Vienna, September 16, 2022 – Researchers of a recent paper published by Nature Food - “Machine learning can guide food security efforts when primary data are not available” - suggest a method which they claim will allow decision-makers to make more timely and informed decisions on policies and programs oriented towards the fight against hunger.
In 2021, 193 million people across 53 countries were acutely food insecure. This number has been steadily increasing during the last few years also as a consequence of the COVID-19 pandemic. To address this global issue, monitoring the situation and its evolution is key.
Governments and humanitarian organizations perform food security assessments on a regular basis through face-to-face and remote mobile phone surveys. However, these approaches have high costs in both monetary and human resources, and hence primary data on the food security situation is not always available for all affected areas. Yet this information is key for governments and humanitarian organizations.
To tackle this issue, researchers of the Nature Food paper propose a machine learning approach to predict the number of people with insufficient food consumption when up-to-date direct measurements are not available. “We also propose a method to identify which variables are driving the changes observed in predicted trends, which is key to making predictions serviceable to decision-makers,” says Assistant Professor Elisa Omodei (Department of Network and Data Science, CEU, Vienna).
The proposed method uses a machine learning algorithm to estimate the current food insecurity situation in a given area from data on the key drivers of food insecurity: conflict, weather extremes and economic shocks. The results show that the proposed methodology can explain up to 81% of the variation in insufficient food consumption.
Researchers claim their approach opens the door to food security near-real time nowcasting on a global scale, allowing decision-makers to make more timely and informed decisions on policies and programs oriented towards the fight against hunger, in the effort to try to achieve SDG 2 of the 2030 Agenda for Sustainable Development.
Notes for Editors:
Central European University (CEU) is accredited in the United States, Austria, and Hungary, and offers English-language bachelor's, master's and doctoral programs in the social sciences, the humanities, law, environmental sciences, management and public policy. With two campuses located in the heart of Central Europe – Vienna, Austria and Budapest, Hungary – CEU has a distinct academic and intellectual focus. The university combines the comparative study of the region's historical, cultural, and social diversity with a global perspective on areas of critical enquiry including good governance, sustainable development and social transformation.
The Department of Network and Data Science at Central European University carries out research with a special focus on the foundations and applications of network science to practical data-driven problems. The Department organizes a BA program in Quantitative Social Sciences as well as hosts a PhD Program and an Advanced Certificate Program in Network Science.
