enz-j/Rvfv_dynamics_gambia

Our study addresses a key gap in Rift Valley fever epidemiology in the West African Sahel, where transmission remains poorly understood. We develop deterministic and stochastic models to assess RVF virus dynamics in transhumant cattle, offering insights into disease persistence, livestock management, zoonotic risk, and control strategies. # Impact This repository contains the data and source code for the study: Essa Jarra, Divine Ekwem, Daniel T. Haydon "Rift Valley fever virus dynamics in a transhumant cattle system in The Gambia." eLife2026;14:RP107346 DOI: doi.org Our study addresses a key gap in Rift Valley fever epidemiology in the West African Sahel, where transmission remains poorly understood. We develop deterministic and stochastic models to assess RVF virus dynamics in transhumant cattle, offering insights into disease persistence, livestock management, zoonotic risk, and control strategies. # Overview This repository provides code for modelling RVF dynamics in transhumant cattle populations in The Gambia. The models integrate sero-epidemiological data and ecological factors influencing disease transmission. # Data The study is based on sero-epidemiological data from Jarra et al. 2025 (doi:10.1101/2025.03.14.640388). The dataset used to construct the network is provided as _Ca_move_1__; however, household-level identifiers, including geographic coordinates, have been withheld to preserve confidentiality. Estimated weekly force of infection (FOI) values for each eco-region, used to predict age-seroprevalence curves, are provided in _lambda_vec_. # Analysis • Deterministic model: R scripts for deterministic simulations of RVF transmission are available in the deterministic directory. • Stochastic model: The stochastic directory contains R code for stochastic simulations incorporating demographic variability. # Usage To run the simulations, ensure you have R and the necessary dependencies installed. Scripts can be executed directly or modified for specific parameter settings.