Open AccessExplainable death toll motion modeling: COVID-19 data-driven narratives
Author(s) -
Adriano Veloso,
Nívio Ziviani
Publication year - 2022
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0264893
Subject(s) - pace , death toll , toll , covid-19 , pandemic , acceleration , motion (physics) , narrative , public health , multitude , data science , computer science , aeronautics , public relations , medicine , political science , artificial intelligence , engineering , environmental health , geography , immunology , law , physics , nursing , pathology , geodesy , classical mechanics , disease , infectious disease (medical specialty) , philosophy , linguistics
Models have gained the spotlight in many discussions surrounding COVID-19. The urgency for timely decisions resulted in a multitude of models as informed policy actions must be made even when so many uncertainties about the pandemic still remain. In this paper, we use machine learning algorithms to build intuitive country-level COVID-19 motion models described by death toll velocity and acceleration. Model explainability techniques provide insightful data-driven narratives about COVID-19 death toll motion models—while velocity is explained by factors that are increasing/reducing death toll pace now, acceleration anticipates the effects of public health measures on slowing the death toll pace. This allows policymakers and epidemiologists to understand factors driving the outbreak and to evaluate the impacts of different public health measures.