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An Autoregressive Disease Mapping Model for Spatio-Temporal Forecasting
- Source :
- Mathematics, Volume 9, Issue 4, Mathematics, Vol 9, Iss 384, p 384 (2021)
- Publication Year :
- 2021
- Publisher :
- Multidisciplinary Digital Publishing Institute, 2021.
-
Abstract
- One of the more evident uses of spatio-temporal disease mapping is forecasting the spatial distribution of diseases for the next few years following the end of the period of study. Spatio-temporal models rely on very different modeling tools (polynomial fit, splines, time series, etc.), which could show very different forecasting properties. In this paper, we introduce an enhancement of a previous autoregressive spatio-temporal model with particularly interesting forecasting properties, given its reliance on time series modeling. We include a common spatial component in that model and show how that component improves the previous model in several ways, its predictive capabilities being one of them. In this paper, we introduce and explore the theoretical properties of this model and compare them with those of the original autoregressive model. Moreover, we illustrate the benefits of this new model with the aid of a comprehensive study on 46 different mortality data sets in the Valencian Region (Spain) where the benefits of the new proposed model become evident.
- Subjects :
- mortality studies
Computer science
General Mathematics
0208 environmental biotechnology
disease mapping
forecasting
02 engineering and technology
Machine learning
computer.software_genre
Bayesian statistics
01 natural sciences
spatial statistics
010104 statistics & probability
Component (UML)
Computer Science (miscellaneous)
0101 mathematics
Engineering (miscellaneous)
Spatial analysis
spatio-temporal statistics
Polynomial regression
Series (mathematics)
business.industry
lcsh:Mathematics
lcsh:QA1-939
020801 environmental engineering
Time series modeling
Autoregressive model
Mortality data
Artificial intelligence
business
computer
Subjects
Details
- Language :
- English
- ISSN :
- 22277390
- Database :
- OpenAIRE
- Journal :
- Mathematics
- Accession number :
- edsair.doi.dedup.....53dbdb063f6b380e0d5a194be733f754
- Full Text :
- https://doi.org/10.3390/math9040384