The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Spatial analysis and GIS in the study of COVID-19. A review
Open accessAbstract
This study entailed a review of 63 scientific articles on geospatial and spatial-statistical analysis of the geographical dimension of the 2019 coronavirus disease (COVID-19) pandemic. The diversity of themes identified in this paper can be grouped into the following categories of disease mapping: spatiotemporal analysis, health and social geography, environmental variables, data mining, and web-based mapping. Understanding the spatiotemporal dynamics of COVID-19 is essential for its mitigation, as it helps to clarify the extent and impact of the pandemic and can aid decision making, planning and community action. Health geography highlights the interaction of public health officials, affected actors and first responders to improve estimations of disease propagation and likelihoods of new outbreaks. Attempts at interdisciplinary correlation examine health policy interventions for the siting of health/sanitary services and controls, mapping/tracking of human movement, formulation of appropriate scientific and political responses and projection of spatial diffusion and temporal trends. This review concludes that, to fight COVID-19, it is important to face the challenges from an interdisciplinary perspective, with proactive planning, international solidarity and a global perspective. This review provides useful information and insight that can support future bibliographic queries, and also serves as a resource for understanding the evolution of tools used in the management of this major global pandemic of the 21 Century. It is hoped that its findings will inspire new reflections on the COVID-19 pandemic by readers.
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Cites
Clinical characteristics of coronavirus disease 2019 in China
Guan, W.-J., Ni, Z.-Y., Hu, Y., Liang, W.-H., Ou, C.-Q., He, J.-X., Liu, L., Shan, H., Lei, C.-L., Hui, D. S. C., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2002032 · Cited by 30,246
An interactive web-based dashboard to track COVID-19 in real time
Dong, E., Du, H., & Gardner, L. (2020) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(20)30120-1 · Cited by 10,048
Holmes, E. A., O'Connor, R. C., Perry, V. H., Tracey, I., Wessely, S., Arseneault, L., Ballard, C., Christensen, H., Cohen Silver, R., Everall, I., et al. (2020) · The Lancet Psychiatry · doi:10.1016/s2215-0366(20)30168-1 · Cited by 5,715
Pham, Q.-V., Nguyen, D. C., Huynh-The, T., Hwang, W.-J., & Pathirana, P. N. (2020) · IEEE Access · doi:10.1109/access.2020.3009328 · Cited by 413 · cited 2×
“Such studies could help us to understand a pandemic's behaviour in terms of outbreak tracking, disease treatment and future vaccine manufacture and distribution (Pham et al., 2020).”
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Cited by
FedSN: A Federated Learning Framework Over Heterogeneous LEO Satellite Networks
Lin, Z., Chen, Z., Fang, Z., Chen, X., Wang, X., & Gao, Y. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2024.3481275 · Cited by 66
Ndlovu, S. (2023) · Sustainability · doi:10.3390/su15021420 · Cited by 11
Gerbino, K. R., Borin, J. M., Ardell, S. M., Lee, J. J., Corbett, K. D., & Meyer, J. R. (2024) · Virus Evolution · doi:10.1093/ve/veae049 · Cited by 7
Metadata
- Authors
- Franch-Pardo, I., Napoletano, B. M., Rosete-Verges, F., & Billa, L.
- Venue
- Science of The Total Environment
- Publisher
- Elsevier BV
- Published
- 2020-10
- Volume
- 739
- Pages
- 140033
- ISSN
- 0048-9697 (ISSN-L)
- OpenAlex
- W3033503930
- Cited by
- 655
- References
- 4
- FWCI
- 19.61
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Franch-Pardo, I., Napoletano, B. M., Rosete-Verges, F., & Billa, L. (2020). Spatial analysis and GIS in the study of COVID-19. A review. Science of The Total Environment. https://doi.org/10.1016/j.scitotenv.2020.140033
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.