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Karim Barhoumi, Seung Mo Choi, Tara Iyer, Jiakun Li, Franck Ouattara, Mr. Andrew J Tiffin, and Jiaxiong Yao

Copyright Page © 2022 International Monetary Fund WP/22/88 IMF Working Paper African Department Overcoming Data Sparsity: A Machine Learning Approach to Track the Real-Time Impact of COVID-19 in Sub-Saharan Africa Prepared by Karim Barhoumi, Seung Mo Choi, Tara Iyer, Jiakun Li, Franck Ouattara, Andrew Tiffin, and Jiaxiong Yao Authorized for distribution by Papa N’Diaye May 2022 IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate . The views expressed in IMF Working

Karim Barhoumi, Seung Mo Choi, Tara Iyer, Jiakun Li, Franck Ouattara, Mr. Andrew J Tiffin, and Jiaxiong Yao

for emerging markets and developing economies, including Turkey ( Solmaz and Sanjani 2015 ), Lebanon ( Tiffin 2016 ), India ( Iyer and Gupta 2019 ), and others ( Marini 2016 ; Narita and Yin 2018 ). This paper contributes to the literature by expanding the application to the data-sparse environment of sub-Saharan Africa. 2 Machine-learning algorithms have often performed relatively well when quickly capturing sharp turning points in GDP growth . Jung and others (2018) test for the robustness of machine-learning forecasts in historical crises and find that