Prestigious UNESCO award given for Φ-lab AI-powered dengue fever research
The International Research Centre in Artificial Intelligence under the auspices of UNESCO (IRCAI) has recognised a Φ-lab initiative as part of its Global Top 100, a list of projects solving problems related to the United Nations Sustainable Development Goals (SDGs). The research, carried out in collaboration with UNICEF, developed an Artificial Intelligence (AI) solution for quantifying dengue fever outbreaks from space.
As one of the world’s most common and rapidly spreading arboviral diseases, dengue fever causes major public health and economic consequences in tropical and sub-tropical regions. Forecasting outbreaks is an essential tool for aid agencies and health authorities, but the complex dynamics of the spread of dengue present major challenges for modelling.
A Φ-lab team worked with UNICEF to create an AI-ensemble model for predicting dengue outbreaks based on Earth Observation (EO) satellite data and products. Φ-lab research leader Rochelle Schneider dos Santos explains: “It’s crucial to factor in long-term dependency in time series data when modelling outbreaks, but this is problematic at country level due to geographical variations in dengue incidence. Our climate-based ensemble model used multiple Machine Learning approaches to predict the incident rate one month in advance for each administrative area in Peru.”
IRCAI named the Φ-lab/UNICEF research on its 2021 Global Top 100 list of AI solutions for sustainable development. The project came under the Promising category in recognition of the successful piloting of the dengue predictive model in Peru (the pilot has since been extended to Brazil).
“This project is a perfect example of collaboration between a humanitarian organisation and a research entity to support the UN SDGs,” said Dohyung Kim, Lead Data Scientist at the UNICEF Office of Global Innovation. “Not only is the outcome of the project expected to deliver information and insights for policy makers, it also provides tools which can be directly used by field operators to track the changes in dengue prevalence at a finer scale, thanks to the powerful Earth observation data from ESA.”
Full details on the Φ-lab entry for the IRCAI Global Top 100 can be found here, and the complete list of projects is here. A research paper on the initiative was published in the proceedings of the International Conference on Machine Learning 2021.
