Abstract
This paper explores the scope of malaria-susceptibility modelling to predict malaria occurrence in an area. 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. 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. 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.
Highlights
Malaria remains one of the greatest killers, in the developing world.[1]
There are only a few examples of the application of epidemiological maps in malaria control and this may be explained by a lack of suitable, spatially defined data and by a relatively incomplete understanding of how epidemiological variables relate to disease occurrence
A geographical information system (GIS) technique integrated with remote sensing can play a variety of roles in the planning and management of a dynamic and complex healthcare system, disease mapping, public health and epidemiology,[8,9] it is still at an early stage of integration into public healthcare planning.[20]
Summary
Malaria remains one of the greatest killers, in the developing world.[1] Malaria transmission depends on diverse factors that all have an influence on the vectors, parasites, human hosts and the interactions among them These factors may include, among others, meteorological and environmental conditions.[2] The most apparent determinants are observed to be the meteorological and environmental parameters such as rainfall, temperature, humidity and vegetation type and cover.[3,4] There are only a few examples of the application of epidemiological maps in malaria control and this may be explained by a lack of suitable, spatially defined data and by a relatively incomplete understanding of how epidemiological variables relate to disease occurrence. 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
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