An epidemiological and spatiotemporal analysis to identify high risk areas of malaria in Visakhapatnam district of Andhra Pradesh, India, 1999–2015

• Published in: Spatial information research (Online) Vol. 27; no. 6; pp. 659 - 672
• Main Authors: Mopuri, Rajasekhar, Mutheneni, Srinivasa Rao, Kumaraswamy, Sriram, Kadiri, Madhusudhan Rao, Upadhyayula, Suryanarayana Murty, Naish, Suchithra
• Format: Journal Article
• Language: English
• Published: Singapore Springer Singapore 01.12.2019; 대한공간정보학회
• Subjects: Database Management; Earth and Environmental Science; Geographical Information Systems/Cartography; Geography; Remote Sensing/Photogrammetry; 공학일반; Spatial modelling; Malaria; Vishakhapatnam; Andhra Pradesh; GIS mapping
• ISSN: 2366-3286; 2366-3294
• DOI: 10.1007/s41324-019-00267-z

Malaria is a major public health problem in Vishakhapatnam district of Andhra Pradesh, India. To understand malaria prevalence a retrospective surveillance study was conducted in the district from 1995 to 2015. A total of 204,229 malaria cases were reported from 1999 to 2015. Plasmodium falciparum and Plasmodium vivax are the major parasites that accounted for 66.8% and 33.2% of the total cases. Tribal population (67%) affected more than the coastal population (33%). Similarly, males were affected (56%) more than female (44%) populace and the highest prevalence was observed in > 15 years age group (83.74%). The spatial analysis reveals that the distribution of malaria is having high spatial autocorrelation (0.231 to 0.493) and scan statistics declare that the malaria cases were significantly clustered in spatial, temporal and spatiotemporal distribution. The most likely spatiotemporal cluster of malaria (LLR = 26,562.24, RR = 6.62, P  < 0.001) occurred in the Northern part of the district covering 11 mandals with the time frame from April 2010 to September 2015. The results confirm that the presence of spatial and space–time clusters concentrated in the North and North-eastern region of the district, which contribute for better understanding of disease spreading dynamics in high-risk areas for future malaria prevention and control.