Modeling sparse Rift Valley fever incidence data: a Bayesian perspective on zero-inflated self-exciting and autoregressive models
These findings underscore the importance of zero-inflated negative binomial models and climate-based covariates in enhancing early warning systems for RVF-endemic regions.
Authors
- Penelope Vounatsou (ORCID: https://orcid.org/0000-0002-4904-5352)
- Bryan O. Nyawanda (ORCID: https://orcid.org/0000-0003-0069-1473)
- Alexandros Angelakis
Institutions
- Swiss Tropical and Public Health Institute (CH)
- University of Basel (CH)
Publication Details
- Journal
- BMC Infectious Diseases
- Published
- 2025-09-29
- DOI
- https://doi.org/10.1186/s12879-025-11506-0
- Primary Topic
- Viral Infections and Vectors
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Universität Basel
- European Commission