Link to Session Platform

Chairs(s):

Hui Luan, University of Oregon

; ,

Description:

Mapping disease incidence/prevalence and identifying risk factors have been longstanding areas of interest in public health and spatial epidemiology. The emergence of the COVID-19 pandemic has stimulated extensive research in spatiotemporal disease mapping and analysis, including a flood of dashboard visualizing disease spread over space and time via Web and mobile applications. With innovations in data acquisition and dissemination, and with methodological advances in analyzing complex longitudinal data, contemporary disease mapping research has been increasingly focused on understanding how health varies across space, time, socioeconomic, and demographic groups. There are, however, a number of unresolved and challenging methodological issues in mapping and analyzing spatiotemporal health outcomes and behaviours, including but not limited to:

• Sparse data and noise (e.g., zero-inflation)
• Spatiotemporally misaligned data analysis
• Multiscale modeling
• Spatially and temporally varying regression modeling
• Multivariate modeling of more than one health outcome
• Missing data and imputation
• Spatiotemporal cluster detection
• Disease surveillance
• Uncertainty modelling and quantification
• Human mobility and social distancing in disease spreading
• Geoprivacy
• Geovisual analytics of disease patterns

Non-Presenting Participants Agenda