Video/Audio Open Access
Novel Application in Machine Learning: Predicting the Issuance of COVID-19 Stay-at-Home Orders in Africa
Rhea, Carter;
Mansell, Jordan;
Murray, Gregg
In the work, we investigate which factors are most important for the issuance of SAHO among n=54 African countries between January 31st and June 15th, 2020. We employ a novel dataset of 260 different variables capturing country-level information on economic, political, social, external, and health-related factors for each country in our dataset. To identify the most significant factors, we treat the question of which countries issue SAHO as a classification problem (issued orders vs. did not issue orders) and utilize a random forest classifier, a method of machine learning, to identify the variables that best explain whether a country issued a SAHO order or not.
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Earth&Sky-Rhea.webm
md5:a7f2df8baa5e859ee5a7bd8aaed4ecb1 |
12.8 MB | Download |
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| Views | 37 | 37 |
| Downloads | 3 | 3 |
| Data volume | 38.4 MB | 38.4 MB |
| Unique views | 34 | 34 |
| Unique downloads | 3 | 3 |
- Publication date:
- June 2, 2021
- DOI:
-
- Keyword(s):
- COVID-19, Machine Learning, Random Forest
- Meeting:
- Where the Earth meets the Sky, Copenhagen, 27-28 May 2021
- Communities:
- License (for files):
- Creative Commons Attribution 4.0 International