The core of the modeling effort is a multi-component simulation framework that links several distinct biological and economic modules. On the vector side, the model tracks Aedes and Culex mosquitoes using Susceptible Exposed Infectious dynamics, capturing the biology of the two genera that drive transmission in nature. Aedes mosquitoes, whose eggs can survive dry periods and hatch when flooding rains arrive, are classically responsible for initiating outbreaks, while Culex species amplify transmission among animals once an epizootic is underway. On the host side, livestock populations follow Susceptible Exposed Infectious Recovered dynamics, layered on top of a herd demography module that accounts for births, deaths and the gradual turnover of animals that changes the susceptible fraction of the population between outbreaks.

What distinguishes this framework from many earlier RVF models is the explicit coupling of these infection dynamics to end-market processes and demand-side behavior. Livestock producers in pastoral systems such as Ijara County do not operate in a vacuum: when disease strikes, buyers retreat, prices fall and household incomes contract, and these economic signals feed back into decisions about animal holdings and sales. By incorporating demand shocks and behavioral feedback directly into the simulation, the authors allow the economic consequences of an outbreak to unfold endogenously rather than being imposed as fixed external costs. This design reflects the study’s central premise that livestock diseases present systemic threats to animal health, rural livelihoods and national economies, and that nonlinear feedbacks, time lags and cross-sectoral interactions must be captured to evaluate policy honestly.

The simulation runs over a ten-year horizon with a daily time step, a resolution fine enough to represent the rapid course of mosquito-borne transmission and the timing of vaccination campaigns with realistic precision. Parameterization draws on both primary and secondary data, including El Niño associated rainfall patterns that historically precede RVF epizootics in East Africa and the results of producer surveys conducted in the study area. The heavy rains associated with El Niño events create the flooded habitats in which Aedes eggs hatch and Culex populations explode, so anchoring the model’s environmental forcing to these observed patterns helps ensure that simulated outbreaks resemble real ones. The authors report that the model reproduces key outbreak features consistent with historical RVF events, an important credibility check before using it to compare policies that have never been implemented at scale.

With this validated platform in place, the researchers compared alternative vaccination strategies against a business-as-usual baseline and a no-intervention scenario. The business-as-usual strategy corresponds to the reactive posture that has often prevailed in practice: vaccination is delayed until an outbreak is already underway, constrained by the logistical realities of mobilizing vaccines, cold chains and veterinary teams in remote pastoral areas. The preventive alternatives include annual vaccination of susceptible animals, biannual campaigns and triennial schedules, each representing different assumptions about budget, coverage and the frequency with which herds are refreshed with newly born, susceptible stock.

The results are striking in their asymmetry. Under business-as-usual delayed reactionary vaccination, the model shows only marginal improvements in herd recovery and producer income relative to doing nothing at all. By the time a reactive campaign takes effect, the explosive early phase of an RVF epizootic has typically already run its course through the most susceptible animals, so late vaccination buys little additional protection and recovers little of the lost income. In contrast, annual preventive vaccination with sufficient coverage of susceptible animals prevents simulated outbreaks entirely within the model horizon. Biannual and triennial strategies occupy an intermediate position: they reduce outbreak severity but do not fully eliminate risk, because gaps in coverage allow susceptible animals to accumulate and transmission to rekindle when vector populations surge.

Beyond the comparison of fixed schedules, the model illuminates the decisive importance of timing itself. Shortening the delay between the onset of an outbreak and the initiation of vaccination substantially reduces livestock losses and improves the trajectory of income recovery for producers. This finding quantifies a principle that outbreak responders have long understood intuitively: in a fast-moving vector-borne epidemic, every week of delay translates into animals infected that timely vaccination could have protected. The system dynamics formulation makes the cost of delay explicit, translating it into herd-level mortality and household-level economic outcomes rather than leaving it as an abstract warning.

For policymakers in Kenya and other RVF-endemic countries, the practical implications are considerable. Preventive vaccination is expensive and competes with other demands on limited veterinary budgets, and in years without outbreaks it can appear to be money spent for nothing. The model’s ex-ante comparisons offer a way to weigh that recurring cost against the episodic, catastrophic losses of uncontrolled epizootics, and to demonstrate that proactive programs outperform reactive ones in both disease control and economic resilience. The authors position the model as a decision-support tool for livestock health policy, one that can be rerun under different assumptions about rainfall, coverage, vaccine efficacy and market behavior to stress-test strategies before committing public resources to them.

Methodologically, the study illustrates why system dynamics approaches are gaining traction in animal health economics. Because the framework integrates vector ecology, host infection dynamics, herd demography and market processes in one coupled structure, it can reveal behaviors that emerge from interactions among components rather than from any single module. Time lags between rainfall triggers, mosquito population booms, livestock exposure and policy response are represented explicitly, as are the nonlinear thresholds that separate a contained incursion from a runaway epizootic. Conventional static impact assessments, which typically multiply observed losses by fixed prices, cannot capture these dynamics, and the authors argue that this limitation has systematically distorted the apparent value of preventive investment.

The research also speaks to the broader challenge of preparing for climate-sensitive disease threats. RVF outbreaks in East Africa are tightly associated with flooding events whose frequency and intensity may shift with a changing climate, and models of this kind provide a template for evaluating how preparedness investments perform across a range of environmental futures. For the pastoral communities of Ijara County and similar regions, where livestock constitute both income and a form of savings, the difference between a prevented outbreak and a delayed response is measured in household welfare as much as in veterinary outcomes. By demonstrating that annual preventive vaccination with adequate coverage can hold the virus at bay within the simulated decade, while reactive campaigns barely improve on inaction, the study offers a quantified case for shifting RVF policy from reaction to anticipation, and a modeling architecture that other endemic countries can adapt to their own epidemiological and economic circumstances.

Subject of Research: System dynamics modeling of Rift Valley fever vaccination strategies in Kenya

Article Title: Integrating epidemiological and economic dynamics to assess Rift Valley fever vaccination strategies: A system dynamics model from Kenya

Article References: Bahta, S., Wanyoike, F., Bett, B., & Rich, K. M. (2026). Integrating epidemiological and economic dynamics to assess Rift Valley fever vaccination strategies: A system dynamics model from Kenya. PLOS Complex Systems, 3(6), e0000101. https://doi.org/10.1371/journal.pcsy.0000101

Image Credits: AI Generated

DOI: 10.1371/journal.pcsy.0000101

Keywords: Rift Valley fever, system dynamics, vaccination strategy, Kenya, livestock health, epidemiological modeling, economic impact, Aedes mosquitoes, Culex mosquitoes, SEIR model, preventive vaccination, pastoral systems