IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i11p6449-d824402.html
   My bibliography  Save this article

The Effects of Meteorological Factors on Dengue Cases in Malaysia

Author

Listed:
  • Sarbhan Singh

    (Institute for Medical Research, Ministry of Health, Shah Alam 40170, Malaysia)

  • Lai Chee Herng

    (Institute for Medical Research, Ministry of Health, Shah Alam 40170, Malaysia)

  • Lokman Hakim Sulaiman

    (Institute for Research, Development and Innovation (IRDI), International Medical University, Kuala Lumpur 57000, Malaysia
    School of Medicine, International Medical University, Kuala Lumpur 57000, Malaysia)

  • Shew Fung Wong

    (School of Medicine, International Medical University, Kuala Lumpur 57000, Malaysia
    Centre for Environmental and Population Health, Institute for Research, Development and Innovation (IRDI), International Medical University, Kuala Lumpur 57000, Malaysia)

  • Jenarun Jelip

    (Vector Borne Disease Control Division, Ministry of Health Malaysia, Putrajaya 62000, Malaysia)

  • Norhayati Mokhtar

    (Vector Borne Disease Control Division, Ministry of Health Malaysia, Putrajaya 62000, Malaysia)

  • Quillon Harpham

    (HR Wallingford, Wallingford OX10 8BA, UK)

  • Gina Tsarouchi

    (HR Wallingford, Wallingford OX10 8BA, UK)

  • Balvinder Singh Gill

    (Institute for Medical Research, Ministry of Health, Shah Alam 40170, Malaysia)

Abstract

Dengue is a vector-borne disease affected by meteorological factors and is commonly recorded from ground stations. Data from ground station have limited spatial representation and accuracy, which can be overcome using satellite-based Earth Observation (EO) recordings instead. EO-based meteorological recordings can help to provide a better understanding of the correlations between meteorological variables and dengue cases. This paper aimed to first validate the satellite-based (EO) data of temperature, wind speed, and rainfall using ground station data. Subsequently, we aimed to determine if the spatially matched EO data correlated with dengue fever cases from 2011 to 2019 in Malaysia. EO data were spatially matched with the data from four ground stations located at states and districts in the central (Selangor, Petaling) and east coast (Kelantan, Kota Baharu) geographical regions of Peninsular Malaysia. Spearman’s rank-order correlation coefficient (ρ) was performed to examine the correlation between EO and ground station data. A cross-correlation analysis with an eight-week lag period was performed to examine the magnitude of correlation between EO data and dengue case across the three time periods (2011–2019, 2015–2019, 2011–2014). The highest correlation between the ground-based stations and corresponding EO data were reported for temperature (mean ρ = 0.779), followed by rainfall (mean ρ = 0.687) and wind speed (mean ρ = 0.639). Overall, positive correlations were observed between weekly dengue cases and rainfall for Selangor and Petaling across all time periods with significant correlations being observed for the period from 2011 to 2019 and 2015 to 2019. In addition, positive significant correlations were also observed between weekly dengue cases and temperature for Kelantan and Kota Baharu across all time periods, while negative significant correlations between weekly dengue cases and temperature were observed in Selangor and Petaling across all time periods. Overall negative correlations were observed between weekly dengue cases and wind speed in all areas from 2011 to 2019 and 2015 to 2019, with significant correlations being observed for the period from 2015 to 2019. EO-derived meteorological variables explained 48.2% of the variation in dengue cases in Selangor. Moderate to strong correlations were observed between meteorological variables recorded from EO data derived from satellites and ground stations, thereby justifying the use of EO data as a viable alternative to ground stations for recording meteorological variables. Both rainfall and temperature were found to be positively correlated with weekly dengue cases; however, wind speed was negatively correlated with dengue cases.

Suggested Citation

  • Sarbhan Singh & Lai Chee Herng & Lokman Hakim Sulaiman & Shew Fung Wong & Jenarun Jelip & Norhayati Mokhtar & Quillon Harpham & Gina Tsarouchi & Balvinder Singh Gill, 2022. "The Effects of Meteorological Factors on Dengue Cases in Malaysia," IJERPH, MDPI, vol. 19(11), pages 1-24, May.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:11:p:6449-:d:824402
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/11/6449/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/11/6449/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Simba Farai Malvern & Chiturumani Maurice, 2018. "Comparison of Satellite Data and Ground Based Weather Data in Masvingo, Zimbabwe," International Journal of Environmental Sciences & Natural Resources, Juniper Publishers Inc., vol. 8(4), pages 102-107, January.
    2. Felipe J Colón-González & Leonardo Soares Bastos & Barbara Hofmann & Alison Hopkin & Quillon Harpham & Tom Crocker & Rosanna Amato & Iacopo Ferrario & Francesca Moschini & Samuel James & Sajni Malde &, 2021. "Probabilistic seasonal dengue forecasting in Vietnam: A modelling study using superensembles," PLOS Medicine, Public Library of Science, vol. 18(3), pages 1-30, March.
    3. Yoon Ling Cheong & Katrin Burkart & Pedro J. Leitão & Tobia Lakes, 2013. "Assessing Weather Effects on Dengue Disease in Malaysia," IJERPH, MDPI, vol. 10(12), pages 1-16, November.
    4. Jureckova, Jana & Picek, Jan, 2007. "Shapiro-Wilk-type test of normality under nuisance regression and scale," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 5184-5191, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Adivânia Cardoso da Silva & Paulo Sérgio Scalize, 2023. "Environmental Variables Related to Aedes aegypti Breeding Spots and the Occurrence of Arbovirus Diseases," Sustainability, MDPI, vol. 15(10), pages 1-21, May.
    2. Luba Pascoe & Thomas Clemen & Karen Bradshaw & Devotha Nyambo, 2022. "Review of Importance of Weather and Environmental Variables in Agent-Based Arbovirus Models," IJERPH, MDPI, vol. 19(23), pages 1-24, November.

    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. Villi Dane M. Go, 2023. "Communicable disease surveillance through predictive analysis: A comparative analysis of prediction models," HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE - ENGINEERING AND TECHNOLOGY, HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE, HO CHI MINH CITY OPEN UNIVERSITY, vol. 13(2), pages 45-54.
    2. Ray, Evan L. & Brooks, Logan C. & Bien, Jacob & Biggerstaff, Matthew & Bosse, Nikos I. & Bracher, Johannes & Cramer, Estee Y. & Funk, Sebastian & Gerding, Aaron & Johansson, Michael A. & Rumack, Aaron, 2023. "Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1366-1383.
    3. Vicente Navarro Valencia & Yamilka Díaz & Juan Miguel Pascale & Maciej F. Boni & Javier E. Sanchez-Galan, 2021. "Assessing the Effect of Climate Variables on the Incidence of Dengue Cases in the Metropolitan Region of Panama City," IJERPH, MDPI, vol. 18(22), pages 1-18, November.
    4. Luong Thi Nguyen & Huy Xuan Le & Dong Thanh Nguyen & Ha Quang Ho & Ting-Wu Chuang, 2020. "Impact of Climate Variability and Abundance of Mosquitoes on Dengue Transmission in Central Vietnam," IJERPH, MDPI, vol. 17(7), pages 1-16, April.
    5. Panja, Madhurima & Chakraborty, Tanujit & Nadim, Sk Shahid & Ghosh, Indrajit & Kumar, Uttam & Liu, Nan, 2023. "An ensemble neural network approach to forecast Dengue outbreak based on climatic condition," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    6. Hao Gui & Sylvia Gwee & Jiayun Koh & Junxiong Pang, 2021. "Weather Factors Associated with Reduced Risk of Dengue Transmission in an Urbanized Tropical City," IJERPH, MDPI, vol. 19(1), pages 1-17, December.
    7. Soós, Gabriella & Biacs, Péter Ákos, 2018. "The role of product-related information and factors impacting consumer attitudes during health-conscious food purchase in Hungary," Studies in Agricultural Economics, Research Institute for Agricultural Economics, vol. 120(1), April.
    8. Mazni Baharom & Norfazilah Ahmad & Rozita Hod & Fadly Syah Arsad & Fredolin Tangang, 2021. "The Impact of Meteorological Factors on Communicable Disease Incidence and Its Projection: A Systematic Review," IJERPH, MDPI, vol. 18(21), pages 1-22, October.
    9. Aswi Aswi & Susanna Cramb & Earl Duncan & Wenbiao Hu & Gentry White & Kerrie Mengersen, 2020. "Bayesian Spatial Survival Models for Hospitalisation of Dengue: A Case Study of Wahidin Hospital in Makassar, Indonesia," IJERPH, MDPI, vol. 17(3), pages 1-12, January.
    10. Prasad Liyanage & Hasitha Tissera & Maquins Sewe & Mikkel Quam & Ananda Amarasinghe & Paba Palihawadana & Annelies Wilder-Smith & Valérie R. Louis & Yesim Tozan & Joacim Rocklöv, 2016. "A Spatial Hierarchical Analysis of the Temporal Influences of the El Niño-Southern Oscillation and Weather on Dengue in Kalutara District, Sri Lanka," IJERPH, MDPI, vol. 13(11), pages 1-21, November.
    11. Sijia Wu & Hongyan Ren & Wenhui Chen & Tiegang Li, 2019. "Neglected Urban Villages in Current Vector Surveillance System: Evidences in Guangzhou, China," IJERPH, MDPI, vol. 17(1), pages 1-15, December.

    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:gam:jijerp:v:19:y:2022:i:11:p:6449-:d:824402. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.