Spatiotemporal prediction of infectious diseases using structured Gaussian processes with application to Crimean–Congo hemorrhagic fever

Çiğdem Ak, Önder Ergönül, İrfan Şencan, Mehmet Ali Torunoğlu, Mehmet Gonen

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

Background: Infectious diseases are one of the primary healthcare problems worldwide, leading to millions of deaths annually. To develop effective control and prevention strategies, we need reliable computational tools to understand disease dynamics and to predict future cases. These computational tools can be used by policy makers to make more informed decisions. Methodology/Principal findings: In this study, we developed a computational framework based on Gaussian processes to perform spatiotemporal prediction of infectious diseases and exploited the special structure of similarity matrices in our formulation to obtain a very efficient implementation. We then tested our framework on the problem of modeling Crimean–Congo hemorrhagic fever cases between years 2004 and 2015 in Turkey. Conclusions/Significance: We showed that our Gaussian process formulation obtained better results than two frequently used standard machine learning algorithms (i.e., random forests and boosted regression trees) under temporal, spatial, and spatiotemporal prediction scenarios. These results showed that our framework has the potential to make an important contribution to public health policy makers.

Original languageEnglish (US)
Article numbere0006737
JournalPLoS Neglected Tropical Diseases
Volume12
Issue number8
DOIs
StatePublished - Aug 1 2018
Externally publishedYes

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health
  • Infectious Diseases

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