پيش بيني مكاني-زماني مناطق پرخطر بيماري لپتوسپيروز با استفاده از روش هاي رگرسيون وزندار جغرافيايي و شبكه عصبي پرسپترون چندلايه

Predictive Map of Spatio-Temporal Distribution of Leptospirosis Using Geographical Weighted Regression and Multilayer Perceptron Neural Network Methods

تشخيص عوامل بيماري­زا، شناسايي تجمع مكاني بيماري و يافتن الگوي انتشار آن در محيط از ضروري­ترين نيازها در زمينه بهداشت عمومي و مديريت بيماري­ها به شمار مي­آيند. بيماري لپتوسپيروز يكي از بيماري­هاي مشترك انسان و دام مي­باشد كه تقريبا در تمام نقاط جهان به­ويژه در مناطق گرمسيري، نيمه­ گرمسيري و نواحي گرم و مرطوب شيوع بيشتري دارد. شرايط آب و هوايي معتدل و مرطوب در استانهاي شمالي ايران اين مناطق را در معرض خطر بيشتر براي ابتلا به اين بيماري قرار داده است. اهداف اصلي اين تحقيق بررسي سالانه توزيع مكاني بيماري لپتوسپيروز، كشف خوشه­ هاي مكاني و مكاني- زماني بيماري و تهيه نقشه پيش­بيني توزيع مكاني- زماني آن در استانهاي شمالي ايران مي­باشد. در اين تحقيق داده­هاي وقوع بيماري لپتوسپيروز به همراه داده­هاي محيطي و توپوگرافي از قبيل ميانگين دماي هوا، ميانگين رطوبت، مجموع بارش ساليانه، ارتفاع، شيب، جهت شيب و تعداد روزهاي يخبندان در سه استان گيلان، مازندران و گلستان از ابتداي سال 1389 تا پايان سال 1393 به صورت ماهانه جمع­آوري شده­اند. به منظور بررسي وجود و يا عدم وجود خودهمبستگي مكاني ميان موارد وقوع بيماري لپتوسپيروز از شاخص­هاي عمومي اندازه گيري خودهمبستگي مكاني همچون Moran’s I و General G استفاده شده است. آزمون­هاي خودهمبستگي عمومي براي هر 6 سال به صورت جداگانه فرض تصادفي بودن توزيع بيماري در منطقه را رد كرده و بيانگر اين است كه توزيع بيماري در منطقه در هر 6 سال خوشه­اي مي­باشد. به منظور كشف خوشه ­هاي مكاني و شناسايي مناطق پرخطر بيماري لپتوسپيروز از شاخص­هاي محلي كشف خوشه­ هاي مكاني از قبيل Local Moran’s I و Gi Local استفاده شده است. در اين تحقيق به منظور تهيه نقشه پيش­بيني توزيع مكاني- زماني بيماري لپتوسپيروز، از دو مدل رگرسيون وزندار جغرافيايي و شبكه عصبي پرسپترون چند لايه استفاده شده است. در نهايت معيارهاي ضريب كاپا، RMSE، MAPE و R2 جهت ارزيابي عملكرد مدلهاي پيشنهادي به كار گرفته شده ­اند.

Detection of pathogenic factors, identify the spatial accumulation of disease cases and finding its distribution pattern are of urgent need in the field of public health and disease management and control. Leptospirosis is a zoonotic disease which occurs worldwide but is most common in tropical and subtropical areas with high rainfall. Wet and mild weather conditions in the northern provinces of Iran have put these areas at high risk for Leptospirosis incidence. The main objectives of this study are to investigate the annual pattern of Leptospirosis distribution, identify the spatial and spatio-temporal clusters of the disease and generate the annual predictive map of spatio-temporal distribution of Leptospirosis at the district level in the Northern provinces of Iran. In this study, Leptospirosis incidences, census data and topographical and climate factors have been used to generate the annual predictive map of spatio-temporal distribution of Leptospirosis. The Leptospirosis incidences are continuously recorded by the Center for Disease Control and Prevention of Ministry of Health of Iran. The population census count estimates for period 2009-2014 were obtained from the Statistical Center of Iran. Topographical data were used to generate the altitude, slope and aspect maps. Climate data such as average temperature, average humidity, annual rainfall and number of freezing days were used to model other affecting parameters. Global clustering methods including Moran’s I and general G indices were applied to investigate the existence of spatial autocorrelation between cases of Leptospirosis and also analyze the annual spatial distribution of the existing patterns. Results of both Moran’s I and general G indices indicated meaningful persistent spatial autocorrelation between Leptospirosis cases and highly clustered distribution of Leptospirosis. Additionally, presence of spatial clusters of Leptospirosis and detection of high risk areas of disease were investigated using the local Moran’s I and local G indices. The results of the local Moran’s I and local G indices identified significant spatial clusters of Leptospirosis cases located in central, north-eastern and western parts of Guilan, Mazandaran and Golestan provinces, respectively. Geographically weighted regression (GWR) and multilayer perceptron neural network (MLP) models were used to generate the annual predictive map of spatio-temporal distribution of Leptospirosis and modelling the relation between the distribution of Leptospirosis cases with topographical and climate factors. Performance of GWR and MLP models were compared using Kappa coefficient, RMSE, MAPE and R2 measures. The evaluation results showed that the MLP model was able to predict the incidence rate of Leptospirosis in 2014 for each district with acceptable accuracy. MLP was able to model the relationship between Leptospirosis incidence and factors better than GWR. Additionally, results of both GWR and MLP models showed that average humidity and annual rainfall are most important affecting factors on Leptospirosis incidence in the Northern provinces of Iran, respectively. Such predictive maps can be used to provide essential guidelines for planning of effective control strategies and identification of high risk areas of Leptospirosis which should receive more preventive measures from policy makers and healthcare authorities.