Validating a Bayesian Spatio-Temporal Model to Predict La Crosse Virus Human Incidence in the Appalachian Mountain Region, USA
Abstract
:1. Introduction
2. Materials and Methods
2.1. La Crosse Virus Reported Human Incidence Data for the Eastern USA and Appalachian Mountain Region
2.2. Covariates
2.3. The Model
2.4. Model Selection
2.5. Modeling Prediction and Inter-Year Accuracy
3. Results
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
ACF | Autocorrelation factor |
ARC | Appalachian Regional Commission |
CDC | United States Centers for Disease Control and Prevention |
INLA | Integrated nested Laplace approximation |
LACV | La Crosse Virus |
MCMC | Markov chain Monte Carlo |
NLCD | National Land Cover Database |
USA | United States of America |
VIF | Variance inflation factor |
VPD | Vapor pressure deficit |
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Variable | Estimate | Standard Deviation | Lower Bound of 95% HPD Interval | Upper Bound of 95% HPD Interval |
---|---|---|---|---|
Intercept | −3.7310 | 0.2201 | −4.1707 | −3.3098 |
Proportion Age 19 and under | 0.4006 | 0.0768 | 0.2504 | 0.5515 |
Developed Open Space | 0.8503 | 0.0818 | 0.6909 | 1.0116 |
Developed High Intensity | −0.3587 | 0.0819 | −0.5199 | −0.1986 |
Barren Land | 0.1763 | 0.0577 | 0.0638 | 0.2901 |
Evergreen Forest | −0.4000 | 0.1488 | −0.6938 | −0.1105 |
Hay Pasture | −0.2931 | 0.0721 | −0.4350 | −0.1525 |
Woody Wetland | −3.3714 | 0.5984 | −4.5671 | −2.2267 |
Vapor Pressure Deficit index | −0.2669 | 0.0937 | −0.4511 | −0.0838 |
Year | −0.0391 | 0.0794 | −0.1948 | 0.1162 |
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McCarter, M.; Self, S.C.W.; Li, H.; Ewing, J.A.; Gual-Gonzalez, L.; Kanyangarara, M.; Nolan, M.S. Validating a Bayesian Spatio-Temporal Model to Predict La Crosse Virus Human Incidence in the Appalachian Mountain Region, USA. Microorganisms 2025, 13, 812. https://doi.org/10.3390/microorganisms13040812
McCarter M, Self SCW, Li H, Ewing JA, Gual-Gonzalez L, Kanyangarara M, Nolan MS. Validating a Bayesian Spatio-Temporal Model to Predict La Crosse Virus Human Incidence in the Appalachian Mountain Region, USA. Microorganisms. 2025; 13(4):812. https://doi.org/10.3390/microorganisms13040812
Chicago/Turabian StyleMcCarter, Maggie, Stella C. W. Self, Huixuan Li, Joseph A. Ewing, Lídia Gual-Gonzalez, Mufaro Kanyangarara, and Melissa S. Nolan. 2025. "Validating a Bayesian Spatio-Temporal Model to Predict La Crosse Virus Human Incidence in the Appalachian Mountain Region, USA" Microorganisms 13, no. 4: 812. https://doi.org/10.3390/microorganisms13040812
APA StyleMcCarter, M., Self, S. C. W., Li, H., Ewing, J. A., Gual-Gonzalez, L., Kanyangarara, M., & Nolan, M. S. (2025). Validating a Bayesian Spatio-Temporal Model to Predict La Crosse Virus Human Incidence in the Appalachian Mountain Region, USA. Microorganisms, 13(4), 812. https://doi.org/10.3390/microorganisms13040812