Guixing Wei is a Senior Research Scientist in Spatial Structures in the Social Sciences (S4) at Brown University. Wei's research focuses on health geography, spatial epidemiology, and the development of novel methods in health- and demography-related applications. The main research questions Wei has been trying to answer include: (1). how to conceptualize, measure, and model geographically relevant variables, (2). how to represent and model a spatial pattern and identify the areas of elevated risk, (3) how geographic risk factors along with socioeconomic factors influence adverse health outcomes (e.g., skin cancer).
Wei's current research projects include the development of Bayesian hierarchical spatial models to identify the underlying risk factors for health and income inequality in low- and middle-income countries, the understanding of the spatial pattern of emergency medical service (EMS) calls in Rhode Island, and the investigation of the impact of tanning beds to Melanoma incidence rates. Wei is also a highly collaborative researcher. Wei also helped secure an NSF grant to establish "The Center for Mobility Analysis for Pandemic Prevention Strategies (MAPPS)” in 2022 and has been leading the Thrust 1 team to develop a comprehensive mobility and social mixing database.
Bayesian hierarchical spatial model development and its application in health- and demography-related fields. The Demographic and Health Surveys (DHS) Program has been collecting and disseminating demographic and health information for over 90 countries for decades. In recent years, these data have been augmented with spatial information. Interest in using this spatial information to investigate how population and health outcomes are affected by geographical factors has been growing. However, most investigations to date have been limited in their spatial analysis capabilities due to the complexity of the inherent hierarchical structure of the DHS data and the absence of household-level geographic coordinates. To harness the value of DHS spatial data, practical approaches investigating spatial modeling with multilevel modeling to account for the hierarchical structure and spatial autocorrelation in DHS data are needed. To this end, Wei investigates a series of multilevel spatially explicit models and finds evidence that multilevel spatially explicit models outperform a-spatial models. Along the line, Wei also aims to identify and evaluate the underlying risk factors while accounting for the spatial autocorrelation and hierarchy in data.
- Wei G, White MJ, Short S. “Bayesian spatial multilevel modeling of Demographic and Health Surveys (DHS) data.” Int J Geogr Inf Sci [Under Review]
Understanding the 911 emergency calls from a geospatial perspective. Infectious diseases, including COVID-19, have a severe impact on child health globally. We investigated whether emergency medical service (EMS) calls are a bellwether for future COVID-19 caseload. Wei and his collaborators elaborated on geographical hotspots and socioeconomic risk factors.
- Kienbacher CL,Wei G, Rhodes JM, Herkner H, Roth D,Williams KA. “Risk Factors for pediatric Intoxications in the prehospital setting. A geospatial Survey” World J Emerg Med [Under Review]
- Kienbacher CL,Wei G, Rhodes J, Herkner H,Williams KA. “Socioeconomic Risk Factors for Pediatric Out-of-hospital Cardiac Arrest: A Statewide Analysis.”West J Emerg Med. 2023; 24 doi:10.5811/westjem.2023.2.59107
- Kienbacher CL, Tanzer JR,Wei G, Rhodes JM, Roth D,Williams KA.“Increases in Ambulance Call Volume Are an EarlyWarning Sign of Major COVID-19 Surges in Children.” Int J Environ Res Public Health. 2022 Dec 2;19(23):16152. doi: 10.3390/ijerph192316152. PMID: 36498225; PMCID: PMC9736099.
- Human mobility modeling and its integration with epidemic modeling. Infectious diseases naturally follow the movement of people. Once a transmissible pathogen emerges, human mobility and patterns of social mixing largely govern its spread. Thus, measurements of social mixing and human mobility, with adequate spatiotemporal resolution, are critical to identifying the potential for the pandemic spread of novel pathogens. Despite the increased availability of wearable and other devices, there is a paucity of methods to gather, synthesize, and analyze real-time human mobility and social networking data. Wei and his collaborators from Brown School of Public Health are establishing "The Center for Mobility Analysis for Pandemic Prevention Strategies (MAPPS)". The primary objective of the Center will be to identify, measure, and analyze key elements of migration, social mixing, and biometric data that are critical to our understanding of how diseases spread through populations and essential if we are to have the tools to predict and prevent future pandemics.
- Workshops on GIS spatial analysis, Python, R
- Consulting on GIS spatial analysis, geographic data management, and web mapping