# Nipah spillover risk and large-scale spatial modelling studies

Julien Cappelle, Facundo Munoz

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## To cite this version:

Julien Cappelle, Facundo Munoz. Nipah spillover risk and large-scale spatial modelling studies. Lancet Planetary Health, 2025, 9 (5), pp.e353-e353. hal-05201789

HAL Id: hal-05201789

https://hal.inrae.fr/hal-05201789v1

Submitted on 6 Aug 2025

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Distributed under a Creative Commons Attribution 4.0 International License
Nipah spillover risk and
large-scale spatial
modelling studies

In their Article, Yan-Qun Sun and colleagues¹ assessed the spillover and endemic risk of Nipah virus in south and southeast Asia. Although this effort is important given the zoonotic potential of Nipah virus, the methods used to map risk areas and the conclusions drawn raise questions.

The authors used 50 variables in a boosted regression tree model, including highly correlated climatic variables, resulting in exceptionally high area under the curve (AUC) values² of 99.9% and 97.7%. Although boosted regression tree models can achieve high predictive performance by relying on non-linear and interactive effects between climatic variables serving as proxies for causal mechanisms, the overfitting of the model requires review. The predictive maps highlight only known areas of Nipah virus circulation—ie, the data used to train the model. These results could stem from methodological choices, such as the total geographical extent used for prediction, which was much larger than the areas with observed cases. Using a large geographical extent can artificially inflate the predictive performance indicator of a model by allowing the model to seemingly correctly predict pseudo-absences at will and then weight them the same as the location of cases.³ However, detailed peer examination is impossible as the data and code are not publicly available, and key model quality indicators like the training set AUC scores and AUC standard errors were not disclosed.

A different, but related, problem is the interpretation of results. Because the most influential factors in Sun and colleagues' models are three mean temperature variables, the authors claim that "bioclimatic variables have a substantial effect on Nipah virus transmission by influencing bat

behaviour and migration patterns". Conclusions based on this large-scale correlative model should be drawn cautiously as complex interactions between variables (both measured or hidden) could explain these inferences, as well as other design choices, such as the prediction extent or the pseudo-absence allocation methods. The authors support this result by citing a study on the timing of emergence of an insectivorous Brazilian bat species, which is irrelevant to the ecology of *Pteropus* bats. Although they included proxies for key Nipah virus risk factors, such as date palm niches and pig farming densities, they contributed minimally to their models. These proxies fail to capture the real drivers of spillover, such as palm sap collection methods and pig husbandry practices.⁴,⁵ As a result, the model's projections are unreliable, casting doubt on the claim that "suitable niches for Nipah virus are more extensive than previously reported". Furthermore, this claim does not have support, as the WHO fact sheet on Nipah virus⁶ suggests a broader potential range for Henipaviruses, which overlaps with the *Pteropus* bat distribution area.

In conclusion, focusing surveillance efforts based on a model dominated by mean temperature variables and only one *Pteropus* species distribution is questionable. Instead, research should prioritise regional and local studies across the entire range of *Pteropus* bats, integrating disease ecology, virology, and anthropology to better understand the potential routes of transmission.⁷ More generally, large-scale modelling aimed at identifying spillover risk factors should be interpreted carefully, as local human practices—often crucial to zoonotic emergence—are rarely captured in large-scale datasets. Finally, data and code should be made available for public scrutiny to improve the quality and reliability of the published research.

We declare no competing interests.

Copyright © 2025 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC 4.0 license.

*Julien Cappelle, Facundo Muñoz

julien.cappelle@cirad.fr

CIRAD, UMR ASTRE, F-34398 Montpellier, France (JC, FM); ASTRE, CIRAD, INRAE, University of Montpellier, Montpellier, France (JC, FM)

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