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    Improving livestock distribution maps in developing countries is crucial for disease risk assessment. This study enhances map accuracy using spatial modeling, reducing errors and optimizing resource use without needing more survey data.

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    Area of Science:

    • Veterinary Epidemiology
    • Spatial Statistics
    • Geographic Information Systems (GIS)

    Background:

    • Accurate livestock distribution data is vital for assessing zoonotic disease transmission risks in developing nations.
    • Current methods rely on costly surveys with limited sample sizes, leading to low-resolution and inaccurate distribution estimates.
    • High-resolution spatial data is particularly challenging to achieve with traditional survey methods.

    Purpose of the Study:

    • To improve the accuracy of livestock distribution maps without increasing survey sample sizes.
    • To explore the use of spatial modeling, specifically regression tree forest models, for enhancing livestock enumeration.
    • To compare the accuracy of novel modeling approaches against direct estimation methods.

    Main Methods:

    • Developed spatial models using subsets of the Uganda 2008 Livestock Census data and various spatial covariates.
    • Utilized regression tree forest models for spatial prediction of livestock distribution.
    • Compared the accuracy of spatial models and an ensemble approach (spatial model + direct estimate) against direct estimates from the full dataset.

    Main Results:

    • The novel spatial modeling approach significantly increased the accuracy of livestock distribution estimates.
    • A median relative error decrease was observed, ranging from 0.166 to 0.037 for sample sizes of 80 to 1,600 animals, respectively.
    • The ensemble approach demonstrated improved accuracy compared to direct estimates alone.

    Conclusions:

    • Spatial modeling offers an effective strategy to enhance livestock distribution map accuracy without additional sampling.
    • Achieving high accuracy levels comparable to direct estimates is possible with reduced sample sizes using these advanced methods.
    • This approach represents a more efficient allocation of financial resources for livestock surveys and disease risk assessment.