Rai Praveen Kumar, Nathawat Mahendra Singh, Rai Shalini
Department of Geography, Banaras Hindu University, Varanasi-221005, Uttar Pradesh, India.
School of Sciences, Indira Gandhi National Open University, New Delhi, India.
Inform Prim Care. 2013;21(1):43-52. doi: 10.14236/jhi.v21i1.38.
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.
本文探讨了疟疾易感性建模在预测某地区疟疾发生情况方面的应用范围。
在印度瓦拉纳西地区进行了一项尝试，以评估疟疾疾病的现状，并开发一个模型，通过该模型利用五类相对疟疾易感性（即极低、低、中、高和极高类别）来预测疟疾高发区。采用信息值（Info Val）方法评估疟疾发生情况，并将不同时间作为自变量。运用地理信息系统（GIS）来研究这些变量与导致疟疾传播的不同蚊子分布之间的关联。风险的准确预测取决于许多变量，如土地利用、归一化植被指数（NDVI）、气候因素、人口、到医疗中心、池塘、溪流和道路的距离等，所有这些都会对疟疾传播或报告产生影响。已知气候因素，特别是降雨、温度和相对湿度，对蚊子的生物学特性有重大影响。为了使用这种方法生成疟疾易感性地图，需要计算每组中各类别的权重。然后将这些组叠加起来，绘制疟疾易感性指数（MSI）地图。
我们发现，在根据模型预测疟疾易感性水平较低的地区发现了3.87%的疟疾病例，而在预测为高和极高易感性水平的地区分别发现了39.86%和26.29%的疟疾病例。
利用GIS建模的疟疾易感性可能在预测疟疾风险方面发挥作用，并使公共卫生干预措施更具针对性。
