CCSS Meeting #61: Complexity and data science for the Sustainable Development Goals: the case of food security
The format of this lecture has been changed to a hybrid one: the speaker Dr. Elisa Omodei will be speaking online from Vienna, and Dr. Brian Dermody will be physically present as the on-site moderator to open the session and initiate discussions. The theme of this CCSS Lunch Meeting is Human Well-being.
In the CCSS living room (MIN4.16), participants can enjoy refreshments and lunch - please signup for free below.
Dr. Elisa Omodei is an Assistant Professor at the Department of Network and Data Science of the Central European University, Vienna, Austria. Previously, she worked at the United Nations, first at UNICEF's Office of Innovation in New York and then at the UN World Food Programme in Rome. She also served as Vice-President Secretary of the Complex Systems Society from 2018 to 2021. In her research, she explores how complexity and data science can help us address the needs of the most vulnerable populations and monitor the UN Sustainable Development Goals.
In a rapidly changing world, facing an increasing number of socioeconomic, health and environmental crises, complexity and data science can help us quantify vulnerabilities and monitor progress towards achieving the UN Sustainable Development Goals. In this talk, I will first provide a non-exhaustive overview of the main areas of applications where non-traditional data and computational approaches have shown their potential for social impact, and I will then deep-dive more specifically into my work on predicting food insecurity from conflict, weather, and economic data.
Estimating how many people are food insecure and where they are is of fundamental importance for governments and humanitarian organizations to make informed and timely decisions on relevant policies and programmes. In this study, we propose a machine learning approach to predict the prevalence of people with insufficient food consumption and of people using crisis or above-crisis food-based coping when primary data are not available. Making use of a unique global dataset, the proposed models can explain up to 81% of the variation in insufficient food consumption and up to 73% of the variation in crisis or above food-based coping levels. We also show that the proposed models can nowcast the food security situation in near real time and propose a method to identify which variables are driving the changes observed in predicted trends, which is key to make predictions serviceable to decision-makers.
There will be 45-min lecture from the speaker, followed by a 15-min Question & Answer session.
To attend the lecture online, please click the Zoom link at 12:00 on Tuesday 12 December.
To attend the lecture (physically), please signup below before 15:00 on Monday 11 December.
- Start date and time
- End date and time
- Hybrid Meeting >> CCSS Living Room, Room 4.16, Minneartgebouw
- More information
- Zoom link