IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i6p1354-d1093727.html
   My bibliography  Save this article

Machine-Learning Approach for Risk Estimation and Risk Prediction of the Effect of Climate on Bovine Respiratory Disease

Author

Listed:
  • Joseph K. Gwaka

    (African Institute for Mathematical Sciences, Kigali 20093, Rwanda)

  • Marcy A. Demafo

    (African Institute for Mathematical Sciences, Kigali 20093, Rwanda)

  • Joel-Pascal N. N’konzi

    (African Institute for Mathematical Sciences, Kigali 20093, Rwanda)

  • Anton Pak

    (Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, QLD 4811, Australia
    Centre for the Business and Economics of Health, The University of Queensland, Brisbane, QLD 4067, Australia)

  • Jamiu Olumoh

    (Department of Mathematics, American University of Nigeria, Yola 640001, Nigeria)

  • Faiz Elfaki

    (Statistics Program, Department of Mathematics, Statistics and Physics, Qatar University, Doha P.O. Box 2713, Qatar
    Shared senior authors.)

  • Oyelola A. Adegboye

    (Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, QLD 4811, Australia
    Public Health and Tropical Medicine, College of Public Health, Medical and Veterinary Sciences, James Cook University, Townsville, QLD 4811, Australia
    World Health Organization Collaborating Center for Vector-Borne and Neglected Tropical Diseases, College of Public Health, Medical and Veterinary Sciences, James Cook University, Townsville, QLD 4811, Australia
    Shared senior authors.)

Abstract

Bovine respiratory disease (BRD) is a major cause of illness and death in cattle; however, its global extent and distribution remain unclear. As climate change continues to impact the environment, it is important to understand the environmental factors contributing to BRD’s emergence and re-emergence. In this study, we used machine-learning models and remotely sensed climate data at 2.5 min (21 km 2 ) resolution environmental layers to estimate the risk of BRD and predict its potential future distribution. We analysed 13,431 BRD cases from 1727 cities worldwide between 2005 and 2021 using two machine-learning models, maximum entropy (MaxEnt) and Boosted Regression Trees (BRT), to predict the risk and geographical distribution of the risk of BRD globally with varying model parameters. Different re-sampling regimes were used to visualise and measure various sources of uncertainty and prediction performance. The best-fitting model was assessed based on the area under the receiver operator curve (AUC-ROC), positive predictive power and Cohen’s Kappa. We found that BRT had better predictive power compared with MaxEnt. Our findings showed that favourable habitats for BRD occurrence were associated with the mean annual temperature, precipitation of the coldest quarter, mean diurnal range and minimum temperature of the coldest month. Similarly, we showed that the risk of BRD is not limited to the currently known suitable regions of Europe and west and central Africa but extends to other areas, such as Russia, China and Australia. This study highlights the need for global surveillance and early detection systems to prevent the spread of disease across borders. The findings also underscore the importance of bio-security surveillance and livestock sector interventions, such as policy-making and farmer education, to address the impact of climate change on animal diseases and prevent emergencies and the spread of BRD to new areas.

Suggested Citation

  • Joseph K. Gwaka & Marcy A. Demafo & Joel-Pascal N. N’konzi & Anton Pak & Jamiu Olumoh & Faiz Elfaki & Oyelola A. Adegboye, 2023. "Machine-Learning Approach for Risk Estimation and Risk Prediction of the Effect of Climate on Bovine Respiratory Disease," Mathematics, MDPI, vol. 11(6), pages 1-18, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1354-:d:1093727
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/6/1354/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/6/1354/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Oyelola A. Adegboye & Majeed Adegboye, 2017. "Spatially Correlated Time Series and Ecological Niche Analysis of Cutaneous Leishmaniasis in Afghanistan," IJERPH, MDPI, vol. 14(3), pages 1-14, March.
    2. Philip Thornton & Pierre Gerber, 2010. "Climate change and the growth of the livestock sector in developing countries," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 15(2), pages 169-184, February.
    3. Thornton, Philip K. & Herrero, Mario, 2010. "The inter-linkages between rapid growth in livestock production, climate change, and the impacts on water resources, land use, and deforestation," Policy Research Working Paper Series 5178, The World Bank.
    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. Dilshad Ahmad & Muhammad Afzal, 2021. "Impact of climate change on pastoralists’ resilience and sustainable mitigation in Punjab, Pakistan," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(8), pages 11406-11426, August.
    2. Florence Jacquet & A Aboul-Naga & Bernard Hubert, 2020. "The contribution of ARIMNet to address livestock systems resilience in the Mediterranean region," Post-Print hal-03625860, HAL.
    3. Prasun K. Gangopadhyay & Arun Khatri-Chhetri & Paresh B. Shirsath & Pramod K. Aggarwal, 2019. "Spatial targeting of ICT-based weather and agro-advisory services for climate risk management in agriculture," Climatic Change, Springer, vol. 154(1), pages 241-256, May.
    4. Katerina Zdravkova, 2023. "Personalized Education for Sustainable Development," Sustainability, MDPI, vol. 15(8), pages 1-13, April.
    5. Mudombi, Grace, 2011. "Factors Affecting Perceptions and Responsiveness to Climate Variability Induced Hazards," Research Theses 198517, Collaborative Masters Program in Agricultural and Applied Economics.
    6. Grzegorz Nawalany & Paweł Sokołowski, 2022. "Interaction between a Cyclically Heated Building and the Ground, for Selected Locations in Europe," Energies, MDPI, vol. 15(20), pages 1-17, October.
    7. Jerome Dumortier & Dermot J. Hayes & Miguel Carriquiry & Fengxia Dong & Xiaodong Du & Amani Elobeid & Jacinto F. Fabiosa & Kranti Mulik, 2010. "Modeling the Effects of Pasture Expansion on Emissions from Land-Use Change," Center for Agricultural and Rural Development (CARD) Publications 10-wp504, Center for Agricultural and Rural Development (CARD) at Iowa State University.
    8. Cantarello, Elena & Newton, Adrian C. & Hill, Ross A. & Tejedor-Garavito, Natalia & Williams-Linera, Guadalupe & López-Barrera, Fabiola & Manson, Robert H. & Golicher, Duncan J., 2011. "Simulating the potential for ecological restoration of dryland forests in Mexico under different disturbance regimes," Ecological Modelling, Elsevier, vol. 222(5), pages 1112-1128.
    9. Damir D. Torrico & Xin Nie & Damselina Lukito & Santanu Deb-Choudhury & Scott C. Hutchings & Carolina E. Realini, 2023. "Consumer Attitudes and Acceptability toward Edible New Zealand Native Plants," Sustainability, MDPI, vol. 15(15), pages 1-16, July.
    10. Tshepiso Mangani & Hendri Coetzee & Klaus Kellner & George Chirima, 2020. "Socio-Economic Benefits Stemming from Bush Clearing and Restoration Projects Conducted in the D’Nyala Nature Reserve and Shongoane Village, Lephalale, South Africa," Sustainability, MDPI, vol. 12(12), pages 1-15, June.
    11. Imran Hussain & Abdul Rehman, 2022. "How CO2 emission interacts with livestock production for environmental sustainability? evidence from Pakistan," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(6), pages 8545-8565, June.
    12. Guo Ruo & Brhane Weldegebrial & Genet Yohannes & Gebremedhin Yohannes, 2018. "Climate Change Adaptation Practices by Ruminant Livestock Producer of in Hintalo Wajerat District Tigray Regional State, Northern Ethiopia," Biomedical Journal of Scientific & Technical Research, Biomedical Research Network+, LLC, vol. 11(5), pages 8809-8828, December.
    13. Mensah, Charles & Enahoro, Dolapo, 2022. "Modeling poultry and maize sector interactions in Southern Africa under a changing climate," SocArXiv ehd3j, Center for Open Science.
    14. McCarl, Bruce A. & Attavanich, Witsanu & Musumba, Mark & Mu, Jianhong E. & Aisabokhae, Ruth, 2011. "Land Use and Climate Change," MPRA Paper 83993, University Library of Munich, Germany, revised 2014.
    15. Mudombi, Grace, 2011. "Factors affecting perceptions and responsiveness to climate variability induced hazards," Research Theses 157508, Collaborative Masters Program in Agricultural and Applied Economics.
    16. Marta Kozicka & Petr Havlík & Hugo Valin & Eva Wollenberg & Andre Deppermann & David Leclère & Pekka Lauri & Rebekah Moses & Esther Boere & Stefan Frank & Chris Davis & Esther Park & Noel Gurwick, 2023. "Feeding climate and biodiversity goals with novel plant-based meat and milk alternatives," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    17. Bizimana, Jean-Claude & Bessler, David A. & Angerer, Jay P., 2016. "The 2010-2011 Drought Impacts on Cattle Market Integration in the Horn of Africa: A preliminary Evaluation using VAR and Structural Break Analysis," 2016 Annual Meeting, February 6-9, 2016, San Antonio, Texas 229991, Southern Agricultural Economics Association.
    18. Nyanjige Mbembela Mayala & Mangasini Atanasi Katundu & Elibariki Emmanuel Msuya, 2019. "Socio-cultural Factors influencing livestock investment decisions among Smallholder Farmers in Mbulu and Bariadi Districts, Tanzania," Global Business Review, International Management Institute, vol. 20(5), pages 1214-1230, October.
    19. Haining Liu & Hong Wang & Xiaobing Li & Tengfei Qu & Yao Zhang & Yuting Lu & Yalei Yang & Jiahao Liu & Xili Zhao & Jingru Su & Dingsheng Luo, 2023. "Identification of Constructive Species and Degraded Plant Species in the Temperate Typical Grassland of Inner Mongolia Based on Hyperspectral Data," Agriculture, MDPI, vol. 13(2), pages 1-20, February.
    20. Abdullahi, Abdulazeez & Olatunji , O. I & Bako, Ramatu Usman, 2024. "Climate Change and Small Scale Poultry Production in Selected Local Governmentareas of Kwara State, Nigeria," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 10(12), pages 765-779, January.

    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:jmathe:v:11:y:2023:i:6:p:1354-:d:1093727. 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.