Construction of a Seasonal Difference-Geographically and Temporally Weighted Regression (SD-GTWR) Model and Comparative Analysis with GWR-Based Models for Hemorrhagic Fever with Renal Syndrome (HFRS) in Hubei Province (China)

Abstract

Hemorrhagic fever with renal syndrome (HFRS) is considered a globally distributed infectious disease which results in many deaths annually in Hubei Province, China. In order to conduct a better analysis and accurately predict HFRS incidence in Hubei Province, a new model named Seasonal Difference-Geographically and Temporally Weighted Regression (SD-GTWR) was constructed. The SD-GTWR model, which integrates the analysis and relationship of seasonal difference, spatial and temporal characteristics of HFRS (HFRS was characterized by spatiotemporal heterogeneity and it is seasonally distributed), was designed to illustrate the latent relationships between the spatio-temporal pattern of the HFRS epidemic and its influencing factors. Experiments from the study demonstrated that SD-GTWR model is superior to traditional models such as GWR-based models in terms of the efficiency and the ability of providing influencing factor analysis.

Using geographically weighted regression to explore neighborhood‐level predictors of domestic abuse in the UK

“…Geographically weighted regression (GWR) offers a methodology in which to explore this variation, and this is the approach taken in the present research. GWR has been used to explore a wide range of phenomena, ranging from mosquitos (Ge et al, ; Lin & Wen, ) to obesity (Chalkias et al, ; Wen, Chen, & Tsai, ), participation in higher education (Harris, Singleton, Grose, Brunsdon, & Longley, ), and school attainment (Fotheringham, Charlton, & Brunsdon, ). There have only been a small number of studies where GWR has been used to understand crime.…”

Geographically and Temporally Weighted Regression Modeling in Analyzing Factors Affecting the Spread of Dengue Fever in Malang

“…By using ISA [9], GTWR modeling has bandwidth Optimum h = 3947.54. Table 2 shows the estimated estimation of the GTWR model parameters.…”

“…Where, h is the constant bandwidth performed by the Incremental Spatial Autocorrelation (ISA) method [9]. The Adaptive Gaussian kernel function is used in forming a weighted matrix.…”

A new Seasonal Difference Space-Time Autoregressive Integrated Moving Average (SD-STARIMA) model and spatiotemporal trend prediction analysis for Hemorrhagic Fever with Renal Syndrome (HFRS)

“…In China, a Kulldorff spatial scan statistic has been used to try and identify the clustering of HFRS, drawing upon data spanning the period 1980 to 2009 [ 17 ]. A Gaussian GWR model has also been used to try and identify the factors influencing HFRS transmission (such as meteorological factors, rodent density, surface mean elevation, water area and human population density) drawing upon data from Hubei that was collected between 2011 and 2015 [ 18 ]. Moran’s I index was adopted for a global spatial autocorrelation analysis that sought to identify the overall spatiotemporal pattern of HFRS outbreaks in Hubei between 2005 and 2014, and Spearman's rank correlation analysis was used at the same time to explore the possible factors influencing the epidemics, such as the weather and the area’s geography [ 19 ].…”

“…Research has indicated that HFRS has a characteristic seasonal or cyclic time series-based occurrence [ 42 , 11 ]. In our previous work, a Seasonal Difference—Geographically and Temporally Weighted Regression (SD-GTWR) model was developed as an extension of the GTWR model that sought to use seasonal difference to get stabilized data [ 43 ]. Seasonal difference was used to deal with a non-stationary time series with seasonal distribution characteristics.…”