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

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. Author summary: Infectious diseases cause important health problems worldwide and create difficult challenges for public health policy makers. That is why they need reliable computational tools to better understand disease and to predict case counts. They will benefit from such computational tools to make more informed decisions in developing control and prevention strategies. We formulated a computational framework that can be used to model spatial, temporal, or spatiotemporal dynamics of infectious diseases. We showed the utility of our framework on the problem of modeling Crimean–Congo hemorrhagic fever in Turkey.