Janice Marie McCarthy

Janice Marie McCarthy serves as a Medical Instructor (Lecturer) in the Department of Biostatistics & Bioinformatics at Duke University's Division of Integrative Genomics, where she teaches in the Master of Biostatistics program. Her interdisciplinary expertise bridges mathematical modeling and biological systems to advance team science in biomedical research.

Her educational background includes:

• Ph.D. from Duke University (2009)

Dr. McCarthy's research centers on mathematical modeling of biological dynamics using ordinary differential equations, with dual focus areas: within-host viral/immune system interactions (HIV, influenza, BK virus) and complex antibody-antigen binding kinetics. She emphasizes interdisciplinary collaboration, leveraging her dual training to connect biologists and mathematicians for translational impact in infectious disease and immunology.

Analysis of her 15 publications (2020-2025) reveals consistent application of quantitative methods to virology and immunology challenges, particularly in viral rebound dynamics in SHIV/HIV infant models, BK virus in transplant recipients, and antibody neutralization mechanisms. Her work demonstrates strong NIH alignment through NIAID and NCI-funded projects addressing HIV cure strategies and vaccine development.

Dr. McCarthy has served as an Academic Adviser (2017-2019) and currently leads six major research initiatives:

• Multiscale Modeling of Influenza Neutralizing Antibody and Fc Effector Biology (NIAID, 2024-2029)
• Quantitative Methods for HIV/AIDS Research (NIAID, 2018-2028)
• Training Program in Bioinformatics at Cancer Immunology-Microbiome Intersection (NCI, 2020-2026)
• Mathematical Modeling for BK Virus Control in Kidney Transplants (NIAID, 2023-2025)
• SHIV Viral Rebound Intervention Statistical Core (Weill Cornell, 2020-2022)
• Integrative NGS Course Development (NIH, 2016-2020)

As part of Duke's Division of Integrative Genomics, she collaborates with virology and immunology teams using non-human primate models and clinical datasets to validate mathematical frameworks for therapeutic development.