Pandemic? How to make right decision during crisis using modeling approach?

Kun (Maggie) Hu

Abstract

The open source platform: Spatiotemporal Epidemiological Modeler (STEM: https://www.eclipse.org/stem/) is designed to help scientists, public health officials and modeler create and use spatial and temporal models of emerging infectious diseases. These models can aid in understanding and potentially preventing the spread of infectious diseases like SARS, CoVID-19. Policymakers responsible for strategies to contain disease and prevent epidemics need an accurate understanding of disease dynamics and the likely outcomes of preventive actions. In an increasingly connected world with extremely efficient global transportation links, the vectors of infection can be quite complex. STEM facilitates the development of advanced mathematical models (ODEs), the creation of flexible models involving multiple populations (species) and interactions between diseases, and a better understanding of epidemiology. STEM is designed to make it easy for developers and researchers to plug in their choice of models. It comes with a large number of existing disease models and a model building framework that allows users to rapidly extend existing models or to create entirely new models. The model building framework provides a simple graphical users interface and automatically generates all of the model code and hot injects the code into STEM at runtime. In many cases, no knowledge of Eclipse or Java is required.

In this lecture, Kun will introduce major features of STEM, basics of epidemiological models using ODEs, and several diseases models built in STEM. During epidemic outbreak or global pandemic, those are all critical knowledge and means of research to help one ask right answers, evaluate different intervention strategies, and possibly make right decisions at a population level.

Speaker

Photo of Kun (Maggie) Hu

Kun Hu, Ph.D., is a research staff member and research manager at IBM Almaden Research Center in San Jose, CA. She was trained as a system and behavioral scientist. She is an expert in system dynamics modeling (mathematical model and simulation), Artificial Intelligence (ML/DL). During her early career, she applied quantitative approaches, such as compartmental modeling, agent-based modeling and social network analysis (SNA) to get a better understanding of distinct complex dynamic systems in the domain of public health. She is a committer to the Eclipse open source project: Spatio-Temporal Epidemiological Modeler (STEM: https://www.eclipse.org/stem/). She led community efforts to respond recent epidemic outbreaks, such as H7N9 in 2013, Ebola in 2014-2015, Zika in 2015-2016, CoVID-19 in 2020. She was principle investigator on the research project: big data analytics to accelerate the foodborne disease outbreak investigation that widely reported by media including CNN. She was also nominated to work on IBM Health Corps team partnered with Panamanian CDC (Gorgas Memorial Institute) and MoH (MINSA) at Panama City, Panama in 2017. Kun is a frontier engineer named by the US National Academy of Engineering in 2017 (USFOE). She was nominated and awarded as “Silicon Valley Emerging Leader” by YWCA in 2018.

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