Application FCM in Modelling DIR for Selangor Using Negative Binomial GAM

  • Authors

    • Nazeera Mohamad
    • Norziha Che Him
    • Mohd Saifullah Rusiman
    • Suliadi Sufahani
    • Siti Afiqah Muhammad Jamil
    2018-11-30
    https://doi.org/10.14419/ijet.v7i4.30.21991
  • AIC, DIR, Fuzzy C-Means, membership function, negative binomial GAM

  • This study attempts to obtain the best fitted model among two clusters which describe the relationship between dengue incidence rate (DIR) and relevant covariates such as climatic and non-climatic variables. The significant variables include amount of rainfall and number of rainy days with lag 0 until 3 months, number of locality and population density. Fuzzy C-Means clustering (FCM) was applied in clustering DIR data based on the value of membership function. The boundary of membership function has been set as 0.5. There are two clusters identified in this study with Cluster 1 consist of 569 data and Cluster 2 consist of 43 data. Then, this study developed models to predict future dengue incidences in Selangor by using negative binomial Generalised Additive Model (GAM). The result shows that the model able to be one of tools for future development in controlling and reducing the number of dengue cases particularly in Selangor, Malaysia as well as other states.

  • References

    1. [1] Alto BW & Bettinardi D (2013), Temperature and dengue virus infection in mosquitoes: independent effects on the immature and adult stages, American Journal of Tropical Medicine and Hygiene 88(3), pp.497-505.

      [2] Murray NEA, Quam MB & Wilder-Smith A (2013), Epidemiology of dengue: past, present and future prospects, Clinical Epidemiology, 5, pp.299-309.

      [3] Skae FMT (1902), Dengue fever in Penang, The British Medical Journal, 2(2185), pp.1581-1582.

      [4] Qi X, Weng Y, Li Y, Meng Y, Chen Q, Ma J & Gao GF (2015), The effects of socioeconomic and environmental factors on the incidence of dengue fever in the Pearl River Delta, China, 2013, Public Library of Science Neglected Tropical Diseases, 9(10), e0004159.

      [5] Wan Fairos WY, Wan Azaki WH, Mohamad Alias Y & Bee Wah Y (2010), Modelling dengue fever (DF) and dengue haemorrhagic fever (DHF) outbreak using Poisson and Negative Binomial model, International Journal of Mathematical, Computational, Physical, Electrical and Computer Engineering, 4(2), pp.1-6.

      [6] Lee HL, Rohani A, Khadri MS, Rozilawati H, Nurulhusna AH, Nor Afizah AH, Roziah A, Rosilawati R (2015), Dengue vector control in Malaysia-challenges and recent advances, The International Medical Journal Malaysia, 14(1), pp.11-16.

      [7] Gubler DJ (1998), Dengue and dengue hemorrhagic fever, Clinical Microbiology Reviews. 11(3), pp.480-496.

      [8] Aura DHM & Alfonso JRM (2010), Potential influence of climate variability on dengue incidence registered in a western pediatric Hospital of Venezuela, Tropical Biomedicine, 27(2), pp.280-286.

      [9] Che Him N, Bailey TC & Stephenson DB (2012), Climate variability and dengue incidence in Malaysia, Proceedings of the 27th International Workshop on Statistical Modelling, Prague 2012, Volume II, pp.435-440.

      [10] Morales I, Salje H, Saha S & Gurley ES (2016), Seasonal distribution and climatic correlates of dengue disease in Dhaka, Bangladesh, The American Journal of Tropical Medicine and Hygiene, 9496, pp.1359-1361.

      [11] Lowe R, Bailey TC, Stephenson DB, Graham RJ, Coelho CA, Carvalho MS & Barcellos C (2011), Spatio-temporal modelling of climate-sensitive disease risk: towards an early warning system for dengue in Brazil, Computers & Geosciences, 37(3), pp.371-381.

      [12] Shah SA & Sani JA (2011), SP6-37 Predicting dengue fever incidence in Selangor using time-series analysis technique, Journal of Epidemiology & Community Health, 65, A464.

      [13] Hassan H, Shohaimi S & Hashim NR (2012), Risk mapping of dengue in Selangor and Kuala Lumpur, Malaysia, Geospatial Health, 7(1), pp.21-25.

      [14] Colon-Gonzalez FJ, Fezzi C, Lake IR & Hunter PR (2013), The effects of weather and climate change on dengue, Public Library of Science Neglected Tropical Diseases, 7(11), e2503.

      [15] Moreno-Banda GL, Riojas-Rodriguez H, Hurtado-Diaz M, Danis-Lozano R & Rothenberg SJ (2017), Effects of climatic and social factors on dengue incidence in Mexican municipalities in the state of Veracruz, Salud Pública de México, 59(1), pp.41-52.

      [16] Dunn JC (1973), A fuzzy relative of the ISODATA process and its use in detecting compact, well-separated clusters, Journal of Cybernatics, 3, pp.32-57.

      [17] Bezdek JC (1981), Pattern recognition with fuzzy objective function algorithm, Plenum Press, New York.

      [18] Hastie T & Tibshirani R (1986), Generalized Additive Models, Statistical Science 3 (1), pp.297-310.

      [19] Che Him N (2015), Potential for using climate forecasts in spatio-temporal prediction of dengue fever incidence in Malaysia (Doctoral dissertation). Retrieved from https://ore.exeter.ac.uk/repository/handle/10871/23205

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  • How to Cite

    Mohamad, N., Him, N. C., Rusiman, M. S., Sufahani, S., & Jamil, S. A. M. (2018). Application FCM in Modelling DIR for Selangor Using Negative Binomial GAM. International Journal of Engineering & Technology, 7(4.30), 1-4. https://doi.org/10.14419/ijet.v7i4.30.21991