Journal Article

Geospatial clustering and hot spot detection of malaria incidence in Bahawalpur district of Pakistan

Arshad, Sana, Butt, Ibtisam, Fatima, Munazza

2022

Overview

Malaria is one of the main causes of morbidity and mortality in developing countries like Pakistan. Current study is based on geospatial analysis of malaria across Bahawalpur district of Pakistan. The key purpose is to measure spatial patterns which might be helpful for generating local environmental etiological hypothesis for malaria. Union council level epidemiological data for malaria was collected through 115 health centers from the study area for the period of six years 2012–2017. Techniques of spatial autocorrelation were applied to find results. Local Moran’s I statistics was used to perform cluster and outlier analysis of malaria. Presence of local clustering was further assessed by using Getis Ord Gi* statistics to assess intensity of hotspots and cold spots at the union council level. However, Inverse Distance Weighing (IDW) was used to interpolate and predict the spatial pattern of malaria cases in study area. Results showed spatial heterogeneity of malaria incidence in the district identifying both high (hotspots) and low (cold spots) clusters. Highest statistical significance has been revealed in northwestern rural areas of the district defining them as malaria hotspots. Contrary, extreme northern areas and urban centers of tehsils were found to be cold spot during all the six years. Finally, this study provides also a set of suggestions addressing the local environmental issues and to minimize the incidence of malaria through administrative environmental management and community participation. In addition, it will not only provide a base for advance geospatial research of malaria but can also be applied in other malaria endemic districts of the country.

• Publisher: Springer Nature B.V
• Subject: Autocorrelation / Clustering / Community involvement / Community participation / Data analysis / Developing countries / Environmental management / Epidemiology / Health facilities / Heterogeneity / Hot spots / Human diseases / LDCs / Malaria / Morbidity / Outlier analysis / Outliers (statistics) / Patchiness / Resource management / Rural areas / Rural communities / Spatial analysis / Spatial heterogeneity / Statistical analysis / Statistical methods / Statistical significance / Statistics / Urban areas / Urban environments / Vector-borne diseases / Weighing