This application addresses broad Challenge Area (15) Translational science and Specific Challenge Topic: (15-TW-101) Models to predict health effects of climate change. We propose to develop Bayesian hierarchical statistical methods and software that will help spatial analysts to establish relationships among health outcomes and atmospheric and climate predictors. We propose a comprehensive modeling framework to accommodate disparate sources and types of spatial-temporal data.
In Aim 1 we propose a statistical modelling framework for modelling exposure, climate and health outcome data that integrates methods for point-level spatially mis- aligned data and change of support regression using Bayesian hierarchical spatial models. We identify three health outcomes: asthma hospitalizations, incidence of nonmelanoma skin cancer and a food borne disease salmonellosis.
Aim 2 modifies adapts these models for use with large datasets using a dimension reduction stochastic process called the """"""""predictive process"""""""". Finally, in Aim 3, we promise a suite of software packages that help integrate necessary spatial databases and display components with Bayesian statistical modeling ca- pability, thus delivering our methodology to a far broader audience of health and environmental researchers and administrators than is currently accessible. Identifying environmental and climate-related factors that are pronouncedly more detrimental will improve the understanding and decision making process of health researchers, policy makers and patients, thereby having far-reaching beneficial effects on the health care system and society. By redeeming the investigators from using ad-hoc and qualitative methods that often reveal deceptive stories, our proposed statistical methods can have far reaching beneficial effects in public health research that will potentially touch unexpected corners of society.
Identifying environmental and climate-related factors that are pronouncedly more detrimental will improve the understanding and decision making process of health researchers, policy makers and patients, thereby having far-reaching beneficial effects on the health care system and society. By redeeming the investigators from using ad-hoc and qualitative methods that often reveal deceptive stories, our proposed statistical methods can have far reaching beneficial effects in public health research that will potentially touch unexpected corners of society.
| Martinez-Beneito, Miguel A; Botella-Rocamora, Paloma; Banerjee, Sudipto (2017) Towards a Multidimensional Approach to Bayesian Disease Mapping. Bayesian Anal 12:239-259 |
| Gelfand, Alan E; Banerjee, Sudipto (2017) Bayesian Modeling and Analysis of Geostatistical Data. Annu Rev Stat Appl 4:245-266 |
| Banerjee, Sudipto (2017) High-Dimensional Bayesian Geostatistics. Bayesian Anal 12:583-614 |
| Datta, Abhirup; Banerjee, Sudipto; Finley, Andrew O et al. (2016) On nearest-neighbor Gaussian process models for massive spatial data. Wiley Interdiscip Rev Comput Stat 8:162-171 |
| Datta, Abhirup; Banerjee, Sudipto; Finley, Andrew O et al. (2016) NONSEPARABLE DYNAMIC NEAREST NEIGHBOR GAUSSIAN PROCESS MODELS FOR LARGE SPATIO-TEMPORAL DATA WITH AN APPLICATION TO PARTICULATE MATTER ANALYSIS. Ann Appl Stat 10:1286-1316 |
| Quick, Harrison; Carlin, Bradley P; Banerjee, Sudipto (2015) Heteroscedastic CAR models for areally referenced temporal processes for analyzing California asthma hospitalization data. J R Stat Soc Ser C Appl Stat 64:799-813 |
| Botella-Rocamora, P; Martinez-Beneito, M A; Banerjee, S (2015) A unifying modeling framework for highly multivariate disease mapping. Stat Med 34:1548-59 |
| Quick, Harrison; Banerjee, Sudipto; Carlin, Bradley P (2015) Bayesian modeling and analysis for gradients in spatiotemporal processes. Biometrics 71:575-84 |
| Ren, Qian; Banerjee, Sudipto (2013) Hierarchical factor models for large spatially misaligned data: a low-rank predictive process approach. Biometrics 69:19-30 |
| Quick, Harrison; Banerjee, Sudipto; Carlin, Bradley P (2013) MODELING TEMPORAL GRADIENTS IN REGIONALLY AGGREGATED CALIFORNIA ASTHMA HOSPITALIZATION DATA. Ann Appl Stat 7:154-176 |
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