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Background <p>Rift Valley fever (RVF) is a mosquito-borne zoonotic disease for which predictive modeling is often hindered by sparse data, particularly the high frequency of zero counts in both human and livestock surveillance systems. While zero-inflated models are commonly used for sparse data, several temporal count modelling frameworks exist, including less common self-exciting models that assume an initial case increases the likelihood of subsequent cases.</p> Methods <p>This study compares three zero-inflated Bayesian models: the negative binomial (ZINB) with autoregressive temporal random effects, the self-exciting negative binomial (SE-NB) and the generalized autoregressive moving average negative binomial (GARMA-NB). The models were evaluated across simulated datasets with varying levels of sparsity.</p> Results <p>We found that zero-inflation substantially improves predictive performance within specific sparsity thresholds: 29–94.5% (ZINB), 25–93% (SE-NB), and 30–95% (GARMA-NB). Applied to monthly RVF incidence data from northern Kenya (2018–2024), the ZINB model with a three-month rainfall lag provided the most accurate forecasts.</p> Conclusion <p>These findings underscore the importance of zero-inflated negative binomial models and climate-based covariates in enhancing early warning systems for RVF-endemic regions.</p>

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Modeling sparse Rift Valley fever incidence data: a Bayesian perspective on zero-inflated self-exciting and autoregressive models

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月29日

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  • Alexandros Angelakis,
  • Bryan O. Nyawanda,
  • Penelope Vounatsou

摘要

Background

Rift Valley fever (RVF) is a mosquito-borne zoonotic disease for which predictive modeling is often hindered by sparse data, particularly the high frequency of zero counts in both human and livestock surveillance systems. While zero-inflated models are commonly used for sparse data, several temporal count modelling frameworks exist, including less common self-exciting models that assume an initial case increases the likelihood of subsequent cases.

Methods

This study compares three zero-inflated Bayesian models: the negative binomial (ZINB) with autoregressive temporal random effects, the self-exciting negative binomial (SE-NB) and the generalized autoregressive moving average negative binomial (GARMA-NB). The models were evaluated across simulated datasets with varying levels of sparsity.

Results

We found that zero-inflation substantially improves predictive performance within specific sparsity thresholds: 29–94.5% (ZINB), 25–93% (SE-NB), and 30–95% (GARMA-NB). Applied to monthly RVF incidence data from northern Kenya (2018–2024), the ZINB model with a three-month rainfall lag provided the most accurate forecasts.

Conclusion

These findings underscore the importance of zero-inflated negative binomial models and climate-based covariates in enhancing early warning systems for RVF-endemic regions.