IDEAS home Printed from https://ideas.repec.org/a/spr/endesu/v24y2022i10d10.1007_s10668-021-01920-0.html
   My bibliography  Save this article

Predicting climate change and its impact on future occurrences of vector-borne diseases in West Bengal, India

Author

Listed:
  • Jayanta Mondal

    (University of Gour Banga)

  • Arijit Das

    (University of Gour Banga)

  • Rumki Khatun

    (University of Gour Banga)

Abstract

Climate change is a concerning matter nowadays. It has a long-term effect on human health by spreading vector-borne diseases throughout the world, and West Bengal is not an exception. Vector-borne diseases are life-threatening risk for human; approximately 27,437 people have been infected (2016) every year by this giant killer in West Bengal of India. Temperature and rainfall, two important parameters, have directly influenced the vector-borne diseases. An association between vector-borne diseases and climatic conditions has been established by using geographically weighted regression (GWR) technique. GWR resulted overall r square value more than 0.523 in every case of diseases signifies that the climatic parameters (temperature and rainfall) and vector-borne diseases (Dengue, Malaria, Japanese Encephlities) are strongly correlated. The climatic parameters and positive cases of diseases were mapped out by using inverse distance weight (IDW) interpolation technique in this study. Artificial neural network (ANN) was performed to predict and forecast the climatic condition. The predicted findings have been validated by root mean square error (RMSE) (temperature: 0.301; rainfall: 0.380, i.e., acceptable). This study revealed an insight between climate variables and vector-borne cases in different districts of West Bengal to better understand the effects of climate variability on these diseases. A novel approach of this study is to forecast the spreading of vector-borne diseases for incoming day in West Bengal. After a critical analysis, temperature and rainfall were found to be potent factors for the development of vectors (Aedes Aegypti and Aedes albopictus), and based on this, the risk of vector-borne diseases has been predicted for upcoming years. Forecasted climatic parameters showed that almost all the districts of West Bengal would be reached in a climatic condition where there would be a chance of spreading of vector-borne diseases.

Suggested Citation

  • Jayanta Mondal & Arijit Das & Rumki Khatun, 2022. "Predicting climate change and its impact on future occurrences of vector-borne diseases in West Bengal, India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(10), pages 11871-11894, October.
  • Handle: RePEc:spr:endesu:v:24:y:2022:i:10:d:10.1007_s10668-021-01920-0
    DOI: 10.1007/s10668-021-01920-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10668-021-01920-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10668-021-01920-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wendy B. Foden & Bruce E. Young & H. Resit Akçakaya & Raquel A. Garcia & Ary A. Hoffmann & Bruce A. Stein & Chris D. Thomas & Christopher J. Wheatley & David Bickford & Jamie A. Carr & David G. Hole &, 2019. "Climate change vulnerability assessment of species," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 10(1), January.
    2. Carolin Mabel, M. & Fernandez, E., 2008. "Analysis of wind power generation and prediction using ANN: A case study," Renewable Energy, Elsevier, vol. 33(5), pages 986-992.
    3. Wei-Chun Tseng & Chi-Chung Chen & Ching-Cheng Chang & Yu-Hsien Chu, 2009. "Estimating the economic impacts of climate change on infectious diseases: a case study on dengue fever in Taiwan," Climatic Change, Springer, vol. 92(1), pages 123-140, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hu, Jianming & Wang, Jianzhou & Zeng, Guowei, 2013. "A hybrid forecasting approach applied to wind speed time series," Renewable Energy, Elsevier, vol. 60(C), pages 185-194.
    2. Ata, Rasit, 2015. "Artificial neural networks applications in wind energy systems: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 534-562.
    3. Koo, Junmo & Han, Gwon Deok & Choi, Hyung Jong & Shim, Joon Hyung, 2015. "Wind-speed prediction and analysis based on geological and distance variables using an artificial neural network: A case study in South Korea," Energy, Elsevier, vol. 93(P2), pages 1296-1302.
    4. Le, Anh-Tuan & Tran, Thao Phuong & Mishra, Anil V., 2023. "Climate risk and bank stability: International evidence," Journal of Multinational Financial Management, Elsevier, vol. 70.
    5. Lei, Nuoa & Masanet, Eric, 2020. "Statistical analysis for predicting location-specific data center PUE and its improvement potential," Energy, Elsevier, vol. 201(C).
    6. Cheng-Te Lin & Yu-Sheng Huang & Lu-Wen Liao & Chung-Te Ting, 2020. "Measuring Consumer Willingness to Pay to Reduce Health Risks of Contracting Dengue Fever," IJERPH, MDPI, vol. 17(5), pages 1-15, March.
    7. Boris O. K. Lokonon & Aly A. Mbaye, 2019. "Implications of Climate-Related Factors on Living Standards: Evidence from Sub-Saharan Africa," Economics Bulletin, AccessEcon, vol. 39(2), pages 1404-1417.
    8. Sojung Kim & Junyoung Seo & Sumin Kim, 2024. "Machine Learning Technologies in the Supply Chain Management Research of Biodiesel: A Review," Energies, MDPI, vol. 17(6), pages 1-15, March.
    9. Khaled, Mohamed & Ibrahim, Mostafa M. & Abdel Hamed, Hesham E. & AbdelGwad, Ahmed F., 2019. "Investigation of a small Horizontal–Axis wind turbine performance with and without winglet," Energy, Elsevier, vol. 187(C).
    10. Ouammi, Ahmed & Zejli, Driss & Dagdougui, Hanane & Benchrifa, Rachid, 2012. "Artificial neural network analysis of Moroccan solar potential," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 4876-4889.
    11. Catalão, J.P.S. & Pousinho, H.M.I. & Mendes, V.M.F., 2011. "Short-term wind power forecasting in Portugal by neural networks and wavelet transform," Renewable Energy, Elsevier, vol. 36(4), pages 1245-1251.
    12. Xiao, Yulong & Zou, Chongzhe & Chi, Hetian & Fang, Rengcun, 2023. "Boosted GRU model for short-term forecasting of wind power with feature-weighted principal component analysis," Energy, Elsevier, vol. 267(C).
    13. Aklesso Egbendewe-Mondzozo & Mark Musumba & Bruce A. McCarl & Ximing Wu, 2011. "Climate Change and Vector-borne Diseases: An Economic Impact Analysis of Malaria in Africa," IJERPH, MDPI, vol. 8(3), pages 1-18, March.
    14. Jesús Ferrero Bermejo & Juan Francisco Gómez Fernández & Rafael Pino & Adolfo Crespo Márquez & Antonio Jesús Guillén López, 2019. "Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants," Energies, MDPI, vol. 12(21), pages 1-18, October.
    15. Wang, Jianzhou & Qin, Shanshan & Zhou, Qingping & Jiang, Haiyan, 2015. "Medium-term wind speeds forecasting utilizing hybrid models for three different sites in Xinjiang, China," Renewable Energy, Elsevier, vol. 76(C), pages 91-101.
    16. Salcedo-Sanz, Sancho & Ángel M. Pérez-Bellido, & Ortiz-García, Emilio G. & Portilla-Figueras, Antonio & Prieto, Luis & Paredes, Daniel, 2009. "Hybridizing the fifth generation mesoscale model with artificial neural networks for short-term wind speed prediction," Renewable Energy, Elsevier, vol. 34(6), pages 1451-1457.
    17. Yingying Sun & Ziqiang Han, 2018. "Climate Change Risk Perception in Taiwan: Correlation with Individual and Societal Factors," IJERPH, MDPI, vol. 15(1), pages 1-12, January.
    18. Cadenas, Erasmo & Rivera, Wilfrido, 2010. "Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA–ANN model," Renewable Energy, Elsevier, vol. 35(12), pages 2732-2738.
    19. -, 2011. "An economic assessment of the impact of climate change on the health sector in Montserrat," Sede Subregional de la CEPAL para el Caribe (Estudios e Investigaciones) 38589, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL).
    20. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:endesu:v:24:y:2022:i:10:d:10.1007_s10668-021-01920-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.