Dr Jessica R. Furber

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. March 26, 2026 1/30 Author summary Rift Valley fever is a disease spread by mosquitoes that affects both people and animals. Understanding what drives mosquito populations can help us predict and control the spread of Rift Valley fever. In this study, we used a published mathematical model to conduct a sensitivity analysis to explore how different parameters within the model affect mosquito numbers. In total, we investigated ten parameters, where the analysis emphasizes the role of water bodies used by mosquitoes to lay their eggs. We found that this factor plays a major role in how many mosquitoes are present. However, there is still a lot we do not know about exactly where mosquitoes choose to lay their eggs. More research in this area could improve how we predict and manage Rift Valley fever and other mosquito-borne diseases outbreaks. Our findings highlight which environmental details matter most, and they can help guide better strategies for mosquito control and disease prevention.

Understanding animal movements at different spatial scales presents a significant challenge as their patterns can vary widely from daily foraging behaviours to broader migration or territorial movements. This challenge is of general interest because it impacts the ability to manage wildlife populations effectively. In this study, we conduct diffusion analysis based on European badger (Meles meles) movement data obtained from three different regions in the UK (Gloucestershire, Cornwall, and Northern Ireland) and fit a generalised linear mixed-effects model to examine the relationship between variables. We also feature a novel application of extended dynamic mode decomposition (EDMD) to uncover patterns relating to badger social organisation. By applying our approach to these different populations, we were able to assess its performance across a range of badger densities. A key result was that in some areas, EDMD clusters matched observed group home ranges, whilst in others, discrepancies likely arose because of population management interventions, such as badger culling. The methods presented offer a promising approach for studying territoriality and the impacts of management strategies on animal movement behaviour.

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. Effective control and prediction of such diseases require an understanding of how environmental factors influence mosquito ecology. As mosquito abundance, distribution, and behavior are shaped by ecological conditions, identifying these drivers is essential for anticipating transmission risk and informing public health interventions. This study aims to assess the sensitivity of key factors governing the ecology of mosquitoes, using a deterministic, compartmental model developed for the transmission of RVFV in Kenya. Specifically, we investigate the influence of four model parameters on mosquito abundance, focusing on the proportion of water body area utilized for oviposition. A literature review was conducted to establish parameter ranges and distributions. The Sobol method of global sensitivity analysis was then applied to a simplified model environment, firstly with constant temperature and water body area and secondly with periodic temperature and water body area. The indices calculated were used to rank the parameters based on their influence on mosquito abundance. The analysis showed that there is a need to reduce uncertainty in the area scanned by Culex and identified the proportion of water body area used for oviposition as a highly influential parameter. This finding underscores the need for further research into spatial oviposition trends across different water bodies, as current literature lacks sufficient data to inform realistic parameter estimates.