Predicting the influence of Covid-19 on GDP with ensembled machine learning models

Autor:	Yudong Wu, Yafei Wu, Youshuai Yang, Ye Boshuai
Rok vydání:	2021
Předmět:	2019-20 coronavirus outbreak Coronavirus disease 2019 (COVID-19) Economic indicator Computer science business.industry Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Pandemic Per capita Artificial intelligence business Machine learning computer.software_genre computer
Zdroj:	2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID).
DOI:	10.1109/aiid51893.2021.9456539
Popis:	Since it first broke out in 2019, the Covid-19 pandemic has made a huge impact on everyone's daily lives. As vaccination progresses smoothly in many countries and the pandemic gradually gets under control, it is important that we understand its impact on the global economy to fully recover from the damage Covid-19 has caused. In this paper, we analyzed over 50000 records of pandemic data for over 200 countries to identify the important factors affecting the GDP per capita of the countries. We also proposed a method ensembling three machine learning models predict a country's GDP based on pandemic data.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::237e3d12421f674861fcbea192e66634 https://doi.org/10.1109/aiid51893.2021.9456539 Zobrazit plný text záznamu