Details

Autor(en) / Beteiligte

• Gibson, Graham Casey
• Moran, Kelly R
• Reich, Nicholas G
• Osthus, Dave
• Althouse, Benjamin Muir

Titel

Improving probabilistic infectious disease forecasting through coherence

Ist Teil von

• PLoS computational biology, 2021-01, Vol.17 (1), p.e1007623-e1007623

Ort / Verlag

United States: Public Library of Science

Erscheinungsjahr

2021

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• With an estimated $10.4 billion in medical costs and 31.4 million outpatient visits each year, influenza poses a serious burden of disease in the United States. To provide insights and advance warning into the spread of influenza, the U.S. Centers for Disease Control and Prevention (CDC) runs a challenge for forecasting weighted influenza-like illness (wILI) at the national and regional level. Many models produce independent forecasts for each geographical unit, ignoring the constraint that the national wILI is a weighted sum of regional wILI, where the weights correspond to the population size of the region. We propose a novel algorithm that transforms a set of independent forecast distributions to obey this constraint, which we refer to as probabilistically coherent. Enforcing probabilistic coherence led to an increase in forecast skill for 79% of the models we tested over multiple flu seasons, highlighting the importance of respecting the forecasting system's geographical hierarchy.