Modeling spatio-temporal dynamics of malaria using geospatial technology in India
Student name: Ms Aditi Rastogi
Guide: Dr Neeti
Year of completion: 2017
Host Organisation: TERI School of Advanced Studies
Supervisor (Host Organisation): Dr Neeti
Abstract: Malaria in India is a life threatening disease which causes huge casualties on a large scale.
Hence, this study is done to understand the spatio-temporal pattern of malaria and enlist out
various regional and global phenomena that may impact the malaria situation in India. A Mann-
Kendall test for trend analysis is performed on the data collected for malaria cases from 1994-
2016. The trend analysis results reveal that there is significant increase in the number of malaria
cases over the years whereas most of the other states show declining trend with few states such
as Bihar showing no significant change in number of malaria cases. However, the increase in
number of malaria cases in Meghalaya does not appear to be uniformly distributed across its
districts. Most of the increase in number of malaria cases is seen only in Khaso hill districts
(East and South West). Similarly, the stable number of malaria cases in Bihar state does not
appear to be same across all the districts. There has been significant increase in malaria cases in
Bhojpur, Madhepura, Nalanda and Purnia suggesting that the state level study can portray a
very different picture. The correlation analysis suggests weak negative association of the number
of malaria cases with both ONI and IOD whereas strong positive association with MJO. The
Poisson regression analysis results conclude that malaria risk with respect to change in ONI is
not significant in Rajasthan and Sikk im; IOD increases the risk of malaria in few states only. The
boreal winter season increases the malaria risk in Sikkim only while Uttarakhand has the
maximum risk of change in malaria cases with respect to the summer monsoon.
Keywords: India, Malaria, Mann Kendall, Correlation, Regression
