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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
decision sciences
Phenomenological forecasting of disease incidence using heteroskedastic gaussian processes: A dengue case study
Annals of Applied Statistics, Volume 12, No. 1, Year 2018
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Description
In 2015 the US federal government sponsored a dengue forecasting competition using historical case data from Iquitos, Peru and San Juan, Puerto Rico. Competitors were evaluated on several aspects of out-of-sample forecasts including the targets of peak week, peak incidence during that week, and total season incidence across each of several seasons. Our team was one of the winners of that competition, outperforming other teams in multiple targets/locales. In this paper we report on our methodology, a large component of which, surprisingly, ignores the known biology of epidemics at large—for example, relationships between dengue transmission and environmental factors—and instead relies on flexible nonparametric nonlinear Gaussian process (GP) regression fits that “memorize” the trajectories of past seasons, and then “match” the dynamics of the unfolding season to past ones in real-time. Our phenomenological approach has advantages in situations where disease dynamics are less well understood, or where measurements and forecasts of ancillary covariates like precipitation are unavailable, and/or where the strength of association with cases are as yet unknown. In particular, we show that the GP approach generally outperforms a more classical generalized linear (autoregressive) model (GLM) that we developed to utilize abundant covariate information. We illustrate variations of our method(s) on the two benchmark locales alongside a full summary of results submitted by other contest competitors. © Institute of Mathematical Statistics, 2018.
Authors & Co-Authors
Johnson, Leah R.
United States, Blacksburg
Virginia Polytechnic Institute and State University
Gramacy, Robert B.
United States, Blacksburg
Virginia Polytechnic Institute and State University
Cohen, Jeremy M.
United States, Tampa
University of South Florida, Tampa
Mordecai, Erin A.
United States, Palo Alto
Stanford University
Murdock, Courtney Cuin
United States, Athens
University of Georgia
Rohr, Jason R.
United States, Tampa
University of South Florida, Tampa
Ryan, Sadie J.
United States, Gainesville
University of Florida
Stewart-Ibarra, Anna M.
United States, Syracuse
Suny Upstate Medical University
Weikel, Daniel P.
United States, Ann Arbor
University of Michigan, Ann Arbor
Statistics
Citations: 25
Authors: 9
Affiliations: 7
Identifiers
Doi:
10.1214/17-AOAS1090
ISSN:
19326157
Research Areas
Infectious Diseases
Study Design
Cohort Study
Phenomenological Study
Case Study
Study Approach
Qualitative