Researcher:
Ak, Çiğdem

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Çiğdem

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Ak

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Ak, Çiğdem

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2018 - 20202

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    PublicationOpen Access
    A prospective prediction tool for understanding Crimean-Congo haemorrhagic fever dynamics in Turkey
    (Elsevier, 2020) N/A; N/A; Department of Industrial Engineering; Ak, Çiğdem; Ergönül, Önder; Gönen, Mehmet; Faculty Member; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; School of Medicine; College of Engineering; N/A; 110398; 237468
    Objectives: we aimed to develop a prospective prediction tool on Crimean-Congo haemorrhagic fever (CCHF) to identify geographic regions at risk. The tool could support public health decision-makers in implementation of an effective control strategy in a timely manner. Methods: we used monthly surveillance data between 2004 and 2015 to predict case counts between 2016 and 2017 prospectively. The Turkish nationwide surveillance data set collected by the Ministry of Health contained 10 411 confirmed CCHF cases. We collected potential explanatory covariates about climate, land use, and animal and human populations at risk to capture spatiotemporal transmission dynamics. We developed a structured Gaussian process algorithm and prospectively tested this tool predicting the future year's cases given past years' cases. Results: we predicted the annual cases in 2016 and 2017 as 438 and 341, whereas the observed cases were 432 and 343, respectively. Pearson's correlation coefficient and normalized root mean squared error values for 2016 and 2017 predictions were (0.83; 0.58) and (0.87; 0.52), respectively. The most important covariates were found to be the number of settlements with fewer than 25 000 inhabitants, latitude, longitude and potential evapotranspiration (evaporation and transpiration). Conclusions: main driving factors of CCHF dynamics were human population at risk in rural areas, geographical dependency and climate effect on ticks. Our model was able to prospectively predict the numbers of CCHF cases. Our proof-of-concept study also provided insight for understanding possible mechanisms of infectious diseases and found important directions for practice and policy to combat against emerging infectious diseases.
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    PublicationOpen Access
    Spatiotemporal prediction of infectious diseases using structured Gaussian processes with application to Crimean-Congo hemorrhagic fever
    (Public Library of Science, 2018) Şencan, İrfan; Torunoğlu, Mehmet Ali; N/A; Department of Industrial Engineering; Ak, Çiğdem; Ergönül, Önder; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; School of Medicine; College of Engineering; N/A; 237468
    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.