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. 2025 Apr 3;13(4):812.
doi: 10.3390/microorganisms13040812.

Validating a Bayesian Spatio-Temporal Model to Predict La Crosse Virus Human Incidence in the Appalachian Mountain Region, USA

Maggie McCarter  1 , Stella C W Self  1 , Huixuan Li  1 , Joseph A Ewing  2 , Lídia Gual-Gonzalez  1 , Mufaro Kanyangarara  1 , Melissa S Nolan  1
Affiliations

Validating a Bayesian Spatio-Temporal Model to Predict La Crosse Virus Human Incidence in the Appalachian Mountain Region, USA

Maggie McCarter et al. Microorganisms. .

Abstract

La Crosse virus (LACV) is a rare cause of pediatric encephalitis, yet identifying and mitigating transmission foci is critical to detecting additional cases. Neurologic disease disproportionately occurs among children, and survivors often experience substantial, life-altering chronic disability. Despite its severe clinical impact, public health resources to detect and mitigate transmission are lacking. This study aimed to design a Bayesian modelling approach to effectively identify and predict LACV incidence for geospatially informed public health interventions. A Bayesian negative binomial spatio-temporal regression model best fit the data and demonstrated high accuracy. Nine variables were statistically significant in predicting LACV incidence for the Appalachian Mountain Region. Proportion of children, proportion of developed open space, and proportion of barren land were positively associated with LACV incidence, while vapor pressure deficit index, year, and proportions of developed high intensity land, evergreen forest, hay pasture, and woody wetland were negatively associated with LACV incidence. Model prediction error was low, less than 2%, indicating high accuracy in predicting annual LACV human incidence at the county level. In summary, this study demonstrates the utility of Bayesian negative binomial spatio-temporal regression models for predicting rare but medically important LACV human cases. Future studies could examine more granular models for predicting LACV cases from localized variables such as mosquito control efforts, local reservoir host density and local weather fluctuations.

Keywords: arboviral surveillance; bayesian statistics; forecast modelling; integrated nested laplace approximation technique; prediction modelling.

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Conflict of interest statement

Stella Self received consulting fees and travel funding from the Companion Animal Parasite Council. The other authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Reported, Predicted and Model Error for La Crosse Virus Human Counts, 2021. (a) Actual counts of reported human LACV incident cases in 2021 by county; (b) INLA model predicted LACV incident human case counts for the same year; (c) County-level count errors between actual vs. predicted LACV incidence.
Figure 2
Figure 2
Yearly INLA-Predicted La Crosse virus Human Counts by County for the Years 2015–2020. INLA model predicted LACV incident human case counts for (a) 2015; (b) 2016; (c) 2017; (d) 2018; (e) 2019; and (f) 2020.
Figure 3
Figure 3
Alternate Poisson Model-Predicted Case Counts, 2021.
See this image and copyright information in PMC

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