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Abstract

Background

The potential impact of urban structure, as population density and proximity to essential facilities, on spatial variability of infectious disease cases remains underexplored.

Aim

To analyse the spatial variation of COVID-19 case intensity in relation to population density and distance from urban facilities (as potential contagion hubs), by comparing Alpha and Omicron wave data representing periods of both enacted and lifted non-pharmaceutical interventions (NPIs) in Málaga.

Methods

Using spatial point pattern analysis, we examined COVID-19 cases in relation to population density, distance from hospitals, health centres, schools, markets, shopping malls, sports centres and nursing homes by non-parametric estimation of relative intensity dependence on these covariates. For statistical significance and effect size, we performed Berman 1 tests and Areas Under Curves (AUC) for Receiver Operating Characteristic (ROC) curves.

Results

After accounting for population density, relative intensity of COVID-19 remained consistent in relation to distance from urban facilities across waves. Although non-parametric estimations of the relative intensity of cases showed fluctuations with distance from facilities, Berman’s Z1 tests were significant for health centres only (p < 0.032) when compared with complete spatial randomness. The AUC of ROC curves for population density was above 0.75 and ca 0.6 for all urban facilities.

Conclusion

Results reflect the difficulty in assessing facilities’ effect in propagating infectious disease, particularly in compact cities. Lack of evidence directly linking higher case intensity to proximity to urban facilities shows the need to clarify the role of urban structure and planning in shaping the spatial distribution of epidemics within cities.

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2025-01-23
2025-01-24
/content/10.2807/1560-7917.ES.2025.30.3.2400174
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References

  1. World Health Organization (WHO). Urban health. Geneva: WHO. [Accessed: 22 Jul 2024]. Available from: https://www.who.int/health-topics/urban-health
  2. World Bank Group. Urban Development. Urban Development. [Accessed: 22 Jul 2024]. Available from: https://www.worldbank.org/en/topic/urbandevelopment/overview
  3. United Nations. World Urbanization Prospects. The 2018 Revision. New York: United Nations; 2019. Available from: https://population.un.org/wup/Publications/Files/WUP2018-Report.pdf
  4. Working group for the surveillance and control of COVID-19 in SpainMembers of the Working group for the surveillance and control of COVID-19 in Spain. The first wave of the COVID-19 pandemic in Spain: characterisation of cases and risk factors for severe outcomes, as at 27 April 2020. Euro Surveill. 2020;25(50):2001431. PMID: 33334400 
  5. Orea L, Álvarez IC. How effective has the Spanish lockdown been to battle COVID-19? A spatial analysis of the coronavirus propagation across provinces. Health Econ. 2022;31(1):154-73.  https://doi.org/10.1002/hec.4437  PMID: 34689385 
  6. Walport MJ, Professor Sir Mark Walport on behalf of the Expert Working Group for the Royal Society’s programme on non-pharmaceutical interventions. Executive Summary to the Royal Society report "COVID-19: examining the effectiveness of non-pharmaceutical interventions". Philos Trans A Math Phys Eng Sci. 2023;381(2257):20230211.  https://doi.org/10.1098/rsta.2023.0211  PMID: 37611626 
  7. Brauner JM, Mindermann S, Sharma M, Johnston D, Salvatier J, Gavenčiak T, et al. Inferring the effectiveness of government interventions against COVID-19. Science. 2021;371(6531):802.  https://doi.org/10.1126/science.abd9338  PMID: 33323424 
  8. Li Y, Campbell H, Kulkarni D, Harpur A, Nundy M, Wang X, et al. , Usher Network for COVID-19 Evidence Reviews (UNCOVER) group. The temporal association of introducing and lifting non-pharmaceutical interventions with the time-varying reproduction number (R) of SARS-CoV-2: a modelling study across 131 countries. Lancet Infect Dis. 2021;21(2):193-202.  https://doi.org/10.1016/S1473-3099(20)30785-4  PMID: 33729915 
  9. Baker RE, Park SW, Yang W, Vecchi GA, Metcalf CJE, Grenfell BT. The impact of COVID-19 nonpharmaceutical interventions on the future dynamics of endemic infections. Proc Natl Acad Sci USA. 2020;117(48):30547-53.  https://doi.org/10.1073/pnas.2013182117  PMID: 33168723 
  10. Liu Y, Morgenstern C, Kelly J, Lowe R, Jit M, CMMID COVID-19 Working Group. The impact of non-pharmaceutical interventions on SARS-CoV-2 transmission across 130 countries and territories. BMC Med. 2021;19(1):40.  https://doi.org/10.1186/s12916-020-01872-8  PMID: 33541353 
  11. Evert KJ. Ballard (deceased) EB, Elsworth DJ, Oquiñena I, Schmerber JM, Stipe (deceased) RE, editors. 4716 public infrastructure [n]. In Encyclopedic dictionary of landscape and urban planning. Berlin, Heidelberg: Springer Berlin Heidelberg; 2010:771.
  12. Wang J, Zeng F, Tang H, Wang J, Xing L. Correlations between the urban built environmental factors and the spatial distribution at the community level in the reported COVID-19 samples: A case study of Wuhan. Cities. 2022;129:103932.  https://doi.org/10.1016/j.cities.2022.103932  PMID: 35975194 
  13. Alidadi M, Sharifi A, Murakami D. Tokyo’s COVID-19: An urban perspective on factors influencing infection rates in a global city. Sustain Cities Soc. 2023;97:104743.  https://doi.org/10.1016/j.scs.2023.104743  PMID: 37397232 
  14. Galacho-Jiménez FB, Carruana-Herrera D, Molina J, Ruiz-Sinoga JD. Evidence of the Relationship between Social Vulnerability and the Spread of COVID-19 in Urban Spaces. Int J Environ Res Public Health. 2022;19(9):5336.  https://doi.org/10.3390/ijerph19095336  PMID: 35564729 
  15. Galacho-Jiménez FB, Carruana-Herrera D, Molina J, Ruiz-Sinoga JD. Tempo-Spatial Modelling of the Spread of COVID-19 in Urban Spaces. Int J Environ Res Public Health. 2022;19(15):9764.  https://doi.org/10.3390/ijerph19159764  PMID: 35955122 
  16. Jesri N, Saghafipour A, Koohpaei A, Farzinnia B, Jooshin MK, Abolkheirian S, et al. Mapping and Spatial Pattern Analysis of COVID-19 in Central Iran Using the Local Indicators of Spatial Association (LISA). BMC Public Health. 2021;21(1):2227.  https://doi.org/10.1186/s12889-021-12267-6  PMID: 34876066 
  17. Kuznetsov A, Sadovskaya V. Spatial variation and hotspot detection of COVID-19 cases in Kazakhstan, 2020. Spat Spatio-Temporal Epidemiol. 2021;39(Nov):100430.  https://doi.org/10.1016/j.sste.2021.100430  PMID: 34774254 
  18. Li H, Li H, Ding Z, Hu Z, Chen F, Wang K, et al. Spatial statistical analysis of Coronavirus Disease 2019 (Covid-19) in China. Geospat Health. 2020;15(1):11-8.  https://doi.org/10.4081/gh.2020.867  PMID: 32575956 
  19. Vilinová K, Petrikovičová L. Spatial Autocorrelation of COVID-19 in Slovakia. Trop Med Infect Dis. 2023;8(6):298.  https://doi.org/10.3390/tropicalmed8060298  PMID: 37368716 
  20. Baddeley A, Chang YM, Song Y, Turner R. Nonparametric estimation of the dependence of a spatial point process on spatial covariates. Stat Interface. 2012;5(2):221-36.  https://doi.org/10.4310/SII.2012.v5.n2.a7 
  21. Noordzij M, Dekker FW, Zoccali C, Jager KJ. Measures of disease frequency: prevalence and incidence. Nephron Clin Pract. 2010;115(1):c17-20.  https://doi.org/10.1159/000286345  PMID: 20173345 
  22. Baddeley A, Rubak E, Turner R. Spatial Point Patterns. Methodology and Applications with R. Florida: Chapman and Hall / CRC Interdiscplinary Statistics Series; 2016.  https://doi.org/10.1201/b19708  https://doi.org/10.1201/b19708 
  23. Roselló MJP, Barrionuevo JFS, Prados FJC, Noblejas HC, De la Fuente Roselló AL, Orellana-Macías JM, et al. Potential of hazard mapping as a tool for facing COVID-19 transmission: The geo-COVID cartographic platform. Bol Asoc Geogr Esp. 2021; (91).  https://doi.org/10.21138/bage.3151 
  24. Sortino Barrionuevo JF, Castro Noblejas H, Perles Roselló MJ. Mapping the Risk of COVID-19 Contagion at Urban Scale. Vol. 11. Land (Basel). 2022;11(9):1480.  https://doi.org/10.3390/land11091480 
  25. Perles MJ, Sortino JF, Mérida MF. The Neighborhood Contagion Focus as a Spatial Unit for Diagnosis and Epidemiological Action against COVID-19 Contagion in Urban Spaces: A Methodological Proposal for Its Detection and Delimitation. Int J Environ Res Public Health. 2021;18(6):3145.  https://doi.org/10.3390/ijerph18063145  PMID: 33803729 
  26. Paez A. Reproducibility of Research During COVID-19: Examining the Case of Population Density and the Basic Reproductive Rate from the Perspective of Spatial Analysis. Geogr Anal. 2021;54(4):860-80.  https://doi.org/10.1111/gean.12307  PMID: 34898693 
  27. Change Mediterranean Metropolis Around Time (CAT-MED) and Europe in the Mediterranean. Carta de Málaga sobre modelos urbanos sostenibles. [Málaga Charter on Sustainable Urban Models]. 2011. Spanish. Available from: https://www.upv.es/contenidos/CAMUNISO/info/U0548731.pdf
  28. Neuman M. The Compact City Fallacy. J Plann Educ Res. 2005;25(1):11-26.  https://doi.org/10.1177/0739456X04270466 
  29. Roger R, Power A. Cities for a Small Country. London: Faber; 2000.
  30. Bibri SE, Krogstie J, Kärrholm M. Compact city planning and development: Emerging practices and strategies for achieving the goals of sustainability. Dev Built Environ.2020;4:100021.  https://doi.org/10.1016/j.dibe.2020.100021 
  31. Barke M. City profile Malaga. Cities. 1992;9(1):2-17.  https://doi.org/10.1016/0264-2751(92)90002-M 
  32. The Government of Spain. Agencia Estatal Boletín Oficial del Estado. Real Decreto 956/2020, de 3 de noviembre, por el que se prorroga el estado de alarma declarado por el Real Decreto 926/2020, de 25 de octubre, por el que se declara el estado de alarma para contener la propagación de infecciones causadas por el SARS-CoV-2. [Official State Gazette. Royal Decree 956/2020, November 3rd, extending the state of alarm declared by Royal Decree 926/2020, of October 25th, which declared the state of alarm to contain the spread of infections caused by SARS-CoV-2]. Ministry of the Presidency, Relations with the Parliament and Democratic Memory: 2020; 291:95841-95845. Spanish. Available from: https://www.boe.es/eli/es/rd/2020/11/03/956
  33. Environmental Systems Research Institute (ESRI). ArcGIS Pro. Redlands: ESRI; 2020. Available from: https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview
  34. QGIS. QGIS Geographic Information System. QGIS Association; 2021. Available from: http://www.qgis.org
  35. Gatrell AC, Bailey TC, Diggle PJ, Rowlingson BS. Spatial point pattern analysis and its application in geographical epidemiology. Trans Inst Br Geogr. 1996;21(1):256-74.  https://doi.org/10.2307/622936 
  36. González JA, Moraga P. Non-Parametric Analysis of Spatial and Spatio-Temporal Point Patterns. R J. 2023;15(1):65-82.  https://doi.org/10.32614/RJ-2023-025 
  37. Lambio C, Schmitz T, Elson R, Butler J, Roth A, Feller S, et al. Exploring the Spatial Relative Risk of COVID-19 in Berlin-Neukölln. Int J Environ Res Public Health. 2023;20(10):5830.  https://doi.org/10.3390/ijerph20105830  PMID: 37239558 
  38. Instituto de Estadística y Cartografía de Andalucía. Distribución espacial de la población en Andalucía. [Institute of Statistics and Cartography of Andalusia. Spatial Distribution of the Population in Andalusia]. Seville: Junta de Andalucia; 2023. Spanish. Available from: https://www.juntadeandalucia.es/institutodeestadisticaycartografia/dega/distribucion-espacial-de-la-poblacion-en-andalucia
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