In 2020, the COVID-19 pandemic has highlighted epidemiological models, especially compartmental models, to support insight generation and decision-making. The use of epidemiological models is not new, however many new communities are being introduced to their utility, and their potential. While there is an abundance of data, and several dashboard examples to present their insights, there are still very few mechanisms which are being used to leverage available data with models. In this talk we present how STEM, a modelling platform built on Eclipse, was used as a foundation to use data with models for COVID-19 and engage a broader audience. STEM was used to generate insights on two key classes of understanding: What happened in the past, and what could happen in the future. This is a great time for scientific communities to be exposed to STEM, and by extension Eclipse. Too many are developing models using custom low-level approaches which do not scale, and are difficult to share. In a time where information velocity is high such approaches result in missed opportunities to support timely action.
This session will provide attendees with a deep dive into all the components which are used to support the decision-making pipeline.