Predicting the Presence of Leptospires in Rodents from Environmental Indicators Opens Up Opportunities for Environmental Monitoring of Human Leptospirosis
Abstract
:1. Introduction
2. Materials and Methods
2.1. Sampling Permit and Ethics Approval
2.2. Animal Sampling and Leptospira Detection
2.3. Landscape Analysis
2.4. Meteorological Data
2.5. Cartography
2.6. Statistical Analyses
3. Results
3.1. Trapping Success
3.2. Rat Characteristics
3.3. Renal Carriage of Leptospira spp.
3.4. Ecological Pattern of Rat Distribution
3.5. Environmental Prediction of Leptospires Carriage
4. Discussion
4.1. Rattus Diversity and Abundance
4.2. Leptospira-Laden Rattus spp.
4.3. Rattus Characteristics in Relation to Infection Rates
4.4. Environmental Factors in Relation to Rat Distribution and Infection Rates
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Site | Trap-Nights | Environment | Trapping Success | Leptospira Detection (RT-qPCR) Positive/Total Sampled (% of Positive) | |||
---|---|---|---|---|---|---|---|
Rattus rattus | Rattus norvegicus | Rattus rattus | Rattus norvegicus | All | |||
Casse Dent | 149 | N | 46.9 | 0.0 | 2/65 (3.1) | 0/2 (0.0) | 2/67 (3.0) |
Chemin Dame le Roi | 179 | R | 47.9 | 0.0 | 2/75 (2.7) | - | 2/75 (2.7) |
Fairview La Misère | 250 | P | 31.2 | 10.3 | 8/69 (11.6) | 8/15 (53.3) | 16/84 (19.0) |
Grand Bois Mont Cephale | 140 | N | 47.2 | 0.0 | 3/58 (5.2) | - | 3/58 (5.2) |
La Gogue | 133 | R | 54.0 | 1.7 | 1/66 (1.5) | 0/1 (0.0) | 1/67 (1.5) |
La Reserve | 172 | N | 36.6 | 0.0 | 1/60 (1.7) | - | 1/60 (1.7) |
Police Bay | 168 | N | 60.0 | 0.0 | 0/98 (0.0) | - | 0/98 (0.0) |
Port Launay | 148 | R | 57.9 | 6.3 | 2/78 (2.6) | 3/5 (60.0) | 5/83 (6.0) |
Providence Industrial Estate | 33 | U | 39.3 | 10.5 | 0/6 (0.0) | 2/2 100.0 | 2/8 (25.0) |
Reclaimed Land (Zone 21) | 142 | P | 37.3 | 5.6 | 7/50 (14.0) | 5/5 (100.0) | 12/55 (21.8) |
Victoria | 342 | U | 18.4 | 7.4 | 4/60 (6.7) | 9/20 (45.0) | 13/80 (16.3) |
Total | 1859 | - | 43.3 | 3.8 | 30/685 (4.4) | 27/50 (54.0) | 57/735 (7.8) |
Land Use/Land Cover Classes | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | Total | Producer Accuracy |
---|---|---|---|---|---|---|---|---|---|---|---|
1—Dense Urban | 39 | 2 | 3 | 44 | 0.89 | ||||||
2—Peri-urban | 56 | 3 | 2 | 1 | 4 | 66 | 0.85 | ||||
3—Wooded areas | 79 | 2 | 81 | 0.98 | |||||||
4—Shrub | 4 | 28 | 1 | 33 | 0.85 | ||||||
5—Herbaceous vegetation | 1 | 2 | 1 | 25 | 5 | 34 | 0.74 | ||||
6—Mangroves | 2 | 2 | 26 | 32 | 0.81 | ||||||
7—Bare lands | 2 | 1 | 33 | 3 | 39 | 0.85 | |||||
8—Wet areas, lakes | 1 | 29 | 30 | 0.97 | |||||||
9—Agricultural areas | 1 | 1 | 31 | 33 | 0.94 | ||||||
Total | 39 | 58 | 90 | 35 | 29 | 30 | 45 | 33 | 31 | 390 | |
User accuracy | 1 | 0.97 | 0.88 | 0.80 | 0.86 | 0.87 | 0.73 | 0.88 | 1 |
Model | Variables | Estimate | Std. Error | z Value | p-Value |
---|---|---|---|---|---|
Model 1 (Excluding variable Species) | (Intercept) | −2.943 | 0.238 | −12.382 | <2 × 10−16 |
AIC = 272.74 | Edge density 100 | 0.591 | 0.160 | 3.683 | <3 × 10−4 |
AUCROC = 77.68% | 20-year average rainfall | 0.643 | 0.283 | 2.272 | 0.023 |
AUCPR = 16.08% | 3-month cumulated rainfall | 30.260 | 0.159 | 1.629 | 0.103 |
Accuracy = 92.35% F1 = NA | Elevation | 0.219 | 0.144 | 1.526 | 0.127 |
Model 2 (Excluding variable Species) | (Intercept) | −2.925 | 0.233 | −12.548 | <2 × 10−16 |
AIC = 272.14 | Edge density 100 | 0.499 | 0.177 | 2.81 | 0.005 |
AUCROC = 79.42% | Distances to dense urban areas | −0.738 | 0.317 | −2.332 | 0.020 |
AUCPR = 15.55% | 3-month cumulated rainfall | 0.247 | 0.159 | 1.555 | 0.120 |
Accuracy = 92.35% F1 = NA | |||||
Model 3 (Including variable Species) | (Intercept) | −3.034 | 0.235 | −12.907 | <2 × 10−16 |
AIC = 237.9 | Species RR | −0.648 | 0.107 | −6.044 | 1.5 × 10−9 |
AUCROC = 88.12% | Edge density 100 | 0.380 | 0.195 | 1.947 | 0.052 |
AUCPR = 47.21% Accuracy = 93.99% | Distances to dense urban areas | −0.395 | 0.305 | −1.294 | 0.195 |
F1 = 42.11% | 3-month cumulated rainfall | 0.21 | 0.17 | 1.251 | 0.211 |
Model 4 (Including variable Species) | (Intercept) | −3.1093 | 0.254 | −12.246 | <2 × 10−16 |
AIC = 236.65 AUCROC = 84.87% | Species RR | −0.657 | 0.105 | −6.242 | 4.3 × 10−10 |
AUCPR = 43.07% | Edge density 100 | 0.3835 | 0.180 | 2.132 | 0.033 |
Accuracy = 92.35% | 20-year average rainfall | 0.5217 | 0.304 | 1.718 | 0.086 |
F1 = 30% | 3-month cumulated rainfall | 0.2302 | 0.176 | 1.309 | 0.190 |
Elevation | 0.2125 | 0.158 | 1.348 | 0.178 |
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Biscornet, L.; Révillion, C.; Jégo, S.; Lagadec, E.; Gomard, Y.; Le Minter, G.; Rocamora, G.; Guernier-Cambert, V.; Mélade, J.; Dellagi, K.; et al. Predicting the Presence of Leptospires in Rodents from Environmental Indicators Opens Up Opportunities for Environmental Monitoring of Human Leptospirosis. Remote Sens. 2021, 13, 325. https://doi.org/10.3390/rs13020325
Biscornet L, Révillion C, Jégo S, Lagadec E, Gomard Y, Le Minter G, Rocamora G, Guernier-Cambert V, Mélade J, Dellagi K, et al. Predicting the Presence of Leptospires in Rodents from Environmental Indicators Opens Up Opportunities for Environmental Monitoring of Human Leptospirosis. Remote Sensing. 2021; 13(2):325. https://doi.org/10.3390/rs13020325
Chicago/Turabian StyleBiscornet, Leon, Christophe Révillion, Sylvaine Jégo, Erwan Lagadec, Yann Gomard, Gildas Le Minter, Gérard Rocamora, Vanina Guernier-Cambert, Julien Mélade, Koussay Dellagi, and et al. 2021. "Predicting the Presence of Leptospires in Rodents from Environmental Indicators Opens Up Opportunities for Environmental Monitoring of Human Leptospirosis" Remote Sensing 13, no. 2: 325. https://doi.org/10.3390/rs13020325
APA StyleBiscornet, L., Révillion, C., Jégo, S., Lagadec, E., Gomard, Y., Le Minter, G., Rocamora, G., Guernier-Cambert, V., Mélade, J., Dellagi, K., Tortosa, P., & Herbreteau, V. (2021). Predicting the Presence of Leptospires in Rodents from Environmental Indicators Opens Up Opportunities for Environmental Monitoring of Human Leptospirosis. Remote Sensing, 13(2), 325. https://doi.org/10.3390/rs13020325