Machine learning and phone data can improve targeting of humanitarian aid.

ABSTRACT: The COVID-19 pandemic has devastated many low- and middle-income countries, causing widespread food insecurity and a sharp decline in living standards¹. In response to this crisis, governments and humanitarian organizations worldwide have distributed social assistance to more than 1.5 billion people². Targeting is a central challenge in administering these programmes: it remains a difficult task to rapidly identify those with the greatest need given available data^3,4. Here we show that data from mobile phone networks can improve the targeting of humanitarian assistance. Our approach uses traditional survey data to train machine-learning algorithms to recognize patterns of poverty in mobile phone data; the trained algorithms can then prioritize aid to the po

SUBMITTER: Aiken E 

PROVIDER: S-EPMC8967719 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature