https://doi.org/10.4081/gh.2023.1247
From Snow’s map of cholera transmission to dynamic catchment boundary delineation: current front lines in spatial analysis
Published: 26 October 2023
Abstract Views: 1005
PDF: 467
HTML: 16
HTML: 16
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
The history of mapping infectious diseases dates back to the 19th century when Dr John Snow utilised spatial analysis to pinpoint the source of the 1854 cholera outbreak in London, a ground-breaking work that laid the foundation for modern epidemiology and disease mapping (Newsom, 2006). As technology advanced, so did mapping techniques. In the late 20th century, geographic information systems (GIS) revolutionized disease mapping by enabling researchers to overlay diverse datasets to visualise and analyse complex spatial patterns (Bergquist & Manda 2019; Hashtarkhani et al., 2021). The COVID-19 pandemic showed that disease mapping is particularly valuable for optimising prevention and control strategies of infectious diseases by prioritising geographical targeting interventions and containment strategies (Mohammadi et al., 2021). Today, with the aid of highresolution satellite imagery, geo-referenced electronic data collection systems, real-time data feeds, and sophisticated modelling algorithms, disease mapping has become a feasible and accessible tool for public health officials in tracking, managing, and mitigating the spread of infectious diseases at global, regional and local scales (Hay et al., 2013). [...]
Newsom SWB, 2006.Pioneers in infection control: John Snow, Henry Whitehead, the Broad Street pump, and the beginnings of geographical epidemiology. J Hosp Infect 64:210-6.
DOI: https://doi.org/10.1016/j.jhin.2006.05.020
Bergquist R, Manda S, 2019. The world in your hands: GeoHealth then and now. Geospat Health 14:779.
DOI: https://doi.org/10.4081/gh.2019.779
Hashtarkhani S, Tabatabaei-Jafari H, Kiani B, 2021. Use of geographical information systems in multiple sclerosis research: A systematic scoping review, Mult Scler Relat Disord 51:102909.
DOI: https://doi.org/10.1016/j.msard.2021.102909
Mohammadi A, Mollalo A, Bergquist R, Kiani B, 2021. Measuring COVID-19 vaccination coverage: an enhanced age-adjusted two-step floating catchment area model. Infect Dis Poverty 10:118.
DOI: https://doi.org/10.1186/s40249-021-00904-6
Hay SI, Battle KE, Pigott DM, et al., 2013. Global mapping of infectious disease. Philos Trans R Soc Lond B Biol Sci 368:20120250.
DOI: https://doi.org/10.1098/rstb.2012.0250
Firouraghi N, Bergquist R, Fatima M, et al., 2023. High-risk spatiotemporal patterns of cutaneous leishmaniasis: a nationwide study in Iran from 2011 to 2020. Infect Dis Poverty 12:49.
DOI: https://doi.org/10.1186/s40249-023-01103-1
Kiani B, Raouf Rahmati A, Bergquist R, et al.,2021. Spatio-temporal epidemiology of the tuberculosis incidence rate in Iran 2008 to 2018. BMC Public Health 21:1093.
DOI: https://doi.org/10.1186/s12889-021-11157-1
Mohammadi A, Pishgar E, Fatima M, et al.,2023. The COVID-19 Mortality Rate Is Associated with Illiteracy, Age, and Air Pollution in Urban Neighborhoods: A Spatiotemporal Cross-Sectional Analysis. Trop Med Infect Dis 8:85.
DOI: https://doi.org/10.3390/tropicalmed8020085
Yantzi R, van de Walle G, Lin J, 2019. The disease isn't listening to the drug: The socio-cultural context of antibiotic use for viral respiratory infections in rural Uganda. Glob Public Health 14:750-63.
DOI: https://doi.org/10.1080/17441692.2018.1542017
Tizzoni M, Nsoesie EO, Gauvin L, et al, 2022. Addressing the socioeconomic divide in computational modeling for infectious diseases. Nat Commun 13:2897.
DOI: https://doi.org/10.1038/s41467-022-30688-8
Wangdi K, Sheel M, Fuimaono S, et al, 2022. Lymphatic filariasis in 2016 in American Samoa: Identifying clustering and hotspots using non-spatial and three spatial analytical methods. PLOS Negl Trop Dis 16:e0010262.
DOI: https://doi.org/10.1371/journal.pntd.0010262
MohammadEbrahimi S, Kiani B, Rahmatinejad Z, et al., 2022. Geospatial epidemiology of hospitalized patients with a positive influenza assay: A nationwide study in Iran, 2016–2018. PLoS One 17:e0278900.
DOI: https://doi.org/10.1371/journal.pone.0278900
Fagerlin A, Valley TS, Scherer AM, et al 2017. Communicating infectious disease prevalence through graphics: Results from an international survey. Vaccine 35:4041-7.
DOI: https://doi.org/10.1016/j.vaccine.2017.05.048
Talbi FZ, Nouayti N, El Omari H, et al., 2020. Thematic Maps of the Impact of Urbanization and Socioeconomic Factors on the Distribution of the Incidence of Cutaneous Leishmaniasis Cases in Sefrou Province, Central North of Morocco (2007–2011). Interdiscip Perspect Infect Dis 2020:8673091.
DOI: https://doi.org/10.1155/2020/8673091
Ponce-de-Leon M, del Valle J, Fernandez JM, et al.,2021. COVID-19 Flow-Maps an open geographic information system on COVID-19 and human mobility for Spain. Sci Data 8:310.
DOI: https://doi.org/10.1038/s41597-021-01093-5
Firouraghi N, Mohammadi A, Hamer DH, et al., 2022. Spatio-temporal visualisation of cutaneous leishmaniasis in an endemic, urban area in Iran. Acta Trop 225:106181.
DOI: https://doi.org/10.1016/j.actatropica.2021.106181
Mohidem NA, Osman M, Muharam FM, et al., 2021. Development of a web-geographical information system application for plotting tuberculosis cases. Geospat Health 16:980.
DOI: https://doi.org/10.4081/gh.2021.980
Huber C, Watts A, Grills A, et al., 2022. Modelling airport catchment areas to anticipate the spread of infectious diseases across land and air travel. Spat Spatiotemporal Epidemiol 36:100380.
DOI: https://doi.org/10.1016/j.sste.2020.100380
Pereira RHM, Braga CKV, Servo LM, et al., 2021. Geographic access to COVID-19 healthcare in Brazil using a balanced float catchment area approach. Soc Sci Med 273:113773.
DOI: https://doi.org/10.1016/j.socscimed.2021.113773
Cadavid Restrepo AM, Martin BM, Fuimaono S, et al., 2023. Spatial predictive risk mapping of lymphatic filariasis residual hotspots in American Samoa using demographic and environmental factors. PLoS Negl Trop Dis 17:e0010840.
DOI: https://doi.org/10.1371/journal.pntd.0010840
Helderop E, Nelson JR, Grubesic TH, 2023. ‘Unmasking’ masked address data: A medoid geocoding solution. MethodsX 10:102090.
DOI: https://doi.org/10.1016/j.mex.2023.102090
Owen G, Harris R, Jones K, 2015. Under examination: Multilevel models, geography and health research. Prog Hum Geogr 40:394-412.
DOI: https://doi.org/10.1177/0309132515580814
Kiani B, Mohammadi A, Bergquist R, Bagheri N, 2021. Different configurations of the two-step floating catchment area method for measuring the spatial accessibility to hospitals for people living with disability: a cross-sectional study. Arch Public Health 79:85.
DOI: https://doi.org/10.1186/s13690-021-00601-8
How to Cite
Kiani, B., Lau, C. ., & Bergquist, R. (2023). From Snow’s map of cholera transmission to dynamic catchment boundary delineation: current front lines in spatial analysis. Geospatial Health, 18(2). https://doi.org/10.4081/gh.2023.1247
Copyright (c) 2023 the Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
