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首页> 外文期刊>International Journal of Health Geographics >An information value based analysis of physical and climatic factors affecting dengue fever and dengue haemorrhagic fever incidence
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An information value based analysis of physical and climatic factors affecting dengue fever and dengue haemorrhagic fever incidence

机译：基于信息价值的影响登革热和登革出血热发生的物理和气候因素分析

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Background Vector-borne diseases are the most dreaded worldwide health problems. Although many campaigns against it have been conducted, Dengue Fever (DF) and Dengue Haemorrhagic Fever (DHF) are still the major health problems of Thailand. The reported number of dengue incidences in 1998 for the Thailand was 129,954, of which Sukhothai province alone reported alarming number of 682. It was the second largest epidemic outbreak of dengue after 1987. Government arranges the remedial facilities as and when dengue is reported. But, the best way to control is to prevent it from happening. This will be possible only when knowledge about the relationship of DF/DHF with climatic and physio-environmental agents is discovered. This paper explores empirical relationship of climatic factors rainfall, temperature and humidity with the DF/DHF incidences using multivariate regression analysis. Also, a GIS based methodology is proposed in this paper to explore the influence of physio-environmental factors on dengue incidences. Remotely sensed data provided important data about physical environment and have been used for many vector borne diseases. Information Values (IV) method was utilised to derive influence of various factors in the quantitative terms. Researchers have not applied this type of analysis for dengue earlier. Sukhothai province was selected for the case study as it had high number of dengue cases in 1998 and also due to its diverse physical setting with variety of land use/land cover types. Results Preliminary results demonstrated that physical factors derived from remotely sensed data could indicate variation in physical risk factors affecting DF/DHF. A composite analysis of these three factors with dengue incidences was carried out using multivariate regression analysis. Three empirical models ER-1, ER-2 and ER-3 were evaluated. It was found that these three factors have significant relation with DF/DHF incidences and can be related to the forecast expected number of dengue cases. The results have shown significantly high coefficient of determination if applied only for the rainy season using empirical relation-2 (ER-2). These results have shown further improvement once a concept of time lag of one month was applied using the ER-3 empirical relation. ER-3 model is most suitable for the Sukhothai province in predicting possible dengue incidence with 0.81 coefficient of determination. The spatial statistical relationship of various land use/land cover classes with dengue-affected areas was quantified in the form of information value received from GIS analysis. The highest information value was obtained for the Built-up area. This indicated that Built-up area has the maximum influence on the incidence of dengue. The other classes showing negative values indicate lesser influence on dengue epidemics. Agricultural areas have yielded moderate risk areas based on their medium high information values. Water bodies have shown significant information value for DF/ DHF only in one district. Interestingly, forest had shown no influence on DF/DHF. Conclusion This paper explores the potential of remotely sensed data and GIS technology to analyze the spatial factors affecting DF/DHF epidemic. Three empirical models were evaluated. It was found that Empirical Relatrion-3 (ER-3) has yielded very high coefficient of determination to forecast the number of DF/DHF incidence. An analysis of physio-environmental factors such as land use/ land cover types with dengue incidence was carried out. Influence of these factors was obtained in quantitative terms using Information Value method in the GIS environment. It was found that built-up areas have highest influence and constitute the highest risk zones. Forest areas have no influence on DF/DHF epidemic. Agricultural areas have moderate risk in DF/DHF incidences. Finally the dengue risk map of the Sukhothai province was developed using Information Value meth
机译：背景媒介传播疾病是世界上最可怕的健康问题。尽管已经开展了许多反对它的运动，但登革热（DF）和登革热（DHF）仍然是泰国的主要健康问题。据报道，1998年泰国的登革热发病数为129,954，其中仅素可泰省就报告了令人震惊的682例。这是1987年以来第二大登革热流行病。政府在报告登革热时安排了补救设施。但是，最好的控制方法是防止它发生。仅当发现有关DF / DHF与气候和生理环境因素的关系的知识时，这才有可能。本文运用多元回归分析探讨了降雨，温度和湿度等气候因素与DF / DHF发生率的经验关系。此外，本文提出了一种基于GIS的方法，以探讨生理环境因素对登革热发病率的影响。遥感数据提供了有关物理环境的重要数据，并已用于许多媒介传播的疾病。信息价值（IV）方法被用来导出各种因素在定量方面的影响。研究人员没有更早地将这种类型的分析用于登革热。素可泰省被选为案例研究对象，因为它在1998年有大量登革热病例，而且由于其自然环境多样，土地用途/土地覆盖类型也不同。结果初步结果表明，从遥感数据中得出的物理因素可能表明影响DF / DHF的物理危险因素存在差异。使用多元回归分析对这三个因素与登革热发病率进行了综合分析。评估了三个经验模型ER-1，ER-2和ER-3。发现这三个因素与DF / DHF发病率有显着关系，并且可以与预测的登革热病例预期数量有关。如果仅使用经验关系式2（ER-2）将其应用于雨季，则结果表明测定系数非常高。一旦使用ER-3经验关系应用了一个月的时滞概念，这些结果就表明了进一步的改进。 ER-3模型最适合于素可泰省，以0.81的确定系数来预测可能的登革热发病率。各种登革热受影响地区的土地利用/土地覆盖类别的空间统计关系以从GIS分析获得的信息价值的形式量化。对于“建筑面积”，获得了最高的信息值。这表明，建筑面积对登革热的影响最大。其他显示负值的类别表明对登革热流行的影响较小。农业区域由于其中等较高的信息价值而产生了中等风险区域。水体仅在一个地区就显示了对DF / DHF的重要信息价值。有趣的是，森林对DF / DHF没有影响。结论本文探讨了遥感数据和GIS技术在分析影响DF / DHF流行的空间因素方面的潜力。评估了三个经验模型。发现经验相对论3（ER-3）产生了很高的确定系数，可以预测DF / DHF的发病率。对诸如登革热发病率的土地利用/土地覆盖类型等生理环境因素进行了分析。这些因素的影响是在GIS环境中使用“信息价值”方法定量获得的。人们发现，建成区影响最大，构成最高风险区。林区对DF / DHF流行没有影响。农业地区发生DF / DHF的风险中等。最后，利用信息价值法绘制了素可泰省的登革热风险图

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