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

Joseph K. Gwaka, Marcy A. Demafo, Joel-Pascal N. N’konzi, Anton Pak, Jamiu Olumoh, Faiz Elfaki and Oyelola A. Adegboye ()
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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
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
Oyelola A. Adegboye: Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, QLD 4811, Australia

Mathematics, 2023, vol. 11, issue 6, 1-18

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.

Keywords: bovine respiratory diseases; bio-security; climate change; ecological model; species distribution modelling; machine learning; boosted regression; one health (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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