Space-time disease mapping by combining Bayesian maximum entropy and Kalman filter: the BME-Kalman approach. Issue 3 (4th March 2021)

• Record Type: Journal Article
• Authors: Hu, Bisong; Ning, Pan; Li, Yi; Xu, Chengdong; Christakos, George; Wang, Jinfeng
• Is Part Of: International journal of geographical information science. Volume 35:Issue 3(2021)
• Page Start: 466
• Page End: 489
• Publication Date: 2021-03-04
• Subjects: Bayesian maximum entropy -- Kalman filter -- geostatistics -- space-time analysis -- hand -- foot and mouth disease; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2020.1795177
• Languages: English
• ISSNs: 1365-8816

Abstract

ABSTRACT: In this work, a synthesis of the Bayesian maximum entropy (BME) and the Kalman filter (KF) methods, which enhances their individual strengths and overcomes certain of their weaknesses for spatiotemporal mapping purposes, is proposed in a spatiotemporal disease mapping context. The proposed BME-Kalman synthesis allows BME to use information from both parametric regression modeling and KF estimation leading to enhanced knowledge bases. The BME-Kalman synthetic approach is used to study the space-time incidence mapping of the hand, foot and mouth disease (HFMD) in Shandong province (China) during the period May 1 st, 2008 to March 19 th, 2009. The results showed that the BME-Kalman approach exhibited very good regressive and predictive accuracies, maintained a very good performance even during low-incidence and extremely low-incidence periods, offered an improved description of hierarchical disease characteristics compared to traditional mapping techniques, and provided a clear explanation of the spatial stratified incidence heterogeneity at unsampled locations. The BME-Kalman approach is versatile and flexible so that it can be modified and adjusted according to the needs of the application.