Nonparametric Evaluation of Dynamic Disease Risk: A Spatio-Temporal Kernel Approach

Zhang, Zhijie and Chen, Dongmei and Liu, Wenbao and Racine, Jeffrey S. and Ong, Seng Huat and Chen, Yue and Zhao, Genming and Jiang, Qingwu (2011) Nonparametric Evaluation of Dynamic Disease Risk: A Spatio-Temporal Kernel Approach. PLoS ONE, 6 (3). e17381. ISSN 1932-6203, DOI

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Quantifying the distributions of disease risk in space and time jointly is a key element for understanding spatio-temporal phenomena while also having the potential to enhance our understanding of epidemiologic trajectories. However, most studies to date have neglected time dimension and focus instead on the "average" spatial pattern of disease risk, thereby masking time trajectories of disease risk. In this study we propose a new idea titled "spatio-temporal kernel density estimation (stKDE)" that employs hybrid kernel (i.e., weight) functions to evaluate the spatio-temporal disease risks. This approach not only can make full use of sample data but also "borrows" information in a particular manner from neighboring points both in space and time via appropriate choice of kernel functions. Monte Carlo simulations show that the proposed method performs substantially better than the traditional (i.e., frequency-based) kernel density estimation (trKDE) which has been used in applied settings while two illustrative examples demonstrate that the proposed approach can yield superior results compared to the popular trKDE approach. In addition, there exist various possibilities for improving and extending this method. © 2011 Zhang et al.

Item Type: Article
Uncontrolled Keywords: Algorithms; Burkitt Lymphoma; China; Computer Simulation; Disease; Humans; Monte Carlo Method; Risk Factors; Schistosomiasis; Statistics, Nonparametric; Time Factors; Uganda
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics
Divisions: Faculty of Science > Institute of Mathematical Sciences
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 14 Nov 2019 04:08
Last Modified: 14 Nov 2019 04:08

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