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

Gwaka, Joseph K., Demafo, Marcy A., N’konzi, Joel-Pascal N., Pak, Anton, Olumoh, Jamiu, Elfaki, Faiz, and Adegboye, Oyelola A. (2023) Machine-Learning Approach for Risk Estimation and Risk Prediction of the Effect of Climate on Bovine Respiratory Disease. Mathematics, 11 (6). 1354.

[img]
Preview
PDF (Published Version) - Published Version
Available under License Creative Commons Attribution.

Download (3MB) | Preview
View at Publisher Website: https://doi.org/10.3390/math11061354
 
201


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 km2) 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.

Item ID: 78362
Item Type: Article (Research - C1)
ISSN: 2227-7390
Keywords: bio-security, boosted regression, bovine respiratory diseases, climate change, ecological model, machine learning, one health, species distribution modelling
Copyright Information: © 2023 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Date Deposited: 11 Oct 2023 02:03
FoR Codes: 32 BIOMEDICAL AND CLINICAL SCIENCES > 3202 Clinical sciences > 320211 Infectious diseases @ 50%
42 HEALTH SCIENCES > 4202 Epidemiology > 420204 Epidemiological methods @ 50%
SEO Codes: 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280112 Expanding knowledge in the health sciences @ 100%
Downloads: Total: 201
Last 12 Months: 17
More Statistics

Actions (Repository Staff Only)

Item Control Page Item Control Page