Assessing the Nationwide COVID-19 Risk in Mexico through the Lens of Comorbidity by an XGBoost-Based Logistic Regression Model
Abstract
:1. Introduction
2. Materials and Methods
2.1. Available Data
2.2. Logistic Regression
2.3. XGBoost
3. Results
3.1. Spatiotemporal Distribution of Daily Cases
3.2. Spatiotemporal Distribution of Individuals with Comorbidities
3.3. Classification Analysis with XGBoost-LR and Classic LR
3.4. Predicting New Cases
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
COVID-19 | `CO’ stands for corona, ‘VI’ for virus, ‘D’ for disease, and ‘19’ for 2019 |
CONABIO | National Commission for the Knowledge and Use of Biodiversity |
COPD | Chronic obstructive pulmonary disease |
DGE | Directorate General for Epidemiology |
ROC | Receiver Operating Characteristic |
SARS-CoV-2 | Severe acute respiratory syndrome coronavirus 2 |
WHO | World Health Organization |
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Variable | Description | Units | Input Data Source 1,2 | |
---|---|---|---|---|
1 | Date | − | Days | DGE |
2 | Gender | 0: Male, 1: Female | − | DGE |
3 | Age | − | Years | GDE |
4 | Asthma | 0: No, 1: Yes | − | DGE |
5 | Diabetes | 0: No, 1: Yes | − | DGE |
6 | Cardiovascular disease | 0: No, 1: Yes | − | DGE |
7 | COPD | 0: No, 1: Yes | − | DGE |
8 | Hypertension | 0: No, 1: Yes | − | DGE |
9 | Obesity | 0: No, 1: Yes | − | DGE |
10 | Chronic kidney disease | 0: No, 1: Yes | − | DGE |
11 | Smoking | 0: No, 1: Yes | − | DGE |
12 | Pneumonia | 0: No, 1: Yes | − | DGE |
13 | Other disease | 0: No, 1: Yes | − | DGE |
14 | Longitude | X UTM coordinate | m | CONABIO |
15 | Latitude | Y UTM coordinate | m | CONABIO |
Variable | Yes | No |
---|---|---|
Hypertension | 19.00% | 81.00% |
Pneumonia | 9.00% | 91.00% |
Obesity | 15.50% | 84.50% |
Diabetes | 14.17% | 85.83% |
Smoking | 10.02% | 89.98% |
Asthma | 3.77% | 96.23% |
Chronic kidney failure | 1.88% | 98.12% |
Cardiovascular disease | 1.94% | 98.06% |
Other disease | 2.6% | 97.40% |
COPD | 1.32% | 98.68% |
Variable | Yes | No |
---|---|---|
Hypertension | 21.62% | 78.38% |
Pneumonia | 15.62% | 84.38% |
Obesity | 17.67% | 82.33% |
Diabetes | 16.53% | 83.47% |
Smoking | 8.98% | 91.02% |
Asthma | 3.37% | 96.63% |
Chronic kidney failure | 1.93% | 98.07% |
Cardiovascular disease | 1.95% | 98.05% |
Other disease | 2.78% | 97.22% |
COPD | 1.37% | 98.63% |
Model | Sensitivity | Specificity | Accuracy |
---|---|---|---|
XGBoost | 66.11% | 70.11% | 68.50% |
Multivariate logistic regression | 43.63% | 70.49% | 59.69% |
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Venancio-Guzmán, S.; Aguirre-Salado, A.I.; Soubervielle-Montalvo, C.; Jiménez-Hernández, J.d.C. Assessing the Nationwide COVID-19 Risk in Mexico through the Lens of Comorbidity by an XGBoost-Based Logistic Regression Model. Int. J. Environ. Res. Public Health 2022, 19, 11992. https://doi.org/10.3390/ijerph191911992
Venancio-Guzmán S, Aguirre-Salado AI, Soubervielle-Montalvo C, Jiménez-Hernández JdC. Assessing the Nationwide COVID-19 Risk in Mexico through the Lens of Comorbidity by an XGBoost-Based Logistic Regression Model. International Journal of Environmental Research and Public Health. 2022; 19(19):11992. https://doi.org/10.3390/ijerph191911992
Chicago/Turabian StyleVenancio-Guzmán, Sonia, Alejandro Ivan Aguirre-Salado, Carlos Soubervielle-Montalvo, and José del Carmen Jiménez-Hernández. 2022. "Assessing the Nationwide COVID-19 Risk in Mexico through the Lens of Comorbidity by an XGBoost-Based Logistic Regression Model" International Journal of Environmental Research and Public Health 19, no. 19: 11992. https://doi.org/10.3390/ijerph191911992