جستجوی مقالات مرتبط با کلیدواژه "لپتوسپیروز" در نشریات گروه "عمران"

Leptospirosis is a common zoonosis disease with a high prevalence in the world and is recognized as an important public health drawback in both developing and developed countries owing to epidemics and increasing prevalence. Because of the high diversity of hosts that are capable of carrying the causative agent, this disease has an expansive geographical reach. Various environmental and social factors affect the spread and prevalence of the disease. The combination of epidemiology and Geospatial Information System plus using Ecological niche modeling provides the ability to identify areas at risk of disease, then predict the risk map of the disease for other regions by using relevant environment variables, and prevent and eventually eradicate the disease by conducting constructive activities such as increasing public awareness with education. In this study, using land use, environmental, and climate variables and taking advantage of the occurrences of the disease between 2009 and 2018, the risk level of Leptospirosis was modeled in three provinces of Gilan, Mazandaran, and Golestan based on ecological perspective. For modeling, highly correlated variables and also variables with high multicollinearity were identified and omitted. Because in ecological modeling regions to represent the absence of disease is required in addition to the presence and since these areas are not available, the second objective of this study was to evaluate the efficacy of different methods of generating pseudo-absence data in modeling leptospirosis. Finally, more accurate modeling of the prevalence of the disease in the northern provinces of the country can be obtained. Therefore, after selecting suitable variables for modeling, first, based on five methods (completely random generation of points in the study area, applying physical constraints with buffer at two radii of 5 and 10 km the generating points outside of designated buffer, applying environmental constraints by implementing two models of one-class support vector machine and BIOCLIM and generating points in unsuitable areas defined by these two models) pseudo-absence points representative of disease absence points in the study area were produced. Next, four models of Artificial Neural Network, Generalized Linear Model, Random Forest, and Gradient Boosting Machine were deployed to produce the disease risk in the study area. BIOMOD2 package in the R programming language was applied for modeling. The results showed that applying physical constraints with buffers yields the most reliable performance in comparison to the other three methods. Finally, the constructed model that performed best in TSS Statistics (with values of 0.76, 0.87, 0.84, 0.82 for Models of Artificial Neural Network, Generalized Linear Model, Random Forest, and Gradient Boosting Machine) was considered as the final output. Between all deployed models, Artificial Neural Network delivered the worst performance and had the most unstable results. Based on the risk-map of leptospirosis, central regions of Mazandaran and Gilan province, especially rural areas of Layl, Asalam, Eslam Abad, Chahar-deh, and Lafmejan have very high values of risk. Measures need to be made to reduce the high rate of Leptospirosis incidence in these regions. Furthermore, yearly precipitation was considered the most influential variable for the distribution of Leptospirosis.

The global burden of leptospirosis as a fatal zoonotic disease is increasing all over the world [1]. As there is not any significant decrease in yearly reported cases trend in Iran and potential spatial distribution of leptospirosis remain unknown in national level, we tried to figure out the geographic distribution pattern of leptospirosis in all parts of Iran. The aim of this study is producing leptospirosis risk map by analyzing relations between disease data reported by the Ministry of Health and nine environmental factors, for a period of 2009 to 2018, using Geospatial Information System (GIS) and Remote Sensing (RS) capabilities and Maximum Entropy (MAXENT) model. Altitude, precipitation, average temperature, maximum temperature, Normalized Difference Vegetation Index (NDVI), land cover, displacement (roads, railways and border entrance points), slope and water areas with 1km * 1km resolution were entered to the model as contributing factors, and patients home locations were used as disease incidence points. ArcGIS 10.6.1 and ENVI 5.3 were used to prepare the nine factors for analysis and interpretation of the results. To create the potential distribution, MAXENT as an ecological niche model was used which is a method that its performance in disease distribution modelling has been proved [2,3]. An advantage of this model is that variables can be either continuous or categorical and can be run for even less than 100 points as incidence data [2]. In this study, 60 percent of disease data was selected randomly for training and other 40 percent was applied as test data. Jackknife manipulation technique was performed to investigate the contribution of each variable in model. Our findings on spatial pattern of leptospirosis at least hint that except north parts of Iran that obviously are most vulnerable areas to the leptospirosis outbreaks, west parts of Iran specially Kermanshah are not safe from the spread of the disease, so health policy makers should consider these areas for monitor and control programs specially after severe rainfall or flood in spring and summer. Jackknife results showed that precipitation and altitude by 43.5 and 37 percent contribution, are the two major factors for risk prediction of leptospirosis. On other hand, maximum temperature, water areas and slope have not meaningful impact on incidence of leptospirosis. Land cover with 11.9%, NDVI with 4%, average temperature with 1.3% and displacement with 1.1% were participated in the model. Also, yearly models have been created for years between 2009 to 2018 to investigate that how parameters contributions change over years. Results showed that the incidence rate was related to altitude around 40% for all these ten years, but precipitation contribution percentage is fluctuating over years. Response curves showed a direct relation between incidence rate of disease and precipitation which means more rainfall causes more incidence. It also showed that altitudes around zero are the most suitable height condition on current distribution of leptospirosis. Also, the landcover output curve showed that Post-flooding or irrigated croplands, artificial surfaces and associated areas, mosaic forests or shrublands and grasslands are the most suitable landcovers for incidence of leptospirosis. To assess the model efficiency, Receiver Operating Characteristic (ROC) was employed. The Area under the Receiver Operating Characteristic Curve (AUC) for training data and test data was 0.956 and 0.955, respectively.

There are more than 200 types of zoonotic diseases in the world and leptospirosis is the most important. Leptospirosis occurs mostly in areas with a tropical climate and abundant rainfall. There are no specific statistics of this disease worldwide, and records are underestimated for several reasons. Hence, the World Health Organization (WHO) named leptospirosis as a neglected tropical disease in the world and more research is needed in this field. When paddy season in the north of Iran begins, the disease spread and in severe cases leads to death. Leptospirosis is recognized globally as a multi-faceted disease and failure to recognize or treat it onetime can lead to death of patients. The main cause of the spread of this disease is a bacterium present in the body of domestic and wild animals, especially mice and dogs (as reservoirs of disease) and transmitted through the urine or feces to the environment. As a result, the bacteria can be transmitted to the human body through injuries to the skin or contact with contaminated soil and water. The environment and occupation are very effective in the spread of the disease, which is recognized as a work-related illness and can be dangerous in both urban and rural areas. The emergence of this disease can be due to reasons such as agriculture, livestock, butchers, recreational activities and water sports, poverty, travel to tropical areas, and any activity that leads to contact with water, soil or contaminated environment. This disease is more prevalent in fishermen and farmers, especially sugar cane farmers and workers, and it is very important to cause problems such as inability to work properly in the time and season needed planting and harvesting, as well as medical costs and even mortality. Compared to other provinces, Guilan province has the highest rate of leptospirosis recently. Therefore, the study and modeling of this disease in the province is of great importance. In this paper, the disease statistics in the rural area during 2009-2011 were assessed as the dependant variable and five variables considered as independent variables for modeling spatial distribution. Considering the important effects of bandwidth and weighting function on modeling results, the efficiency of fixed and adaptive kernels, Bi-Square and Gaussian weighting functions investigated. Two criteria were utilized to evaluate the results include MSE and Definition Coefficient. The results showed that the adaptive kernel and Bi-Square performed better than the fixed kernel and Gaussian, respectively. In terms of bandwidth selection criteria, AIC, CV and BIC played more meaningful role consecutively. Among the environmental variables, Temperature, Humidity and Evaporation illustrated positive relationship with disease and, elevation and slope showed negative relationship. The maps of the distribution of the disease indicated that the central regions of Guilan province are more prone to this disease than the other areas, and management and control of the disease in these areas is very important. Finally, all results were assessed by validation criteria and decision makers can use this helpful information for prevention programs and allocation of budget to the risky areas.