Using Computational Epidemiology to Predict the Frequency and Scale of Future Epidemics
Speaker: Nita Madhav, Ginkgo Bioworks
Abstract: Preparedness planning for future epidemics and pandemics has traditionally relied on a handful of scenarios based on historical events. Planning based on these events often leads to recency bias, is often subject to missing data, and creates a limited view of preparedness activities. To facilitate more effective planning, we developed a risk modeling approach combining computational epidemiology, extreme events modeling, and actuarial methods. We used this approach to simulate the spatiotemporal dynamics for over 150,000 scientifically plausible scenarios. Probability distributions for parameters such as emergence locations, transition dynamics, case-fatality ratios, human population density, mobility patterns, and epidemic preparedness were incorporated. We used the resulting event catalog to derive the exceedance probability (EP) function, which in turn was used to estimate the probability of an event or a
given magnitude (e.g., number of deaths), or worse, occurring in any given year. We use this approach to future COVID-level event estimate the likelihood of observing a future COVID-level event. Utilizing this approach is especially useful for low-frequency, high-impact events and can inform epidemic preparedness planning, resource allocation, and epidemic risk management.
Bio: Nita Madhav is Senior Director of Epidemiology & Modeling at Concentric by Ginkgo, the biosecurity and public health unit of Ginkgo Bioworks. Previously, Ms. Madhav served as Chief Executive Officer at Metabiota, a company that specializes in measuring, mitigating, and managing epidemic risk.
Ms. Madhav has over 15 years of experience in probabilistic modeling and risk assessment, with a focus on monitoring and modeling infectious disease spread and economic impacts. The majority of her experience has focused on developing infectious disease risk, burden, and cost models to provide actionable insights to commercial and government entities.
Before becoming CEO of Metabiota, Ms. Madhav was the Vice President of Data Science at Metabiota, where she established and led the data science and modeling group and spearheaded the team's efforts to create a comprehensive library of modeled pathogens. Before joining Metabiota, Ms. Madhav worked as a Principal Scientist at AIR Worldwide, where she led the life and health research and modeling team. Before that, Ms. Madhav performed hantavirus research at the Special Pathogens Branch of the U.S. Centers for Disease Control and Prevention. Ms. Madhav holds a B.S. in Ecology and Evolutionary Biology, with distinction, from Yale University and an M.S.P.H. in Epidemiology from the Rollins School of Public Health at Emory University.