Spatial and Spatio-temporal Epidemiology
Published by Elsevier
ISSN : 1877-5845 eISSN : 1877-5853
Abbreviation : Spat. Spatio-temporal Epidemiology
Aims & Scope
Spatial and Spatio-Temporal Epidemiology is a peer-reviewed scientific journal that provides a home for high quality work which straddles the areas of GIS, epidemiology, exposure science, and spatial statistics.
The journal focuses on answering epidemiological questions where spatial and spatio-temporal approaches are appropriate.
The methods should help to advance our understanding of infectious and non-infectious diseases in humans.
The journal will also consider applications where health care provision is the focus.
Coverage of veterinary topics will be included, and those with direct human health implications are especially welcome.
The journal places special emphasis on spatio-temporal aspects of emerging diseases (e.g., avian flu, SARS), development of spatial statistical and computational methods, and novel applications of geospatial technology (e.g., GPS, GIS) for shedding insights on exposure and disease processes.
The journal accepts three different types of submissions: 1.
Methods papers that outline new methodology in the areas of GIS, spatial statistics, exposure science, and/or epidemiology; 2.
Case Study/Applications papers where recently developed methodology is applied to novel applications with a clear exposure/disease focus; and 3.
Short reports where a) they are around 4-8 text pages in length b) they focus on an important novel development and c) the development should be capable of description within the page length.
Case Studies progress reports in the form of 'what we have done so far' are not acceptable as Short Reports unless they comply with b) or c) above.
View Aims & ScopeMetrics & Ranking
Impact Factor
| Year | Value |
|---|---|
| 2025 | 1.7 |
| 2024 | 2.10 |
SJR (SCImago Journal Rank)
| Year | Value |
|---|---|
| 2024 | 0.601 |
Quartile
| Year | Value |
|---|---|
| 2024 | Q2 |
h-index
| Year | Value |
|---|---|
| 2024 | 35 |
Journal Rank
| Year | Value |
|---|---|
| 2024 | 9659 |
Journal Citation Indicator
| Year | Value |
|---|---|
| 2024 | 367 |
Impact Factor Trend
Abstracting & Indexing
Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.
Subjects & Keywords
Journal’s research areas, covering key disciplines and specialized sub-topics in Environmental Science, Medicine and Social Sciences, designed to support cutting-edge academic discovery.
Most Cited Articles
The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.
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Spatial and spatio-temporal models with R-INLA
Citation: 303
Authors: Marta, Michela, Gianluca, HÃ¥vard
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Spatial analysis of COVID-19 clusters and contextual factors in New York City
Citation: 219
Authors: Jack, Marcia C.
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Spatial and spatio-temporal models with R-INLA
Citation: 209
Authors: Marta, Michela, Gianluca, HÃ¥vard
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A comparison of conditional autoregressive models used in Bayesian disease mapping
Citation: 199
Authors: Duncan
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Daily surveillance of COVID-19 using the prospective space-time scan statistic in the United States
Citation: 132
Authors: Alexander, Eric M., Michael R., Yu
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Bayesian hierarchical spatial models: Implementing the Besag York Mollié model in stan
Citation: 132
Authors: Mitzi, Katherine, Dan, Stephen J., Andrew, Charles
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Risk factor modelling of the spatio-temporal patterns of highly pathogenic avian influenza (HPAIV) H5N1: A review
Citation: 119
Authors: Marius, Dirk U.
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A spatio-temporal model for estimating the long-term effects of air pollution on respiratory hospital admissions in Greater London
Citation: 117
Authors: Alastair, Duncan, Richard
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Review of methods for space–time disease surveillance
Citation: 115
Authors: Colin, Trisalyn A., Ying C., Andrew B.
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Assessment of land use factors associated with dengue cases in Malaysia using Boosted Regression Trees
Citation: 112
Authors: Yoon Ling, Pedro J., Tobia