پديد آورندگان :
شيرزاد، رضا دانشگاه صنعتي خواجه نصيرالدين طوسي - دانشكده مهندسي نقشه برداري - سيستمهاي اطلاعات مكاني , آل شيخ، علي اصغر دانشگاه صنعتي خواجه نصيرالدين طوسي - دانشكده مهندسي نقشه برداري , اصغرزاده نشلي، مجتبي دانشگاه صنعتي خواجه نصيرالدين طوسي - دانشكده مهندسي نقشه برداري
كليدواژه :
سيستم اطلاعات مكاني , لپتوسپيروز , تب شاليزار , Maximum Entropy Model , مدل‌سازي مكاني , جك نايف
چكيده فارسي :
بيماري لپتوسپيروز كه در ايران بيشتر با نام تب شاليزار شناخته ميشود، امروزه به‌عنوان يكي از شايعترين بيماريهاي مشترك ميان انسان و دام ( زئونوزها) ميباشد كه از آن به‌عنوان يك بيماري فراموش‌شده و درعين‌حال با اهميت ياد ميشود. اين بيماري با عوامل محيطي ازجمله آب و هوا، پوشش زمين و ارتفاع و حتي عوامل اقتصادي - اجتماعي مانند وضعيت بهداشت محل سكونت و شغل وابستگي شديدي دارد. با كمك سيستم اطلاعات مكاني و قابليتهاي پيشرفته آن ميتوان با توليد نقشه هاي پيشبيني ريسك، براي شناسايي مناطق تحت خطر شيوع بيماريها به تصميم گيران بهداشت عمومي كشور كمك شاياني نمود. هدف اين مطالعه شناسايي ميزان تأثير فاكتورهاي محيطي بر روي الگوي شيوع لپتوسپيروز به‌منظور توليد نقشه پيشبيني ريسك در كل ايران ميباشد. اين امر با كمك آمار بيماري در يك دوره ده‌ساله از سالهاي 2009 تا 2018 به‌صورت نقطه اي و با به‌كارگيري قابليتهاي سيستم اطلاعات مكاني و سنجش‌ازدور و هم‌چنين الگوريتم حداكثر آنتروپي (MAXENT) به‌عنوان يك روش مدل‌سازي كارآمد و با دقت، صورت گرفته است. فاكتورهاي به‌كاررفته شامل بارندگي، ارتفاع، پوشش زمين، شاخص پوشش گياهي نرمال شده، ميانگين دما، بيشينه دماي ماهيانه، شيب، آب‌هاي سطحي و نقشه مناطق جابه جايي ميباشد. نتايج اين تحقيق نشان داد كه علاوه بر سه استان شمالي ايران، مناطق شمال غربي و غرب كشور نيز از خطر شيوع اين بيماري در امان نيستند. بارش و ارتفاع به‌عنوان دو پارامتر اصلي تأثيرگذار در توزيع حال حاضر لپتوسپيروز شناخته شدند و در مقابل شيب و آب‌هاي سطحي مشاركت نزديك به صفر در مدل به‌عنوان كم تاثيرترين فاكتورها محاسبه شدند. در اين مطالعه نقشه هاي پيشبيني ريسك براي شيوع بيماري لپتوسپيروز به نمايش گذاشته شده است كه ميتواند به‌منظور كنترل و پيشگيري شيوع اين بيماري نه‌تنها براي سه استان شمالي كشور بلكه براي تمام ايران مورداستفاده قرار گيرد.
چكيده لاتين :
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.
