Reply to Bracher: Scoring probabilistic forecasts to maximize public health interpretability
Proc Natl Acad Sci U S A
.
2019 Oct 15;116(42):20811-20812.
doi: 10.1073/pnas.1912694116.
Epub 2019 Sep 26.
Authors
Nicholas G Reich
1
,
Dave Osthus
2
,
Evan L Ray
3
,
Teresa K Yamana
4
,
Matthew Biggerstaff
5
,
Michael A Johansson
6
,
Roni Rosenfeld
7
,
Jeffrey Shaman
4
Affiliations
1
Department of Biostatistics and Epidemiology, University of Massachusetts-Amherst, Amherst, MA 01003; nick@schoolph.umass.edu.
2
Statistical Sciences Group, Los Alamos National Laboratory, Los Alamos, NM 87545.
3
Department of Mathematics and Statistics, Mount Holyoke College, South Hadley, MA 01075.
4
Department of Environmental Health Sciences, Columbia University, New York, NY 10032.
5
Influenza Division, Centers for Disease Control and Prevention, Atlanta, GA 30333.
6
Division of Vector-Borne Diseases, Centers for Disease Control and Prevention, San Juan, PR 00920.
7
Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213.
PMID:
31558611
PMCID:
PMC6800325
DOI:
10.1073/pnas.1912694116
No abstract available
Publication types
Letter
Comment
MeSH terms
Forecasting
Humans
Influenza, Human*
Probability
Public Health
Seasons
United States
Grants and funding
MC_QA137853/MRC_/Medical Research Council/United Kingdom
R35 GM119582/GM/NIGMS NIH HHS/United States
U01 GM110748/GM/NIGMS NIH HHS/United States
R01 GM100467/GM/NIGMS NIH HHS/United States
MC_PC_17228/MRC_/Medical Research Council/United Kingdom