Machine-Learning Approach for Risk Estimation and Risk Prediction of the Effect of Climate on Bovine Respiratory Disease
Abstract
:1. Introduction
2. Materials and Methods
Data Sources
3. Models
3.1. Maximum Entropy Approach
3.2. Boosted Regression Tree Approach
- 1
- The training set, , where and represent the independent (features) and dependent variables, respectively.
- 2
- The loss function, . It is differentiable.
- 3
- Number of iterations/trees.
3.3. Model Predictability
3.4. Modelling Framework and Software
4. Results
4.1. Model Assessment and Variable Importance
4.2. Predicted Suitability
4.3. Predicted Future Suitability
4.4. The Effect of Climate Change
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
References
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Acronym | Description | Unit |
---|---|---|
bio1 | Annual Mean Temperature | °C |
bio2 | Mean Diurnal Range (Mean of monthly (maximum temperature–minimum temperature)) | °C |
bio3 | Isothermality (BIO2/BIO7) | °C |
bio4 | Temperature Seasonality (SD ) | % |
bio5 | Max Temperature of Warmest Month | °C |
bio6 | Min Temperature of Coldest Month | °C |
bio7 | Temperature Annual Range (BIO5-BIO6) | °C |
bio8 | Mean Temperature of Wettest Quarter | °C |
bio9 | Mean Temperature of Driest Quarter | °C |
bio10 | Mean Temperature of Warmest Quarter | °C |
bio11 | Mean Temperature of Coldest Quarter | °C |
bio12 | Annual Precipitation | mm |
bio13 | Precipitation of Wettest Month | mm |
bio14 | Precipitation of Driest Month | mm |
bio15 | Precipitation Seasonality (CV) | % |
bio16 | Precipitation of Wettest Quarter | mm |
bio17 | Precipitation of Driest Quarter | mm |
bio18 | Precipitation of Warmest Quarter | mm |
bio19 | Precipitation of Coldest Quarter | mm |
Model | BRD | Training | Test | ||||||
---|---|---|---|---|---|---|---|---|---|
AUC | PPP | Kappa | Sens_Spec. | AUC | PPP | Kappa | Sens_Spec. | ||
MaxEnt | BTD | 0.835 | 0.725 | 0.377 | 0.411 | 0.910 | 0.644 | 0.285 | 0.340 |
IBR | 0.828 | 0.573 | 0.378 | 0.415 | 0.883 | 0.616 | 0.301 | 0.281 | |
BRT | BTD | 0.878 | 0.792 | 0.456 | 0.402 | 0.936 | 0.603 | 0.511 | 0.512 |
IBR | 0.916 | 0.723 | 0.516 | 0.395 | 0.874 | 0.833 | 0.475 | 0.423 |
Variable | % BTD | % IBR |
---|---|---|
bio19 | 19.9 | 25.2 |
bio6 | 17.5 | 9.3 |
bio1 | 12.2 | 18.4 |
bio2 | 10.4 | 9.3 |
bio12 | 8.3 | 3.5 |
bio8 | 6.7 | 2.7 |
bio4 | 3.9 | 1.6 |
bio10 | 3.5 | 5.0 |
bio5 | 3.4 | 0.4 |
bio3 | 2.9 | 3.0 |
bio11 | 2.6 | 3.4 |
bio18 | 2.4 | 7.3 |
bio17 | 2.2 | 2.7 |
bio7 | 1.2 | 0.8 |
bio9 | 1.1 | 0.4 |
bio16 | 1 | 0.4 |
bio14 | 0.5 | 3.4 |
bio13 | 0.3 | 0.7 |
bio15 | 0.1 | 2.3 |
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Gwaka, J.K.; Demafo, M.A.; N’konzi, J.-P.N.; Pak, A.; Olumoh, J.; Elfaki, F.; Adegboye, O.A. Machine-Learning Approach for Risk Estimation and Risk Prediction of the Effect of Climate on Bovine Respiratory Disease. Mathematics 2023, 11, 1354. https://doi.org/10.3390/math11061354
Gwaka JK, Demafo MA, N’konzi J-PN, Pak A, Olumoh J, Elfaki F, Adegboye OA. Machine-Learning Approach for Risk Estimation and Risk Prediction of the Effect of Climate on Bovine Respiratory Disease. Mathematics. 2023; 11(6):1354. https://doi.org/10.3390/math11061354
Chicago/Turabian StyleGwaka, Joseph K., Marcy A. Demafo, Joel-Pascal N. N’konzi, Anton Pak, Jamiu Olumoh, Faiz Elfaki, and Oyelola A. Adegboye. 2023. "Machine-Learning Approach for Risk Estimation and Risk Prediction of the Effect of Climate on Bovine Respiratory Disease" Mathematics 11, no. 6: 1354. https://doi.org/10.3390/math11061354