Document Type : Original Paper


Department of Statistics, Faculty of Sciences, University of Abuja, Abuja, Nigeria


Tuberculosis (TB) remains one of the 21st century's crucial public health problems. Today, it is the second leading cause of death from a single infectious disease agent. Spatial information on health-related diseases like TB is not well documented in Nigeria. The study of Spatial Distribution of TB in Niger State is necessitated for broad and more comprehensive coverage of TB cases within the State. As such, there is a need to carry out a spatial auto-correlation analysis of the entire 25 Local Government Areas (LGAs) of the State to explore the epidemiological data, distribution, and pattern of infection from 2016 to 2020. This study investigated the spatial auto-correlation structure with the Moran Index, Anselin's local Moran's I, Getis-ord local statistics and kriging interpolation methods. The result of the Analysis of Variance (ANOVA) showed that there was a significant difference in infection patterns between LGAs. This result implied variability in the number of cases across different local governments within the study periods. Although the considerable number of TB-reported instances, the analysis based on Moran's I and global G revealed no clustering pattern in the data. The local indicator of spatial association result showed that the number of cases was random across the LGAs; there were no significant clusters. Kriging interpolation method identified Kontogora, Bosso and Chanchaga LGAs as hotspots of TB cases in Niger State. This study provides information that can assist policymakers in rationally planning targeted interventions to effectively control TB while addressing the underlying socio-economic risk factors in Niger State. This study has shown that despite data limitations, Geostatistical approaches are viable for understanding interpolation patterns.


  1. Zumla A, George A, Sharma V, Herbert RHN, Oxley A, Oliver M. The WHO 2014 global tuberculosis report—further to go. Lancet Glob Health. 2015;3(1):e10-e12.
  2. Centre for Disease Control and Prevention, Global Health Tuberculosis. Available online from: http// on 2nd March, 2022).
  3. World Health Organization Tuberculosis (TB). WHO Africa. Available from: https// on 2nd March, 2022).
  4. Ostfeld RS, Glass GE, Keesing F. Spatial epidemiology: an emerging (or re-emerging) discipline. Trends Ecol Evol. 2005;20(6):328-336.

5.So FFM, Lai P. Spatial epidemiology of asthma in Hong Kong. GIS for Health and the Environment. Springer; Chapter 2038. 2007;154-169:.

  1. Singh DS, Saranya S, Mary MI. Kriging Approach for an Epidemic Data Analysis. Int J Sci Innov Math Res. 2014;2(6):530-536.
  2. Ibrahim S, Hamisu L, Gulma UL. Spatial Pattern of Tuberculosis Prevalence in Nigeria: A Comparative Analysis of Spatial Autocorrelation Indices. Am J Geogr Inf Syst. 2015;4(3):87-94.
  3. Fu WJ, Jiang PK, Zhou GM, Zhao KL. Using Moran ’ s I and GIS to study Sthe spatial pattern of forest litter carbon density in a subtropical region of southeastern China. Biogeosciences. 2014;11(8):2401–2409.
  4. Sani RA, Garba SA, Oyeleke SB, Abalaka ME. Prevalence of Pulmonary Tuberculosis (PTB) in Minna and Suleja Niger State, Nigeria. Am J Med. 2015;5(6):287-291.
  5. National Population Commission’s Population Figures for the Nigeria States for 2006 Population and Housing Census, 2006; NPC: Abuja, Nigeria.
  6. Oguntade ES, Shohaimi S, Nallapan M, Lamidi-sarumoh AA, Salari N. Statistical Modelling of the Effects of Weather Factors on Malaria Occurrence in Abuja, Nigeria. Int J Environ Res Public Health. 2020;17(3474):1–12.
  7. Comber A, Brunsdon C. A spatial analysis of plant phenophase changes and the impact of increases in urban land use. Int J Climatol. 2015;35(6):972-980.
  8. Anselin L. Local indicators of spatial association—LISA. Geographic Analys. 1995;27(2):93-115.
  9. Mathur M. Spatial autocorrelation analysis in plant population: An overview. J Appl Nat Sci. 2015;7(1):501-513.
  10. Ord JK, Getis A. Local spatial autocorrelation statistics: distributional issues and an application. Geographic Analys. 1995;27(4):286-306.
  11. Anselin L, Getis A. Spatial statistical analysis and geographic information systems. Ann Region Sci. 1992;26(1):19-33.
  12. Salahi-Moghaddam A, Khoshdel A, Dalaei H, Pakdad K, Nutifafa GG, Sedaghat MM. Spatial changes in the distribution of malaria vectors during the past 5 decades in Iran. Acta Tropica. 2017;166:45–53.
  13. Lundberg L. Ordinary Kriging Prediction Intervals Based on SemiparametricBootstrap. J Math Stat. 2019;22.
  14. Luo W, Taylor M, Parker S. A comparison of spatial interpolation methods to estimate continuous wind speed surfaces using irregularly distributed data from England and Wales. Int J Climatol. 2008;28(7):947-959.
  15. Haddawy P, Hasan AHMI, Kasantikul R, Lawpoolsri S, Sa-angchai P, Kaewkungwal J, et al. Spatiotemporal Bayesian networks for malaria prediction. Artif Intell Med. 2018;84:127–138.


  1. Cadmus CA, Agada II, Onoja IS. Risk Factors Associated with Bovine Tuberculosis in some Selected Herds in Nigeria. Trop Anim Health Prod. 2010;42:547-549.
  2. Rajab NA, Hashim N, Rasam ARA. Spatial Mapping and Analysis of Tuberculosis Cases in Kuala Lumpur, Malaysia, 2020; IEEE 10th International Conference on System Engineering and Technology (ICSET), 38-43.