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1.
Malaria is one of the most widespread diseases in the world. Endemic malaria no longer occurs in many temperate zones as a result of social and economic improvement. At present malaria is the Third World's most dreaded killer. It kills over 1 million people and causes 300–500 million episodes of illness. In India, malaria-reported deaths have shown an upward trend. In 1955, a drive to eradicate malaria was launched in India. But after initial success it failed and malaria made a comeback. Malarial mosquitoes generally prefer unpolluted natural breeding sites but now they have adapted to the changed urban environment. In this paper, an attempt has been made to examine the occurrence of malaria and related environmental issues in a small town of India. Aligarh city, lying in the shadow of the country's capital New Delhi, was selected as a case study. Data were collected mainly from household surveys with the help of questionnaire interviews. About 2,185 households belonging to different income groups were sampled. The differences in the occurrence of malaria in the different income households (in 87% low, 69% lower-middle, 65% middle, 14% upper-middle, and 5% upper) suggest that most of these differences are related to the environmental conditions existing inside and outside their homes, such as poor drainage system, poor sullage disposal, open blocked drains, waterlogging and indoor water storage in open containers. Commitment both by the Government and local residents is needed to improve the environmental conditions and eradicate malaria.  相似文献   
2.
The study quantifies the environmental risk factors for two diseases with different vectors and cycles of transmission: malaria and Chagas' disease in N.W. Argentina near the Bolivian border. This is the area within Argentina where malaria is still a serious health problem. Chagas' disease is to some extent present in many parts of the country. The field work for the study concerned 5903 people in 1466 dwellings. The study resulted in detailed maps of risk factors: particularly water quality and contacts with migrants from Bolivia in the case of malaria; and thatched roofs and dogs in the case of Chagas' disease. This revised version was published online in August 2006 with corrections to the Cover Date.  相似文献   
3.
Water temperature is an important determinant of the growth and development of malaria mosquito immatures. To gain a better understanding of the daily temperature dynamics of malaria mosquito breeding sites and of the relationships between meteorological variables and water temperature, three clear water pools (diameter × depth: 0·16 × 0·04, 0·32 × 0·16 and 0·96 × 0·32 m) were created in Kenya. Continuous water temperature measurements at various depths were combined with weather data collections from a meteorological station. The water pools were homothermic, but the top water layer differed by up to about 2 °C in temperature, depending on weather conditions. Although the daily mean temperature of all water pools was similar (27·4–28·1 °C), the average recorded difference between the daily minimum and maximum temperature was 14·4 °C in the smallest versus 7·1 °C in the largest water pool. Average water temperature corresponded well with various meteorological variables. The temperature of each water pool was continuously higher than the air temperature. A model was developed that predicts the diurnal water temperature dynamics accurately, based on the estimated energy budget components of these water pools. The air–water interface appeared the most important boundary for energy exchange processes and on average 82–89% of the total energy was gained and lost at this boundary. Besides energy loss to longwave radiation, loss due to evaporation was high; the average estimated daily evaporation ranged from 4·2 mm in the smallest to 3·7 mm in the largest water pool. Copyright © 2008 John Wiley & Sons, Ltd.  相似文献   
4.
疟疾是世界上最严重的一种寄生虫疾病,安徽省是典型的中纬度疟疾高发区域之一。本文以安徽省县级行政单元统计的疟疾发病率为例,从遥感监测数据中获取疟疾潜在驱动因素的数据,使用遗传规划方法建立遥感监测的环境因素与疟疾发病率之间的关系,从而预测疟疾发病率的空间分布,并分析预测结果、评价模型精度。结果表明,遗传规划方法预测的疟疾发病的精度（训练数据的预测R2 = 0.558,检验数据R2 = 0.429）较线性逐步回归方法的预测精度（训练数据的预测R2 = 0.470,检验数据R2 = 0.408）有所提高。遗传规划方法有利于提高预测疟疾发病率空间分布的精度。其为使用遥感监测数据预测疟疾的空间分布和变化的科学研究提供依据。  相似文献   
5.
Abstract

This work comprises a spatial, temporal and statistical analysis of the epidemiology of malaria occurrence in four municipalities of the State of Amazonas, Brazil: Coari, Codajás, Manacapuru and Manaus, for the period 2003–2009. The number of malaria cases, precipitation, water level and temperature data were analysed in this study. The strength of the relationship between these hydrological/meteorological variables and the occurrence of malaria was determined by employing the Spearman rank correlation coefficient. Seasonal peaks of malaria were registered, on average, about 1–2 months before the annual maximum temperature and after the river’s seasonal high-water level. The phenomenon called repiquete (notable variations in the water level) was observed during periods of between 9 and 56 days. The results showed a statistically significant correlation between malaria, temperature, precipitation and water level. Temperature influenced malaria occurrence the least, while rainfall was the most important factor, especially in the municipality of Coari. Water level had an important influence on the records of malarial occurrence in the municipality of Manacapuru.

Editor Z.W. Kundzewicz

Citation Wolfarth, B.R., Filizola, N., Tadei, W.P., and Durieux, L., 2013. Epidemiological analysis of malaria and its relationships with hydrological variables in four municipalities of the State of Amazonas, Brazil. Hydrological Sciences Journal, 58 (7), 1495–1504.  相似文献   
6.
The aim of this study is to derive environmental factors that are likely to influence malarial distribution from Nigeriasat-1 in a geographical information systems (GIS) environment and relate it to the empirical evidence of reported malarial cases in the hospitals using discriminant analysis (DA) to characterize, identify and map malarial risk zones. It is found that using a stepwise DA, Nigeriasat-1 and GIS it is possible to classify the accurately the low malarial risk zone (100%), medium and high risk zones (83.33%), with an overall accuracy of 88.9% being achieved for the study area. The results obtained were in agreement with the ground validation exercise that was carried out and the cross validation method of ‘‘leaving-one-out’ in DA function. These findings indicate that Nigeriasat-1 and GIS combined with statistical technique of DA can be utilized as a decision support tool for a precise identification of the areas warranting mitigation efforts.  相似文献   
7.
Abstract

Malaria burden has considerably declined in the last 15 years mainly due to large-scale vector control. The continued decline can be sustained through malaria risk stratification. Malaria stratification is the classification of geographical areas according to malaria risk. In this study, ecological niche modelling using the maximum entropy algorithm was applied to predict malaria vector habitat suitability in terms of bioclimatic and topographic variables. The output vector suitability map was integrated with malaria prevalence data in a GIS to stratify Zimbabwe into different malaria risk zones. Five improved and validated malaria risk zones were successfully delimited for Zimbabwe based on the World Health Organization classification scheme. These results suggest that the probability of occurrence of major vectors of malaria is a key determinant of malaria prevalence. The delimited malaria risk zones could be used by National Malaria Control programmes to plan and implement targeted malaria interventions based on vector control.  相似文献   
8.
Abstract

This study examines the potentials of remotely sensed data, GIS and some machine learning classifiers and ensemble techniques in the investigation of the non-linear relationship between malaria occurrences and socio-physical conditions in the Dak Nong province of Viet Nam. Accuracy assessment was determined with Receiver Operating Characteristic (ROC) curve and pair t-test. The results showed that the area under ROC of Random Subspace ensemble model performed better than the other models based on statistical indicators. Comparing pair t-test with Area Under Curve values showed a slight difference of about 1%. Therefore ensemble techniques had significantly improved the performance of the base classifier. However, the performances might vary according to geographic locations. It is concluded that the machine learning classifiers combined with remotely sensed data and GIS is promising for malaria vulnerability mapping, and the derived maps can be used as a fundamental basis for programmes on spatial disease control.  相似文献   
9.
Abstract

Development programmes in Sahelian Africa are beginning to use geographic information system (GIS) technology. One of the GIS and remote sensing programmes introduced to the region in the late 1980s was the use of seasonal vegetation maps made from satellite data to support grasshopper and locust control. Following serious outbreaks of these pests in 1987, the programme addressed a critical need, by national and international crop protection organizations, to monitor site-specific dynamic vegetation conditions associated with grasshopper and locust breeding. The primary products used in assessing vegetation conditions were vegetation index (greenness) image maps derived from National Oceanic and Atmospheric Administration satellite imagery. Vegetation index data were integrated in a GIS with digital cartographic data of individual Sahelian countries. These near-real-time image maps were used regularly in 10 countries for locating potential grasshopper and locust habitats. The programme to monitor vegetation conditions is currently being institutionalized in the Sahel.  相似文献   
10.
The primary objective of this research was to determine if the remotely-sensed metric, Normalised Difference Vegetation Index (NDVI) and ground-collected dekadal climatological variables were useful predictors of future malaria outbreaks in an epidemic-prone area of Nairobi, Kenya. Data collected consisted of 36 dekadal (10-day) periods for the variables rainfall, temperature and NDVI along with yearly documented malaria admissions in 2003 for Nairobi, Kenya. Linear regression models were built for malaria cases reported in Nairobi, Kenya, as the dependent variable and various time-based groupings of temperature, rainfall and NDVI data from the dekads in both the current and the previous month as the independent variables. Data from 2003 show that malaria incidence in any given month is best predicted (R2  = 0.881, p < 0.001) by the average NDVI for the 30 days including the final two dekads of the previous month and first dekad of the current month, and by the average rainfall for the 30 days including the three dekads of rainfall data from the prior month. Forecasting an outbreak in an epidemic zone would allow public health entities to plan for and disseminate resources to the general public such as antimalarials and insecticide impregnated bed nets. In addition, vector control measures could be implemented to slow the rate of transmission in the impacted population.  相似文献   
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