A Comparative Analysis of Multidimensional COVID-19 Poverty Determinants: An Observational Machine Learning Approach
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Theoretical Computer Science,Software
Link
https://link.springer.com/content/pdf/10.1007/s00354-023-00203-8.pdf
Reference51 articles.
1. World Bank Group, Poverty and Shared Prosperity 2020, Accessed: 26 Feb 2022
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3. Zixi, H.: Poverty Prediction Through Machine Learning, 2021 2nd International Conference on E-Commerce and Internet Technology (ECIT) (2021), pp. 314–324
4. Jean, N., Burke, M., Xie, M., Davis, W.M., Lobell, D.B., Ermon, S.: Combining satellite imagery and machine learning to predict poverty. Science 353(6301), 790–794 (2016)
5. Alkire, S., Nogalesa, R., NaïriQuinn, N., Suppaade, N.: Global multidimensional poverty and COVID-19: a decade of progress at risk? Soc. Sci. Med. 291, 114457 (2021)
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