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International Journal of Mosquito Research
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International Journal of Mosquito Research
Vol. 9, Issue 2, Part B (2022)

Time series forecasting using GMDH neural networks for Chikungunya in Mysore district, India

Author(s): Stavelin Abhinandithe K, Madhu B, Somanathan Balasubramanian and Sridhar Ramachandran
Abstract: Infectious diseases are the diseases which are caused by microorganisms. It plays a vital role in day to day life. Infectious diseases prediction is the growing field in the present scenario. Predicting and forecasting the infectious diseases helps as early warning to prepare for medical emergencies. Among Infectious diseases, chikungunya is an infection spread by mosquitos that carry the dengue and Zika viruses. Infectious illness transmission spread not only depends on vector and host but also depends on environmental/meteorological factors which are a complex process that requires advanced computer approaches such as soft computing and artificial intelligence to estimate and anticipate the complex phenomenon to predict and forecast infectious diseases. In order to predict and forecast the infectious diseases, GMDH techniques are used. The data is collected from Mysore district. We have considered confirmed cases of chikungunya from 2006-2019 along with 19 meteorological variables. A lag period of 0-7 was considered. GMDH models showed that for chikungunya cases, the RMSE value was low for 5/7 with less parameter. Here we have considered the Minimum temperature, mean relative humidity and average sunshine as the significant predictors in predicting and forecasting the chikungunya incidences.
Pages: 111-116  |  51 Views  5 Downloads
How to cite this article:
Stavelin Abhinandithe K, Madhu B, Somanathan Balasubramanian, Sridhar Ramachandran. Time series forecasting using GMDH neural networks for Chikungunya in Mysore district, India. Int J Mosq Res 2022;9(2):111-116. DOI: https://doi.org/10.22271/23487941.2022.v9.i2b.607
International Journal of Mosquito Research