Infectious disease prediction with kernel conditional density estimation

Volume: 36, Issue: 30, Pages: 4908 - 4929
Published: Sep 14, 2017
Abstract
Creating statistical models that generate accurate predictions of infectious disease incidence is a challenging problem whose solution could benefit public health decision makers. We develop a new approach to this problem using kernel conditional density estimation (KCDE) and copulas. We obtain predictive distributions for incidence in individual weeks using KCDE and tie those distributions together into joint distributions using copulas. This...
Paper Details
Title
Infectious disease prediction with kernel conditional density estimation
Published Date
Sep 14, 2017
Volume
36
Issue
30
Pages
4908 - 4929
Citation AnalysisPro
  • Scinapse’s Top 10 Citation Journals & Affiliations graph reveals the quality and authenticity of citations received by a paper.
  • Discover whether citations have been inflated due to self-citations, or if citations include institutional bias.