Identifying Spatiotemporal Clusters by Means of Agglomerative Hierarchical Clustering and Bayesian Regression Analysis with Spatiotemporally Varying Coefficients: Methodology and Application to Dengue Disease in Bandung, Indonesia

Abstract

Dengue disease has serious health and socioeconomic consequences. Preventive actions are needed to avoid outbreaks. Bayesian spatiotemporal models with conditional autoregressive (CAR) and random walk (RW) priors are two common smoothing approaches used in disease mapping to develop early warning systems and action plans. However, this approach can lead to over-smoothing, such that discontinuities in the risk surface are "averaged" out. Moreover, local variation in the relationship between disease risk and spatiotemporal risk factors can be concealed by the assumption of homogeneous regression effects. To overcome these problems, we propose a two-stage spatiotemporal clustering approach. In the first stage, an agglomerative hierarchical clustering algorithm is applied to map out the complete set of cluster configurations. In the second stage, the optimal cluster configuration is selected using a spatiotemporal model with spatiotemporally varying coefficients. The methodology is applied to data for 30 districts in the city of Bandung, Indonesia, for the period January 2012 to December 2016. Weather factors are used as the risk factors. High-risk and low-risk clusters are identified that strongly vary across space and time, with sunshine duration, precipitation, evaporation, and humidity as the most important risk factors.

Spatiotemporal high-resolution prediction and mapping: methodology and application to dengue disease

“…The expected rate is calculated using external standardization. It is defined based on the overall average across all areas and periods (Abente et al 2018 ; Jaya and Folmer 2020 , 2021a , b ): …”

“…Following Moraga et al ( 2017 ), Jaya and Folmer ( 2020 , 2021a , b ), and Utazi et al ( 2019 ), we model the relative risk as a non-separable Poisson log-linear model as follows 6 : …”

“…High-resolution information is needed for research on the etiology of the disease and the development of control and prevention strategies (Ak et al 2018 ; Jaya et al 2017 ; Jaya and Folmer 2020 , 2021a , b ; Pokharel and Deardon 2016 ). Dengue disease typically involves a spatiotemporal pattern (Jaya and Folmer 2020 , 2021a , b ; Phanitchat et al 2019 ; Puggioni et al 2020 ); therefore, high-resolution spatiotemporal models and maps of the distribution of relative risk are basic elements of an early warning system aimed at identifying where and when an outbreak will occur (Hanigan et al 2019 ; Shi et al 2013 ; Xu et al 2019 ).…”

Bayesian spatiotemporal forecasting and mapping of COVID‐19 risk with application to West Java Province, Indonesia

“…with λ taken to be 1 (Fahrmeir & Kneib, 2011) (d) The combination of inverse distance and fixed distance nearest neighbor distance. We evaluated the spatial weights matrices W for Model M4 using the following model selection criteria: the deviance information criterion (DIC), the Watanabe-Akaike information criterion (WAIC), the marginal predictive likelihood (MPL), mean absolute error (MAE), the root mean square error (RMSE), the correlation between observed and predicted values (r), and the spatiotemporal Moran I statistic (MoranST) for residual spatiotemporal autocorrelation (see Jaya and Folmer (2020b) for details on the MoranST). We ran the 15 models in INLA, and report the results in Table B5.…”

Regionalization and Trend Analysis of Seasonal Precipitation (1970 - 2020) using Hierarchical Clustering and Wavelet Transform Approach in the Mahi River Basin, India

“…In this, all the stored observations make their cluster and merge until all similar information cluster in one. Several recent (Hu et al, 2019;Jaya et al, 2020) studies have used this clustering and indicated that this method is much easier to utilize. Some important applications using the clustering method were discussed in Bouguettaya et al 2015.…”