SPATIAL MODELLING OF INTERACTIONS BETWEEN DENGUE INCIDENCES AND CHANGING CLIMATE BY INTEGRATING ANN TECHNIQUE WITH GIS
DOI:
https://doi.org/10.25175/jrd/2018/v37/i2/129670Keywords:
Dengue, Outbreaks, Risk, Artificial Neural Networks, Regression.Abstract
According to various studies, it is well established that climate characteristics are one of the significant factors influencing vector-borne diseases and their longterm variations in a climate change scenario may affect the vector-borne diseases. According to World Health Organisation (WHO) factsheets (2016), more than 2.5 billion people in over 100 countries are at risk of dengue alone which is one of the deadliest vector-borne diseases. Therefore, it is very vital to develop a surveillance system which is capable of predicting the high-risk areas so that the proactive and effective control measures can be taken immediately. In the present study, a spatial data mining model is developed by integrating artificial neural network (ANN) technique into a Geographic Information System (GIS). A statistical method such as logistic regression has been used to detect the areas where the prevalence of the disease is high. Also, possible associations between disease incidences and meteorological parameters have been investigated. This model will highlight areas which are at high risk of dengue by examining the interactions between dengue fever incidences and environment. The primary purpose of the present work is to provide a better understanding of the spatial dispersal of the dengue fever risk in the rural as well as the urban areas of Delhi. Also, this model will bring new insights to the public health officials and policymakers to reduce the risk of deaths mainly in rural areas due to lack of awareness and health facilities.Downloads
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