Call for transparency of COVID-19 models
Author(s) -
C. Michael Barton,
Marina Alberti,
Daniel P. Ames,
JoAn Atkinson,
Jerad Bales,
Edmund Burke,
Min Chen,
Saikou Y. Diallo,
David J. D. Earn,
Brian D. Fath,
Zhilan Feng,
C. L. M. H. Gibbons,
Ross A. Hammond,
Jane M. Heffernan,
Heather Houser,
Peter S. Hovmand,
Birgit Kopainsky,
Patricia L. Mabry,
Christina Mair,
Petra Meier,
Rebecca Niles,
Brian A. Nosek,
Nathaniel Osgood,
Suzanne A. Pierce,
Gary Polhill,
Lisa A. Prosser,
Erin Robinson,
Cynthia Rosenzweig,
Shankar Sankaran,
Kurt C. Stange,
Gregory E. Tucker
Publication year - 2020
Publication title -
science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 12.556
H-Index - 1186
eISSN - 1095-9203
pISSN - 0036-8075
DOI - 10.1126/science.abb8637
Subject(s) - transparency (behavior) , covid-19 , pandemic , infectious disease (medical specialty) , open data , open science , business , data science , computer science , disease , computer security , medicine , world wide web , virology , pathology , outbreak , physics , astronomy
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