Journal of Rural Development

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Spatial Modelling of Interactions Between Dengue Incidences and Changing Climate by Integrating ANN Technique with GIS


Affiliations
1 Civil Engineering Department, Malaviya National Institute of Technology, Jaipur - 302017, India
2 Research Scholar and Associate Professor, Respectively, Civil Engineering Department, Malaviya National Institute of Technology, Jaipur-302017, India
     

   Subscribe/Renew Journal


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.

Keywords

Dengue, Outbreaks, Risk, Artificial Neural Networks, Regression.
User
Subscription Login to verify subscription
Notifications
Font Size

  • Hii, Y. L., Rocklöv, J., Ng, N., Tang, C. S., Pang, F. Y. and Sauerborn, R. (2009), 'Climate Variability and Increase in intensity and Magnitude of Dengue Incidence in Singapore', Global Health Action, 2(1), pp. 20-36.

  • Kumar, S., Singh, M. and Chakraborty, A. (2015), 'Climatic Imbalance and their Effect on Prevalence of Dengue Fever in India', International Journal of Current Microbiology and Applied Sciences, 4(11), pp. 185-191.

  • Lee, S. and Sambath, T. (2006), 'Landslide susceptibility Mapping in the Damrei Romel area, Cambodia Using Frequency Ratio and Logistic Regression Models', Environmental Geology, 50(6), pp. 847–855.

  • Looney, C. G. (2002), 'Radial Basis Functional Link Nets and Fuzzy Reasoning', Neurocomputing. Elsevier, 48(1–4), pp. 489–509.

  • Pinto, E., Coelho, M. and Oliver, L. (2011), 'The Influence of Climate Variables on Dengue in Singapore', International Journal of Environmental Health Research, 21(6), pp. 415-426.

  • Tsegaw, T., Gadisa, E., Seid, A., Abera, A., Teshome, A., Mulugeta, A., Herrero, M., Argaw, D., Jorge, A. and Aseffa, A. (2013) 'Identification of Environmental Parameters and Risk Mapping of Visceral Leishmaniasis in Ethiopia by Using Geographical Information Systems and a Statistical Approach', Geospatial Health, 7(2), pp. 299–308.

  • Ughelli, V., Lisnichuk, Y., Paciello, J., Pane, J. (2017), 'Prediction of Dengue Cases in Paraguay Using Artificial Neural Networks', The 3rd Int'l Conf on Health Informatics and Medical Systems.

  • Vikram, K., Nagpal, B. N., Pande, V., Srivastava, A., Gupta, S. K., Paul, R., Valecha, N. and Telle, O. (2015), 'Comparison of Ae . Aegypti Breeding in Localities of Different Socio-economic Groups of Delhi , India', International Journal of Mosquito Research, 2(2), pp. 83–88.

  • WHO (2013), 'Factsheet: Dengue and Severe Dengue'.

  • WHO (2014), 'Factsheet: Dengue and Severe Dengue'.

  • Xu, H., Fu, X., Lee, L., Ma, S., Goh, K. and Wong, J. (2014), 'Statistical Modeling Reveals the Effect of Absolute Humidity on Dengue in Singapore', PLOS Neglected Tropical Diseases,8(5), pp. e2805.

  • Yang, C. (2016) A Comparison of Four Methods of Diseases Mapping. Department of Physical Geography and Ecosystem Science, Lund University, Sweden.


Abstract Views: 220

PDF Views: 2




  • Spatial Modelling of Interactions Between Dengue Incidences and Changing Climate by Integrating ANN Technique with GIS

Abstract Views: 220  |  PDF Views: 2

Authors

Mala Shuchi
Civil Engineering Department, Malaviya National Institute of Technology, Jaipur - 302017, India
Mahesh Kumar Jat
Research Scholar and Associate Professor, Respectively, Civil Engineering Department, Malaviya National Institute of Technology, Jaipur-302017, India

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.

Keywords


Dengue, Outbreaks, Risk, Artificial Neural Networks, Regression.

References





DOI: https://doi.org/10.25175/jrd%2F2018%2Fv37%2Fi2%2F129670