-->PONE-D-26-09292-->-->Mapping the prevalence of household-scale livestock ownership by animal taxon in low- and middle-income countries: an INLA prediction model-->-->PLOS One

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Reviewer #1: Partly

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Reviewer #1: No

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Reviewer #1: Yes

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Reviewer #1: Yes

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-->5. Review Comments to the Author

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Reviewer #1: This manuscript uses publicly available household survey microdata from DHS, MICS, and CFPS to model and map the prevalence of household-scale livestock ownership for poultry, swine, and ruminants across low- and middle-income countries using INLA-based spatial logistic models. The topic is important, the dataset is large, and the resulting maps could be useful for zoonotic disease research. The paper is generally well written and potentially suitable for publication, but several aspects of the modelling strategy, validation, assumptions, and interpretation need clarification or strengthening before the conclusions can be fully supported.

Major comments

The manuscript notes that the semi-variogram for swine suggests a non-stationary spatial process that may violate the stationary Matérn assumption, yet the same stationary INLA specification is retained for all three taxa. This is a substantive technical concern because swine ownership is also the most culturally constrained and spatially heterogeneous outcome. I suggest the authors either fit a more flexible model for swine, such as region-specific spatial structure or another approach that relaxes stationarity, or provide stronger evidence that the current specification is adequate and then moderate the interpretation of the swine map in the abstract and discussion.

he manuscript reports ROC-AUC, recall, precision, accuracy, and F1-score, and describes performance as impressive, but it is unclear whether these metrics were obtained in-sample, through random cross-validation, or using spatially separated folds. For spatial prediction models, random validation can give over-optimistic results because nearby observations are not independent. The paper should state exactly how performance was assessed and ideally report spatial cross-validation or another out-of-sample strategy that reflects the intended use of the maps.

According to the Methods, one household with non-missing outcome per cluster was retained for analysis to reduce database size. This choice discards substantial within-cluster information and may affect both prevalence estimation and covariate importance. At minimum, the authors should explain the implications more clearly.

In majority-Muslim countries where swine ownership was not asked in any survey, households were coded as ‘no’ rather than missing. Although this may often be reasonable, it is still an assumption that may not hold and could influence both model fit and predicted prevalence in sparse-data regions. I suggest the authors run the analysis with these data as missing to see if the results are significantly altered. Reporting these findings would provide a fuller justification and discussion of the possible bias and would strengthen the manuscript.

The Results acknowledge major geographic gaps, including many South American countries and several countries without public surveys or without livestock questions. In those settings, the model is extrapolating largely from covariates and broad regional structure, which likely increases uncertainty. I suggest distinguishing more explicitly between data-rich and data-poor regions in the Discussion, and include clearer communication of predictive uncertainty rather than focusing mainly on average predicted prevalence.

The manuscript occasionally overstates what the maps represent. The study estimates binary household ownership of broad livestock taxa, not animal density, husbandry intensity, within-household contact, or disease transmission risk itself. For that reason, statements suggesting that the maps identify areas of high exposure to animal disease reservoirs should be softened. These outputs are better described as geographically resolved proxy exposure layers that may support future zoonotic risk analyses when combined with other epidemiologic or environmental data.

Minor comments

The manuscript would benefit from a brief discussion of covariate collinearity. Several predictors appear likely to be correlated, including built-up area, nighttime lights, and other development-related measures. Because SHAP values are presented as indicators of variable contribution, the paper should note that interpretation of individual covariates may be unstable when predictors are strongly correlated.

Missing raster pixels were filled using k-nearest neighbours, but no details are given about the extent of missingness or the likely effect on prediction uncertainty. A short explanation in the Methods and a brief note in the limitations would help.

Recommendation

This is a useful and potentially publishable paper, but the current version would benefit from major revision before it is suitable for publication. The most important issues concern the spatial assumptions of the INLA models, the transparency of validation, the implications of key outcome-coding and subsampling decisions, and the need for more cautious interpretation in data-sparse regions.

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Reviewer #1: No

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PONE-D-26-09292R1

Dear Dr. Colston,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Joshua Kamani, PhD

Academic Editor

PLOS One

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