Abstract
Vector-borne diseases are a major global health concern, with Rift Valley fever (RVF) serving as a key example due to its impact on both human and animal health. Predicting and controlling such diseases requires understanding how environmental factors shape mosquito ecology. Due to mosquito abundance, distribution, and behavior being influenced by ecological conditions, identifying the drivers of these dynamics is essential for anticipating transmission risk. This study aims to assess the sensitivity of ecological factors governing the ecology of mosquitoes, using a deterministic, compartmental model of RVF transmission in Kenya. We conducted a local and global sensitivity analysis on ten model parameters: four species-specific parameters for each of the two mosquito species of interest (Culex spp. and Aedes spp.) and two parameters shared across species. The focal species-specific parameters were the area scanned before oviposition, the proportion of area suitable for egg laying, the number of eggs laid, and the maximum density of eggs. The two shared parameters represented livestock population size and the influence of livestock on vector fecundity and gonotrophic cycles. Parameter ranges and distributions were defined using a scoping literature review. Sobol sensitivity analysis was performed under two environmental scenarios: (i) constant temperature and water body area, and (ii) periodically varying temperature and water body area, implemented using a time-dependent Sobol framework. Results revealed species-specific differences in parameter influence. For Culex spp., uncertainty in the area scanned for oviposition was highly influential, while for Aedes spp., the pairing of the proportion of area that eggs are laid on and the maximum density of eggs emerged as dominant. These findings highlights the need for improved empirical data on spatial oviposition patterns across water bodies, as current evidence remains limited. By identifying the ecological parameters that most critically shape mosquito population dynamics and/or influence model outputs, and thereby the transmission potential of RVF, this work supports more targeted vector surveillance and strengthens public health decision-making in RVF-endemic regions.