Spatiotemporal modeling

This paper reviews some conceptual and practical issues regarding the application of georeferenced data in epidemiologic research. Starting with the disease mapping tradition of geographical medicine, topics such as types of georeferenced data, implications for data analysis, spatial autocorrelation and main analytical approaches are heuristically discussed, relying on examples from the epidemiologic literature, most of them concerning mapping disease distribution, detection of disease spatial clustering, evaluation of exposure in environmental health investigation and ecological correlation studies. As for concluding remarks, special topics that deserve further development, including the misuses of the concept of space in epidemiologic research, issues related to data quality and confidentiality, the role of epidemiologic designs for spatial research, sensitivity analysis and spatiotemporal modeling, are presented.

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Spatiotemporal Modeling and Implementation for Radar-Based Rainfall Estimation

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2016.2597170 ◽

2016 ◽

Vol 13 (11) ◽

pp. 1601-1605 ◽

Cited By ~ 2

Author(s):

Qiuming Kuang ◽

Xuebing Yang ◽

Wensheng Zhang ◽

Guoping Zhang

Keyword(s):

Spatiotemporal Modeling ◽

Rainfall Estimation

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Spatiotemporal Modeling of Correlated Small‐Area Outcomes: Analyzing the Shared and Type‐Specific Patterns of Crime and Disorder

Geographical Analysis ◽

10.1111/gean.12173 ◽

2018 ◽

Vol 51 (2) ◽

pp. 221-248 ◽

Cited By ~ 2

Author(s):

Matthew Quick ◽

Guangquan Li ◽

Jane Law

Keyword(s):

Small Area ◽

Spatiotemporal Modeling

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Community factors associated with local epidemic timing of respiratory syncytial virus: A spatiotemporal modeling study

Science Advances ◽

10.1126/sciadv.abd6421 ◽

2021 ◽

Vol 7 (26) ◽

pp. eabd6421

Author(s):

Zhe Zheng ◽

Virginia E. Pitzer ◽

Joshua L. Warren ◽

Daniel M. Weinberger

Keyword(s):

New York ◽

Respiratory Syncytial Virus ◽

The Elderly ◽

Spatiotemporal Modeling ◽

Geographic Proximity ◽

Community Factors ◽

Harmonic Regression ◽

Syncytial Virus ◽

Zip Code ◽

Epidemic Timing

Respiratory syncytial virus (RSV) causes a large burden of morbidity in young children and the elderly. Spatial variability in the timing of RSV epidemics provides an opportunity to probe the factors driving its transmission, including factors that influence epidemic seeding and growth rates. Using hospitalization data from Connecticut, New Jersey, and New York, we estimated epidemic timing at the ZIP code level using harmonic regression and then used a Bayesian meta-regression model to evaluate correlates of epidemic timing. Earlier epidemics were associated with larger household size and greater population density. Nearby localities had similar epidemic timing. Our results suggest that RSV epidemics grow faster in areas with more local contact opportunities, and that epidemic spread follows a spatial diffusion process based on geographic proximity. Our findings can inform the timing of delivery of RSV extended half-life prophylaxis and maternal vaccines and guide future studies on the transmission dynamics of RSV.

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