COVID-19 Risk Mapping with Considering Socio-Economic Criteria Using Machine Learning Algorithms
Abstract
:1. Introduction
2. Materials and Methods
2.1. Methodology
2.2. Study Area
2.3. The Effective Criteria
2.4. Methods
2.4.1. The RF Algorithm
2.4.2. The LR Algorithm
2.4.3. The ANFIS Algorithm
2.4.4. Feature Selection Using OneR Technique
2.4.5. Pearson Correlation Technique
2.4.6. Validation
3. Results
3.1. Feature Selection
3.2. Correlation between COVID-19 and Land Use
3.3. COVID-19 Modeling Process
3.4. Validation of COVID-19 Risk Maps
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Land Use | Number of Land Uses | Format |
---|---|---|
ATM | 1084 | Point |
Bank | 2378 | Point |
Bakery | 900 | Point |
Fuel station | 102 | Point |
Hospital | 196 | Point |
Pharmacy | 661 | Point |
Supermarket | 443 | Point |
Public transportation station | 2113 | Point |
RF | ANFIS | LR | ||||
---|---|---|---|---|---|---|
Train | Test | Train | Test | Train | Test | |
RMSE | 0.1963 | 0.549 | 0.277 | 0.557 | 0.365 | 0.571 |
MAE | 0.176 | 0.511 | 0.2511 | 0.520 | 0.33 | 0.526 |
Variable | Coefficient | Std. Error |
---|---|---|
Public transportation stations | 0.794 | 0.434 |
Banks | 0.075 | 0.42 |
Pharmacies | 0.899 | 0.276 |
Fuel stations | 0.747 | 0.214 |
Bakeries | 0.4 | 0.397 |
Hospitals | 0.515 | 0.413 |
ATMs | 0.057 | 0.316 |
Supermarkets | 0.499 | 0.352 |
Constant | 0.586 | - |
Algorithms | AUC | SE | 95% CI |
---|---|---|---|
RF | 0.803 | 0.0343 | 0.734–0.861 |
ANFIS | 0.758 | 0.0381 | 0.685–0.821 |
LR | 0.747 | 0.0381 | 0.673–0.812 |
Pair-Wise Algorithm | Z Value | p-Value | Significant |
---|---|---|---|
RF-ANFIS | 6.3 | <0.0001 | Yes |
RF-LR | 5.8 | <0.0001 | Yes |
LR-ANFIS | 3.7 | <0.0001 | Yes |
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Razavi-Termeh, S.V.; Sadeghi-Niaraki, A.; Farhangi, F.; Choi, S.-M. COVID-19 Risk Mapping with Considering Socio-Economic Criteria Using Machine Learning Algorithms. Int. J. Environ. Res. Public Health 2021, 18, 9657. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18189657
Razavi-Termeh SV, Sadeghi-Niaraki A, Farhangi F, Choi S-M. COVID-19 Risk Mapping with Considering Socio-Economic Criteria Using Machine Learning Algorithms. International Journal of Environmental Research and Public Health. 2021; 18(18):9657. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18189657
Chicago/Turabian StyleRazavi-Termeh, Seyed Vahid, Abolghasem Sadeghi-Niaraki, Farbod Farhangi, and Soo-Mi Choi. 2021. "COVID-19 Risk Mapping with Considering Socio-Economic Criteria Using Machine Learning Algorithms" International Journal of Environmental Research and Public Health 18, no. 18: 9657. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18189657