Anna-Sofie Stensgaard

Abstract Rift Valley Fever (RVF) is a mosquito-borne zoonotic disease often linked to climatic factors. However, identifying the onset and duration of outbreaks can be hindered by high numbers of zero counts in sparse surveillance data. We analyzed monthly RVF data from Kenya (2015–2022), compared the performance of widely used outbreak-detection methods, and developed Negative Binomial models augmented with a hidden Markov process and zero-inflation to address data sparsity.

1 Introduction Malaria is a mosquito-borne parasitic disease with around 250 million cases annually. It causes approximately half a million deaths, more than 90% of which occur in sub-Saharan Africa (Weiss et al. 2019; World Health Organization 2024). Malaria transmission is heavily influenced by environmental factors.