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Applying Data Assimilation Tools to COVID Forecasting Models

Femke Vossepoel

Professor in Geoscience and Engineering at Delft University of Technology in the Netherlands
About this episode

Femke Vossepoel, Professor in Geoscience and Engineering at Delft University of Technology in the Netherlands, explains how data assimilation tools can be used to improve COVID-19 forecasting models.


After earning her PhD in Aerospace Engineering at Delft, Femke spent several years in oceanography, climate research, and subsurface modeling. She developed an expertise in data assimilation that she’s now applying to improve COVID-19 pandemic forecasting models.

Femke explains that data assimilation originated in weather forecasting, where a model is updated with the current day’s weather observations to provide a more accurate forecast for the next day. Data assimilation tools tune the model to provide a more accurate forecast. This concept can be applied in many areas including financial markets, the oil industry, and for COVID-19 research.

To help improve COVID-19 forecasting, she is using a compartmental model where there are compartments for different groups: those susceptible to COVID-19, those exposed to it, those infected, those who recovered, those in quarantine, and those who are deceased. The model is like a set of boxes, and the transition from one box to the other is governed by an ordinary differential equation. Then in those equations, you have parameters, which are typically not well-known.

The data assimilation approach is to work more from the “outside in” instead of from the “inside out”. So, if you know the number of people that have died since the start of COVID, then according to this data, you can determine what the parameters would have looked like three weeks ago. With this type of inverse modeling, you can actually tune the parameters in that compartment model, and find the most likely reproduction number or the likely number of infected in the first place. The approach of having these simple relationships between the different compartments is a good framework for a very complex process. However, you cannot expect the data to tell you the story if you don’t have any prior domain knowledge. In order to take their research to the next level, it will be critical for Femke and her colleagues to collaborate with the medical experts that built the models who know how to express certain relationships.

As she has transitioned from one field to another in her career, Femke has needed to learn how to apply her expertise to entirely different research areas. She says one of the most important skills she has developed is to ask a lot of questions and not worry about being wrong and she advises young researchers to do the same. Sometimes those questions can help people already in the field think differently, and lead to new insights.

Femke’s experience as an endurance athlete has also taught her valuable lessons for her work as a scientist. “People who excel in sports lose more races than they win. You have to make mistakes and fail, that’s the way you actually grow.” It also teaches you perseverance, to hang in there when it gets tough, and be happy with small increments of your own progress rather than always comparing yourself to your competitors.

About the Host
Margot Gerritsen

Stanford Professor [Emerita] Margot Gerritsen is the Executive Director and co-founder of Women in Data Science Worldwide (WiDS) and born and raised in the Netherlands. Margot received her MSc in Applied Mathematics from Delft University of Technology before moving to the US in search of sunnier and hillier places. In. 1996 she completed her PhD in Scientific Computing & Computational Mathematics at Stanford University and moved further West to New Zealand where she spent 5 years at the University of Auckland as a lecturer in Engineering Science. In 2001, she returned to Stanford as faculty member in Energy Resources Engineering. Margot was the Director of the Institute for Computational & Mathematical Engineering (ICME) at Stanford from 2010-2018 and the Senior Associate Dean for Educational Affairs in Stanford’s School of Earth Sciences from 2015-2020. In 2022, Margot took Emerita status to devote herself to WiDS full time. Margot is a Fellow of the Society of Industrial & Applied Mathematics, and received honorary doctorates from Uppsala University, Sweden, and the Eindhoven University of Technology in the Netherlands. She now lives in Oregon with her husband Paul.

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