Author(s):
Tuberculosis (TB) is known as a disease that prone to spatial clustering. Recent development has seen a sharp rise in the number of epidemiologic studies employing Geographical Information System (GIS), particularly in identifying TB clusters and evidences of etiologic factors. This retrospective population-based study was conducted to analyze spatial patterns of TB incidence in Punjab province, Pakistan. TB notification data from 2007 to 2017 collected from TB clinics throughout the province was used along with population data to reveal a descriptive epidemiology of TB incidences. Spatial distribution of the disease was observed by using ArcGis. Machine learning algorithms like ANN, SVM and Maximum Entropy were used to predict the presence of the disease with a prediction power of 82%, 75% and 78% respectively. This study has also shown a heterogeneous pattern of the disease over the years with some consistently high risked areas. This study can be very helpful for policy makers to refine their policies for successful eradication of the disease.
