directly addresses the policy and strategies used to achieve the 2025 and 2035 TB Global Targets in China, India, and South Africa. Further, the team focuses on how to best create pathways to healthcare, and how TB-HIV co-infection impacts the dynamics of TB and HIV.