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Nutrition Machine Learning Workshop - Day 1

A compilation of Day 1 resources, including presentations and participant list:

  • Session 1) IPC Acute Malnutrition forecasting presented by Douglas Jayasekaran: This is methodology currently used by most of the countries (does not involve AI or any mathematical modeling; mostly trend analysis with assumptions);
  • Session 2) Geospatial data solutions for humanitarian action presented by Margaretha Barkhof: the purpose of this session was to introduce the work developing global level resources for humanitarian action;
  • Session 3) ATARI presented by Nicholas Haan: The IPC Secretariat has established the Advanced Technology and Artificial Intelligence (ATARI) working group to improve forecasting for the IPC FS analysis. This presentation shared experiences that can be potentially transferred for enhancing AM predictions;
  • Session 4) Hunger Map presented by Elisa Omodei: The purpose of this session is to present the predictive model that WFP developed to nowcast the prevalence of people with insufficient food consumption - 
  • Session 5) Modelling Early Risk Indicators to Anticipate Malnutrition (MERIAM) presented by Alice Stevenson: Modelling Early Risk Indicators to Anticipate Malnutrition (MERIAM), is a four-year research project funded by the UK government, which seeks to identify, test, and scale up cost-effective means to improve the prediction and monitoring of acute malnutrition, through the use of open access secondary data.
  • Session 6) South Sudan forecasting presented by Qutab Alam: This example from South Sudan used predictive modeling techniques to estimate the burden of acute malnutrition and severity mapping in the absence of recent anthropometric data


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