• DOI:10.14236/jhi.v21i1.38
• Corpus ID: 23557577

Using the information value method in a geographic information system and remote sensing for malaria mapping: a case study from India.

@article{Rai2014UsingTI,
  title={Using the information value method in a geographic information system and remote sensing for malaria mapping: a case study from India.},
  author={Praveen Kumar Rai and Mahendra Singh Nathawat and Shalini Rai},
  journal={Informatics in primary care},
  year={2014},
  volume={21 1},
  pages={
          43-52
        }
}

• P. Rai, M. S. Nathawat, Shalini Rai
• Published 26 February 2014
• Medicine
• Informatics in primary care

BACKGROUND
This paper explores the scope of malaria-susceptibility modelling to predict malaria occurrence in an area.

OBJECTIVE
An attempt has been made in Varanasi district, India, to evaluate the status of malaria disease and to develop a model by which malaria-prone zones could be predicted using five classes of relative malaria susceptibility, i.e.very low, low, moderate, high and very high categories. The information value (Info Val) method was used to assess malaria occurrence and various time-were used as the independent variables. A geographical information system (GIS) is employed to investigate associations between such variables and distribution of different mosquitoes responsible for malaria transmission. Accurate prediction of risk depends on a number of variables, such as land use, NDVI, climatic factors, population, distance to health centres, ponds, streams and roads etc., all of which have an influence on malaria transmission or reporting. Climatic factors, particularly rainfall, temperature and relative humidity, are known to have a major influence on the biology of mosquitoes. To produce a malaria-susceptibility map using this method, weightings are calculated for various classes in each group. The groups are then superimposed to prepare a Malaria Susceptibility Index (MSI) map.

RESULTS
We found that 3.87% of the malaria cases were found in areas with a low malaria-susceptibility level predicted from the model, whereas 39.86% and 26.29% of malaria cases were found in predicted high and very high susceptibility level areas, respectively.

CONCLUSIONS
Malaria susceptibility modelled using a GIS may have a role in predicting the risks of malaria and enable public health interventions to be better targeted.