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"Tracking machine learning models for pandemic scenarios: a systematic ..."

Marcelo Benedeti Palermo et al. (2022)

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DOI: 10.1007/S13721-022-00384-0

access: closed

type: Journal Article

metadata version: 2022-12-07

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  - https://dblp.org/rec/journals/netmahib/PalermoPCR22
Marcelo Benedeti Palermo, Lucas Micol Policarpo, Cristiano André da Costa, Rodrigo da Rosa Righi:
Tracking machine learning models for pandemic scenarios: a systematic review of machine learning models that predict local and global evolution of pandemics. Netw. Model. Anal. Health Informatics Bioinform. 11(1): 40 (2022)

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