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

Ak, Çiğdem; Ergönül, Önder; Şencan, İrfan; Torunoğlu, Mehmet Ali; Gönen, Mehmet

doi:10.1371/journal.pntd.0006737

Cited by 23 publications

(23 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Monthly data covering 14 years and comprising >10 000 cases could be valuable for understanding the spatiotemporal dynamics of disease spread. We have already presented the improved performance of a structured Gaussian process (GP), against frequently used machinelearning algorithms used in ecological and epidemiological applications [13]. Here we describe the spatiotemporal dynamics of CCHF and extract the important covariates for CCHF virus infection using a structured GP method on the surveillance data set for Turkey.…”

Section: Introductionmentioning

confidence: 99%

A prospective prediction tool for understanding Crimean–Congo haemorrhagic fever dynamics in Turkey

Ergönül

Gönen

2020

Clinical Microbiology and Infection

View full text Add to dashboard Cite

a b s t r a c tObjectives: We aimed to develop a prospective prediction tool on CrimeaneCongo 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. Ç. Ak, Clin Microbiol Infect 2020;26:123.e1e123.e7

show abstract

Section: Introductionmentioning

confidence: 99%

A prospective prediction tool for understanding Crimean–Congo haemorrhagic fever dynamics in Turkey

Ergönül

Gönen

2020

Clinical Microbiology and Infection

View full text Add to dashboard Cite

show abstract

“…Ak, Ergonul, Sencan, Torunoglu and Gonen [28] The time and space prediction of an infectious diseases Luttinen and Ilin [29] Sea level temperature reconstruction Nguyen and Peters [30] Kinetics model estimation Nguyen, Hu and Spanos [31] Efficient building field formation by estimating indoor environment fields Chen, Qian, Meng and Nabney [32] Wind prediction for energy efficiency GPR has been used in many fields, as it has high prediction measurement for changing environment based on a Bayesian framework, and enables probabilistic analyses using Gaussian distribution. Figure 2 illustrates the process for estimating the final output value Y * using an f * at the input value X * after producing the intermediate value f i by Bayesian renewal to convert the input value X i to the observed value Y i .…”

Section: Research Studies Using Gpr Application Fieldsmentioning

confidence: 98%

Development of a Convolution-Based Multi-Directional and Parallel Ant Colony Algorithm Considering a Network with Dynamic Topology Changes

Lee

2019

Applied Sciences

View full text Add to dashboard Cite

While network path generation has been one of the representative Non-deterministic Polynomial-time (NP)-hard problems, changes of network topology invalidate the effectiveness of the existing metaheuristic algorithms. This research proposes a new and efficient path generation framework that considers dynamic topology changes in a complex network. In order to overcome this issue, Multi-directional and Parallel Ant Colony Optimization (MPACO) is proposed. Ant agents are divided into several groups and start at different positions in parallel. Then, Gaussian Process Regression (GPR)-based pheromone update method makes the algorithm more efficient. While the proposed MPACO algorithm is more efficient than the existing ACO algorithm, it is limited in a network with topological changes. In order to overcome the issue, the MPACO algorithm is modified to the Convolution MPACO (CMPACO) algorithm. The proposed algorithm uses the pheromone convolution method using a discrete Gaussian distribution. The proposed pheromone updating method enables the generation of a more efficient network path with comparatively less influence from topological network changes. In order to show the effectiveness of CMPACO, numerical networks considering static and dynamic conditions are tested and compared. The proposed CMPACO algorithm is considered a new and efficient parallel metaheuristic method to consider a complex network with topological changes.

show abstract

Section: Research Studies Using Gpr Application Areasmentioning

confidence: 99%

“…Jochem et al [20] Automated spectral band analysis Ak et al [21] The time and space prediction of an infectious diseases Luttinen and Ilin [22] Sea level temperature reconstruction using GPR Nguyen and Peters [23] Kinetics model estimation Nguyen, Hu and Spanos [24] Efficient building field formation using an estimation of indoor environment fields Chen et al [25] Wind prediction for energy efficiency Oh and Lee [26] Estimation of pheromone values based on ant colony optimization Figure 3 depicts the general process of GPR. The latent variable is derived from the input value with the observed value .…”

Section: Research Studies Using Gpr Application Areasmentioning

confidence: 99%

See 1 more Smart Citation

An Imbalanced Data Handling Framework for Industrial Big Data Using a Gaussian Process Regression-Based Generative Adversarial Network

Lee

2020

Symmetry

View full text Add to dashboard Cite

The developments in the fields of industrial Internet of Things (IIoT) and big data technologies have made it possible to collect a lot of meaningful industrial process and quality-based data. The gathered data are analyzed using contemporary statistical methods and machine learning techniques. Then, the extracted knowledge can be used for predictive maintenance or prognostic health management. However, it is difficult to gather complete data due to several issues in IIoT, such as devices breaking down, running out of battery, or undergoing scheduled maintenance. Data with missing values are often ignored, as they may contain insufficient information from which to draw conclusions. In order to overcome these issues, we propose a novel, effective missing data handling mechanism for the concepts of symmetry principles. While other existing methods only attempt to estimate missing parts, the proposed method generates a whole set of data set using Gaussian process regression and a generative adversarial network. In order to prove the effectiveness of the proposed framework, we examine a real-world, industrial case involving an air pressure system (APS), where we use the proposed method to make quality predictions and compare the results with existing state-of-the-art estimation methods.

show abstract

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

Cited by 23 publications

References 25 publications

A prospective prediction tool for understanding Crimean–Congo haemorrhagic fever dynamics in Turkey

A prospective prediction tool for understanding Crimean–Congo haemorrhagic fever dynamics in Turkey

Development of a Convolution-Based Multi-Directional and Parallel Ant Colony Algorithm Considering a Network with Dynamic Topology Changes

An Imbalanced Data Handling Framework for Industrial Big Data Using a Gaussian Process Regression-Based Generative Adversarial Network

Contact Info

Product

Resources

About