Predicting WNV Circulation in Italy Using Earth Observation Data and Extreme Gradient Boosting Model
Abstract
:1. Introduction
2. Materials and Methods
2.1. WNV Circulation Dataset (Ground Truth Data)
2.2. EO Products (LSTD, LSTN, NDVI, SSM): Sources and Preparation
2.3. Modelling
3. Results
3.1. WNV Dataset (Ground Truth Data)
3.2. EO Products (LSTD, LSTN, NDVI, SSM): Sources and Preparation
3.3. Modelling
4. Discussion
- The model classifies entomological positive cases better than birds. The worst classification in birds could be due to the fact that the coordinates used represent the place of death, rather than the place of infection and working at 250 m resolution, this aspect can affect also the classification of resident birds of target species. However, it should be noted that only 16 of the 67 bird cases had all the 3 × 3 pixels negative; in the other cases, at least one pixel was predicted as positive, showing a potential risk for the area. In addition, from the epidemiological point of view the detection of WNV in mosquito pools is clearly the best predictor, in time and space, of virus transmission. The distance of flight range can affect the correct location of virus exposure and infection in birds; as well, active movements for riding or other services can influence the exact estimation of the place of infection for horses.
- As far as the negatives are concerned, it is important to notice that only a consistent and frequent monitoring over the same area can be considered satisfying to define a true negative. In the Italian context, this occurs essentially for the entomological subset, where we have evidence of a positivity and the corresponding negativity in the previous period for the same place. In our model, we can distinguish the observed negatives in two groups (pseudo-absence in space and negatives in time, i.e., the entomological subset) and we found more accurate results in predicting negatives in time rather than pseudo-absence in space. The pseudo negatives, created randomly outside the VCA, and used to train the model, do not guarantee the real absence of the virus. The results of the model, however, show us that climatic and environmental conditions favourable to the spread of WNV can also occur in areas where the virus was not detected during 2019, although sporadically detected in the past.
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Year | Birds + | Mosquitoes + (−) | Equids + | Pseudo Absence (−) | Total + (−) |
---|---|---|---|---|---|
2017 | 40 | 43 (90) | 47 | (186) | 130 (276) |
2018 | 216 | 137 (137) | 151 | (804) | 504 (941) |
2019 | 67 | 43 (86) | 8 | (150) | 118 (236) |
Total | 323 | 223 (313) | 206 | (1140) | 752 (1453) |
EO Product | Spatial Resolution | Temporal Resolution | Percentage of NoData Pixels in Source: Median (Min, Max) | Images in the Data-Cube (2016–2019) | ||
---|---|---|---|---|---|---|
Source | Model | Source | Model | |||
MOD11A2 LSTD | 1 km | 250 m | 8 days | 8 days | 0.64% (0.24%, 38.10%) | 184 |
MOD11A2 LSTN | 1 km | 8 days | 8 days | 1.05% (0.25%, 32.93%) | 184 | |
MOD13Q1 NDVI | 250 m | 16 days | 16 days | 0.55% (0.41%, 1.70%) | 92 | |
Copernicus SSM | 1 km | daily | 8 days | * 22.52% (22.3%, 68.25%) | 184 |
Observed Positive | Observed Negative | Total | |
---|---|---|---|
Predicted Positive | 771 | 230 | 1001 |
Predicted Negative | 269 | 1818 | 2087 |
Total | 1040 | 2048 | 3088 |
Birds | Mosquitoes | Equids | Total | |
---|---|---|---|---|
Predicted Positive | 406 | 311 | 54 | 771 |
Predicted Negative | 184 | 67 | 18 | 269 |
Total | 590 | 378 | 72 | 1040 |
Pseudo-Absence in Space | Negatives in Time | Total | |
---|---|---|---|
Predicted Positive | 208 | 22 | 230 |
Predicted Negative | 1138 | 680 | 1818 |
Total | 1346 | 702 | 2048 |
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Candeloro, L.; Ippoliti, C.; Iapaolo, F.; Monaco, F.; Morelli, D.; Cuccu, R.; Fronte, P.; Calderara, S.; Vincenzi, S.; Porrello, A.; et al. Predicting WNV Circulation in Italy Using Earth Observation Data and Extreme Gradient Boosting Model. Remote Sens. 2020, 12, 3064. https://doi.org/10.3390/rs12183064
Candeloro L, Ippoliti C, Iapaolo F, Monaco F, Morelli D, Cuccu R, Fronte P, Calderara S, Vincenzi S, Porrello A, et al. Predicting WNV Circulation in Italy Using Earth Observation Data and Extreme Gradient Boosting Model. Remote Sensing. 2020; 12(18):3064. https://doi.org/10.3390/rs12183064
Chicago/Turabian StyleCandeloro, Luca, Carla Ippoliti, Federica Iapaolo, Federica Monaco, Daniela Morelli, Roberto Cuccu, Pietro Fronte, Simone Calderara, Stefano Vincenzi, Angelo Porrello, and et al. 2020. "Predicting WNV Circulation in Italy Using Earth Observation Data and Extreme Gradient Boosting Model" Remote Sensing 12, no. 18: 3064. https://doi.org/10.3390/rs12183064