Exploring the Relationship between Melioidosis Morbidity Rate and Local Environmental Indicators Using Remotely Sensed Data
Abstract
:1. Introduction
2. Data and Methods
2.1. Study Area
2.2. Conceptual Framework
2.3. Melioidosis Data
2.4. Spatial Data
2.4.1. LST
2.4.2. Vegetation
2.4.3. Soil Moisture
2.4.4. Rainfall
2.5. Data Preparation and Pre-Processing
2.6. Spatial Statistics
2.6.1. Spatial Autocorrelation
2.6.2. Global Poisson Regression (GPR)
2.6.3. Local Poisson Regression
3. Results
3.1. Melioidosis Morbidity Rate
3.2. Spatial Autocorrelation
3.3. GPR Model
3.4. Local Poisson Regression
3.5. Local Percent Deviance
3.6. Comparison between GPR and GWPR
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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ID | Data | Satellite | Data Full Name | Resolution |
---|---|---|---|---|
1 | LST | MODIS | MOD11A1.006 Terra Land Surface Temperature and Emissivity Daily Global 1 km | 1 km |
2 | NDVI | MODIS | MOD13Q1.006 Terra Vegetation Indices 16-Day | 250 m |
3 | NDWI | MODIS | MODIS Terra Daily NDWI | 463.313 m |
4 | Rainfall | CHIRPS PENTAD | Climate Hazards Group InfraRed Precipitation with Station Data | 5566 m |
Monthly | Moran’s I | Mean | S.D. | z-Value | Pseudo p-Value |
---|---|---|---|---|---|
January | 0.462 | –0.004 | 0.039 | 11.673 | 0.002 |
February | 0.317 | –0.007 | 0.040 | 8.021 | 0.002 |
March | 0.162 | –0.004 | 0.039 | 4.178 | 0.004 |
April | 0.187 | –0.007 | 0.042 | 4.639 | 0.002 |
May | 0.068 | –0.008 | 0.038 | 1.990 | 0.042 |
June | 0.076 | –0.005 | 0.042 | 1.913 | 0.034 |
July | 0.246 | –0.006 | 0.038 | 6.570 | 0.002 |
August | 0.351 | –0.0003 | 0.040 | 8.780 | 0.002 |
September | 0.253 | –0.004 | 0.040 | 6.390 | 0.002 |
October | 0.244 | –0.003 | 0.040 | 6.083 | 0.002 |
November | 0.187 | –0.006 | 0.038 | 4.994 | 0.002 |
December | 0.257 | –0.001 | 0.039 | 6.610 | 0.002 |
Monthly | Intercept | LST | NDVI | NDWI | Rainfall |
---|---|---|---|---|---|
January | –4.546 | 0.135 | 1.82 | –2.329 | 0.077 |
February | –2.289 | 0.046 | –0.055 | 1.34 | 0.16 |
March | 2.804 | –0.082 | –0.193 | –6.077 | –0.025 |
April | 1.762 | –0.054 | –3.555 | –6.519 | 0.005 |
May | –0.546 | 0.004 | –1.581 | –10.219 | –0.003 |
June | –2.625 | 0.073 | 3.209 | 4.1483 | –0.001 |
July | 5.14 | –0.11 | –3.195 | 31.425 | 0.0008 |
August | –2.808 | 0.045 | 0.72 | 34.014 | 0.003 |
September | 2.068 | 0.033 | 0.705 | –8.659 | –0.003 |
October | –2.948 | 0.056 | 6.686 | 1.9126 | –0.006 |
November | 1.991 | –0.018 | –0.823 | 4.952 | –0.023 |
December | 6.755 | –0.208 | 0.088 | –10.567 | 0.130 |
Monthly | GPR | GWPR | Moran’s I | z-Score | p-Value | ||
---|---|---|---|---|---|---|---|
AICc | Deviance | AICc | Deviance | ||||
January | 1197.00 | 0.144 | 432.808 | 0.526 | 0.008 | 0.318 | 0.374 |
February | 924.00 | 0.157 | 403.175 | 0.395 | –0.056 | –1.275 | 0.898 |
March | 859.00 | 0.053 | 430.776 | 0.231 | –0.019 | –0.373 | 0.645 |
April | 828.00 | 0.025 | 431.394 | 0.280 | –0.035 | –0.753 | 0.774 |
May | 850.00 | 0.037 | 436.957 | 0.145 | –0.025 | –0.520 | 0.698 |
June | 952.00 | 0.018 | 441.450 | 0.299 | –0.104 | –2.435 | 0.992 |
July | 974.00 | 0.204 | 406.892 | 0.325 | –0.072 | –1.656 | 0.951 |
August | 1087.00 | 0.121 | 421.122 | 0.416 | –0.048 | –1.071 | 0.858 |
September | 1075.00 | 0.087 | 439.833 | 0.442 | –0.092 | –2.154 | 0.984 |
October | 899.00 | 0.132 | 408.941 | 0.378 | –0.047 | –1.059 | 0.855 |
November | 837.00 | 0.022 | 418.079 | 0.275 | –0.035 | –0.747 | 0.772 |
December | 751.00 | 0.156 | 402.712 | 0.422 | 0.014 | 0.475 | 0.317 |
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Wongbutdee, J.; Jittimanee, J.; Daendee, S.; Thongsang, P.; Saengnill, W. Exploring the Relationship between Melioidosis Morbidity Rate and Local Environmental Indicators Using Remotely Sensed Data. Int. J. Environ. Res. Public Health 2024, 21, 614. https://doi.org/10.3390/ijerph21050614
Wongbutdee J, Jittimanee J, Daendee S, Thongsang P, Saengnill W. Exploring the Relationship between Melioidosis Morbidity Rate and Local Environmental Indicators Using Remotely Sensed Data. International Journal of Environmental Research and Public Health. 2024; 21(5):614. https://doi.org/10.3390/ijerph21050614
Chicago/Turabian StyleWongbutdee, Jaruwan, Jutharat Jittimanee, Suwaporn Daendee, Pongthep Thongsang, and Wacharapong Saengnill. 2024. "Exploring the Relationship between Melioidosis Morbidity Rate and Local Environmental Indicators Using Remotely Sensed Data" International Journal of Environmental Research and Public Health 21, no. 5: 614. https://doi.org/10.3390/ijerph21050614
APA StyleWongbutdee, J., Jittimanee, J., Daendee, S., Thongsang, P., & Saengnill, W. (2024). Exploring the Relationship between Melioidosis Morbidity Rate and Local Environmental Indicators Using Remotely Sensed Data. International Journal of Environmental Research and Public Health, 21(5), 614. https://doi.org/10.3390/ijerph21050614