Although predictive analytics is not a new field, its application in humanitarian response has only just begun. The increasing availability of data from a variety of sources, together with advancements in statistics and machine learning, is generating a growing interest in using models to gain insight and trigger anticipatory action.

When it comes to modeling for humanitarian operations, the goal is to analyze current and historical data to predict an event or some characteristic of an event (the probability, severity, magnitude, or duration). Predicting an event involves anticipating a new shock such as a disaster, disease outbreak, or conflict outbreak.

Predicting the characteristics of an event could include population movements, worsening food insecurity, or precursors to drought. By creating an early signal of need that is tied to pre-agreed financing and actions, the response has the potential to be faster, cheaper and better, with more lives saved and protected.

“One of the biggest opportunities we have is to try to use data, and especially the tools of predictive analytics to get ahead, to be more anticipatory, to predict what is about to happen and to trigger the response earlier.”
-Mark Lowcock, United Nations Under-Secretary-General for Humanitarian Affairs

The Centre’s Focus

The Centre initially explored aspects of predictive analytics through our 2018 and 2019 Data Fellows Programme. The Fellows developed pilot models (for Somalia and South Sudan) and frameworks for model governance. Based on requests from UN leadership and increased demand from partners, the Centre now includes predictive analytics as a core aspect of our work. 

We are focusing on the following three areas: 

Modelling We develop new models and support existing partner models for use in humanitarian operations. 
Quality Assurance  We offer a peer review process that brings together experts in the field to assess the ethical, technical, and humanitarian relevance of OCHA and partner models.
Community We build capacity and community by convening events, developing case studies, and offering training on predictive analytics.


Modeling COVID-19 to inform humanitarian operations

The COVID-19 pandemic has brought into stark focus the need for data and the value of models to inform response strategies. Since March, the Centre has been working with the Johns Hopkins University Applied Physics Laboratory (APL) to develop a COVID-19 model adapted for use in humanitarian contexts.


Peer Review Framework

Cover page of the Peer Review Framework

The Centre has established a Peer Review Framework for Predictive Analytics in Humanitarian Response. The Framework aims to support humanitarians making the best use of predictive models, highlighting the scope within which models can be applied and the main risks that their deployment entails. The Framework consists of five steps:

  1. Model Submission
  2. Technical Review
  3. Implementation Plan Submission
  4. Ethical Review
  5. Client Consultation
  6. Recommendations and Model Report

The Centre’s Peer Review Framework is focused on predictive model development and outputs and will prioritize models being considered for informing humanitarian decision making. Through peer review, the Centre seeks to ensure models can be understood and trusted by all stakeholders including affected people. 

If your organization is interested in working with the Centre to peer review your model, contact us at  

A Catalogue of Predictive Models

The number of models being developed to inform humanitarian action is growing rapidly. To help keep track, we have created a catalogue of predictive models with basic information on ‘who is doing what, where and when’.

If your organization is developing or using a predictive model, please share information in this Google form.