The Role of Spatial Analysis in Notifiable Disease Monitoring and Health Risk Management: A Case Study of Constantine

Authors

Samira Djebari, Siham Bestandji

Abstract

The study aims to enhance understanding of the distribution of notifiable diseases using maps created with ArcGIS in Constantine. Over six years, it focused on the prevalence rates of waterborne diseases and zoonoses (e.g., tuberculosis, meningitis, and COVID-19). A database was created for each municipality using official data, which was processed using SPSS and Microsoft Excel and integrated into a geographic information system (GIS). The maps revealed a high prevalence of diseases in the state's centre, particularly in the municipalities of Constantine, El Khroub, Didouche, and Mourad. The analysis also highlighted a positive relationship between the increase in disease cases and population density, emphasising the critical role of urbanisation in disease spread. Furthermore, seasonal variations were observed in the distribution of certain diseases, indicating that environmental factors, such as temperature and rainfall, influence disease outbreaks. As a result of this study, the maps have demonstrated a fundamental role in monitoring diseases and their development, offering valuable insights for public health surveillance and policy formulation. By visualising trends and patterns, these maps can support decision-making processes to manage health risks better and allocate resources effectively in the region.

Keywords

disaster, health risk, spatial analysis, management, Covid-19, Constantine

Publication Details

Journal: International Journal of Disaster Risk Management
Year: 2025
Volume: 7
Issue: 1
Pages: 215-234
Published: 2025-06-16

DOI and Full Text

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Suggested Citation

Djebari, S., & Bestandji, S. (2025). The Role of Spatial Analysis in Notifiable Disease Monitoring and Health Risk Management: A Case Study of Constantine. International Journal of Disaster Risk Management, 7(1), 215-234. https://doi.org/10.18485/ijdrm.2025.7.1.12

References

1. Astagneau, P., & Mégarbane, M. M. (2011). Surveillance épidémiologique. In M. M. Mégarbane & J. M. Launay (Eds.), Santé publique (pp. 309–319

2. Beale, L. A. (2008). Methodological issues and approaches to spatial epidemiology. Environmental Health Perspectives, 1105–1110.https://doi.org/10.1289/ehp.10816

3. Beck, L. R., Lobitz, B. M., & Wood, B. L. (2000). Remote sensing and human health: New sensors and new opportunities. Emerging Infectious Diseases, 6(3), 217–227. https://doi.org/10.3201/eid0603.000302

4. Cvetković, V. M., Nikolić, N., Radovanović, D., & Milinović, S. (2020). Preparedness and preventive behaviours for a pandemic disaster caused by COVID-19 in Serbia. International Journal of Environmental Research and Public Health, 17(11), 4124. https://doi.org/10.3390/ijerph17114124

5. Cvetković, V. M., Nikolić, N., Ocal, A., Martinović, J., & Dragašević, A. (2022). A Predictive Model of Pandemic Disaster Fear Caused by Coronavirus (COVID-19): Implications for Decision-Makers. International journal of environmental research and public health, 19(2), 652.

6. Desjardins, M. R., Hohl, A., & Delmelle, E. M. (2020). Rapid assessment of COVID-19 spatial accessibility in the United States. PLOS ONE, 15(8), e0237294. https://doi.org/10.1371/journal.pone.0237294

7. Port, K. J. N., & Jawahar, G. G. P. (2024). Management of COVID: The Creeping Disaster in the Indian Scenario. IJDRM, 6(1), 103–110. https://ijdrm.com/archives/vol6issue1/port2024

8. Jerrett, M., Burnett, R. T., Pope, C. A., Ito, K., Thurston, G., Krewski, D., ... & Thun, M. J. (2005). Long-term ozone exposure and mortality. New England Journal of Medicine, 360(11), 1085–1095. https://doi.org/10.1056/NEJMoa1045616

9. Janković, B., & Cvetković, V. (2020). Public Perception of Police Behaviour in the COVID-19 Disaster – The Case of Serbia. Policing: An International Journal, 43(6), 979-992

10. Kirby, R. S., Delmelle, E., & Eberth, J. M. (2017). Advances in spatial epidemiology and geographic information systems. Annals of Epidemiology, 27(1), 1–9. https://doi.org/10.1016/j.annepidem.2016.12.001

11. Kulldorff, M. (1997). A spatial scan statistic. Communications in Statistics - Theory and Methods, 26(6), 1481–1496. https://doi.org/10.1080/03610929708831995

12. Ministry of Health, Population and Hospital Reform. (2022). Executive Decree No. 22-250 of 30 June 2022 establishing the list of communicable diseases subject to mandatory reporting under international surveillance

13. Öcal, A., Cvetković, V. M., Baytiyeh, H., Tedim, F. M. S., & Zečević, M. (2020). Public reactions to the disaster COVID-19: a comparative study in Italy, Lebanon, Portugal, and Serbia. Geomatics, Natural Hazards and Risk, 11(1), 1864-1885.

14. Pickle, L. W. (2009). Spatial analysis for epidemiology. Oxford University Press.

15. Rezaeian, M., Dunn, G., St Leger, S., & Appleby, L. (2007). Geographical epidemiology, spatial analysis and geographical information systems: A multidisciplinary glossary. Journal of Epidemiology and Community Health, 61(2), 98–102. https://doi.org/10.1136/jech.2005.043117.

16. Thacker, S. B., Stroup, D. F., & Parrish, R. G. (1996). Public health surveillance for chronic conditions: A scientific basis for decisions. Statistical Methods in Medical Research, 5(4), 293–309. https://doi.org/10.1177/096228029600500402

17. Ulal, S., & Karmakar, D. (2023). Hazard risk evaluation of COVID-19: A case study. International Journal of Disaster Risk Management, 5(2), 81–101.