A GIS-Based Artificial Neural Network Model for Spatial Distribution of Tuberculosis across the Continental United States
Abstract
:1. Introduction
2. Material and Methods
2.1. Tuberculosis Data
2.2. Explanatory Data
2.3. Global and Local Clustering
2.4. Artificial Neural Networks
2.5. Model Pre-Processing
2.6. Model Evaluation
3. Results
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Rank | State | No. Hotspot Counties | Percentage (#hotspots/#counties) |
---|---|---|---|
1 | Georgia | 57 | 35.8% |
2 | Texas | 30 | 11.8% |
3 | North Carolina | 23 | 23.0% |
4 | Louisiana | 22 | 34.3% |
5 | Florida | 20 | 29.9% |
6 | California | 17 | 22.7% |
7 | South Carolina | 17 | 37% |
8 | Arkansas | 12 | 16.0% |
9 | Mississippi | 12 | 14.6% |
10 | Alabama | 10 | 14.9% |
POP730 | LFE330 | IPE110 | POP778 | Min Temp | SPR440 | HIS305 | RHI820 | |
---|---|---|---|---|---|---|---|---|
POP730 | 1.000 | 0.051 | 0.041 | 0.064 | −0.124 | −0.078 | −0.138 | −0.024 |
LFE330 | 0.051 | 1.000 | 0.018 | 0.057 | 0.136 | −0.499 | −0.186 | −0.040 |
IPE110 | 0.041 | 0.018 | 1.000 | 0.266 | −0.231 | −0.108 | 0.066 | 0.384 |
POP778 | 0.064 | 0.057 | 0.266 | 1.000 | −0.005 | 0.091 | −0.390 | 0.248 |
Min Temp | −0.124 | 0.136 | −0.231 | −0.005 | 1.000 | 0.066 | −0.032 | 0.308 |
SPR440 | −0.078 | −0.499 | −0.108 | 0.091 | 0.066 | 1.000 | −0.015 | 0.003 |
HIS305 | −0.138 | −0.186 | 0.066 | −0.390 | −0.032 | −0.015 | 1.000 | 0.403 |
RHI820 | −0.024 | −0.040 | 0.384 | 0.248 | 0.308 | 0.003 | 0.403 | 1.000 |
R | R Square | Adjusted R Square | Change Statistics | Durbin–Watson | |||||
---|---|---|---|---|---|---|---|---|---|
R Square Change | F | df1 | df2 | Sig. | |||||
LR | 0.666 a | 0.443 | 0.440 | 0.443 | 184.246 | 8 | 1854 | 0.000 | 2.041 |
Unstandardized Coefficients | Standardized Coefficients | t | Sig. | 95.0% Confidence Interval for B | Collinearity Statistics | |||
---|---|---|---|---|---|---|---|---|
Variables | B | Beta | Lower Bound | Upper Bound | Tolerance | VIF | ||
(Constant) | 0.001 | 0.009 | 0.993 | −0.198 | 0.200 | |||
RHI820 | −0.007 | −0.294 | −11.117 | 0.000 | −0.009 | −0.006 | 0.429 | 2.328 |
LFE330 | −0.023 | −0.166 | −7.929 | 0.000 | −0.029 | −0.017 | 0.683 | 1.463 |
Min Temp | 0.013 | 0.210 | 9.809 | 0.000 | 0.010 | 0.016 | 0.653 | 1.532 |
POP778 | 0.083 | 0.282 | 12.621 | 0.000 | 0.070 | 0.095 | 0.602 | 1.661 |
IPE110 | 0.012 | 0.140 | 6.662 | 0.000 | 0.008 | 0.015 | 0.677 | 1.477 |
SPR440 | −0.009 | −0.097 | −4.703 | 0.000 | −0.013 | −0.005 | 0.701 | 1.426 |
HIS305 | −0.019 | −0.145 | −5.976 | 0.000 | −0.026 | −0.013 | 0.508 | 1.968 |
POP730 | −0.015 | −0.080 | −4.489 | 0.000 | −0.021 | −0.008 | 0.950 | 1.053 |
Model | Training | Cross-Validation | Test | ||||||
---|---|---|---|---|---|---|---|---|---|
MAE | RMSE | R | MAE | RMSE | R | MAE | RMSE | R | |
LR | 0.27 | 0.35 | 0.66 | 0.27 | 0.36 | 0.65 | 0.28 | 0.36 | 0.61 |
MLP (1 hidden layer) | 0.25 | 0.33 | 0.70 | 0.26 | 0.35 | 0.67 | 0.27 | 0.35 | 0.63 |
MLP (2 hidden layers) | 0.26 | 0.34 | 0.69 | 0.26 | 0.35 | 0.65 | 0.27 | 0.36 | 0.62 |
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Mollalo, A.; Mao, L.; Rashidi, P.; Glass, G.E. A GIS-Based Artificial Neural Network Model for Spatial Distribution of Tuberculosis across the Continental United States. Int. J. Environ. Res. Public Health 2019, 16, 157. https://doi.org/10.3390/ijerph16010157
Mollalo A, Mao L, Rashidi P, Glass GE. A GIS-Based Artificial Neural Network Model for Spatial Distribution of Tuberculosis across the Continental United States. International Journal of Environmental Research and Public Health. 2019; 16(1):157. https://doi.org/10.3390/ijerph16010157
Chicago/Turabian StyleMollalo, Abolfazl, Liang Mao, Parisa Rashidi, and Gregory E. Glass. 2019. "A GIS-Based Artificial Neural Network Model for Spatial Distribution of Tuberculosis across the Continental United States" International Journal of Environmental Research and Public Health 16, no. 1: 157. https://doi.org/10.3390/ijerph16010157