Rift Valley fever virus dynamics in a transhumant cattle system in The Gambia
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eLife Assessment
This modelling study tests several hypotheses describing how seasonality and migration drive the epidemiology of Rift Valley Fever Virus among transhumant cattle in The Gambia. The work is methodologically solid, and findings offer valuable insights into how the movement of cattle in and out of the Gambia River and Sahel ecoregions could lead to source-sink transmission dynamics among cattle subpopulations, sustaining endemic transmission.
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Abstract
Abstract
Rift Valley fever (RVF) is a zoonotic disease of global concern, driven by environmental conditions, vector activity, and livestock mobility. Although RVF has been reported in The Gambia, its epidemiology remains poorly understood. This study developed a compartmental model to study RVF dynamics in the cattle population of the country. The model incorporated seasonally dynamic transmission parameters reflecting transhumant movement and ecological differences between two distinct ecoclimatic regions: the Sahelian area and the Gambia river. Parameterised using serological data linked to household survey data, the model predicted endemic RVF virus (RVFV) circulation within The Gambia, and captured temporal infection trends that closely match empirical data. Weak decay rates of seropositivity were required to match predicted and observed age-seroprevalence. Results indicated sustained RVFV transmission during the dry season in the Gambia river eco-region, with a high risk of seasonal virus introductions to the Sahelian eco-region at the start of the wet season via the returning transhumant cattle. A hypothetical transhumant movement ban reduced the frequency of outbreaks in the Sahelian eco-region but disproportionately increased their magnitude, with no long-term reduction in the number of infectious cattle. Our study highlighted the role of livestock mobility in RVFV epidemiology in The Gambia and the need for targeted control strategies that do not involve bans on transhumance movements. These control measures might include, for example, targeted cattle vaccination or application of topical insecticide treatments.
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eLife Assessment
This modelling study tests several hypotheses describing how seasonality and migration drive the epidemiology of Rift Valley Fever Virus among transhumant cattle in The Gambia. The work is methodologically solid, and findings offer valuable insights into how the movement of cattle in and out of the Gambia River and Sahel ecoregions could lead to source-sink transmission dynamics among cattle subpopulations, sustaining endemic transmission.
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Joint Public Review:
Summary:
This study uses data from a recent RVFV serosurvey among transhumant cattle in The Gambia to inform the development of an RVFV transmission model. The model incorporates several hypotheses that capture the seasonal nature of both vector-borne RVFV transmission and cattle migration. These natural phenomena are driven by contrasting wet and dry seasons in The Gambia's two main ecoregions and are purported to drive cyclical source-sink transmission dynamics. Although the Sahel is hypothesized to be unsuitable for year-long RVFV transmission, findings suggest that cattle returning from the Gambia River to the Sahel at the beginning of the wet season could drive repeated RVFV introductions and ensuing seasonal outbreaks. The model is also used to evaluate the potential impacts of cattle movement bans on …
Joint Public Review:
Summary:
This study uses data from a recent RVFV serosurvey among transhumant cattle in The Gambia to inform the development of an RVFV transmission model. The model incorporates several hypotheses that capture the seasonal nature of both vector-borne RVFV transmission and cattle migration. These natural phenomena are driven by contrasting wet and dry seasons in The Gambia's two main ecoregions and are purported to drive cyclical source-sink transmission dynamics. Although the Sahel is hypothesized to be unsuitable for year-long RVFV transmission, findings suggest that cattle returning from the Gambia River to the Sahel at the beginning of the wet season could drive repeated RVFV introductions and ensuing seasonal outbreaks. The model is also used to evaluate the potential impacts of cattle movement bans on transmission dynamics, although there is doubt about the certainty of these latter findings in light of various simplifying assumptions.
Strengths:
Like most infectious diseases in animal systems in low- and middle-income countries, the transmission dynamics of RVFV in cattle in The Gambia are poorly understood. This study harnesses important data on RVFV seroepidemiology to develop and parameterize a novel transmission model, providing plausible estimates of several epidemiological parameters and transmission dynamic patterns.
This study is well written and easy to follow.
The authors consider both deterministic and stochastic formulations of their model, demonstrating potential impacts of random events (e.g., extinctions) and providing confidence regarding model robustness.
The authors use well-established Bayesian estimation techniques for model fitting and confront their transmission model with a seroepidemiological model to assess model fit.
Elasticity analyses help to understand the relative importance of competing demographic and epidemiological drivers of transmission in this system.
Weaknesses:
The model predicts relatively stable annual dynamics reminiscent of a seasonal endemic pathogen, but RVF in sub-Saharan Africa is often characterized as causing periodic epizootics with sustained lulls in between outbreaks. Do the authors believe this conventional wisdom regarding RVF epidemiology is wrong, and that their results better support that transmission patterns are seasonal but truly relatively stable year-over-year, at least in the Gambia? The authors should discuss whether these predicted dynamics could be an artefact of the model's structure, and what ramifications this could have for their conclusions.
It is unclear how the network analysis is used to inform the model. The network (Figure S2) suggests a highly fragmented population, which could better support, for example, a herd metapopulation approach. The first results section highlights that transhumant movements cover large distances (perhaps to justify the assumption of homogenous mixing within each ecoregion?), but the median (13.5km) is quite short.
The model does not include an impact of infection on cattle birth rates, but the authors highlight the well-known impacts of RVF epizootics on cattle abortion and neonatal death.
ODEs for M herds in the dry season are missing from the appendix. Even in the absence of transmission among this subpopulation in this season, demographic turnover should influence its SIR population dynamics. Were these not included in the model or simply omitted from the text?
The importance of the LVFV positivity decay rate is highlighted, but the loss of immunity is not considered in the SIR model. The authors do discuss uncertainty regarding model structure, but could better justify their choice. Is there evidence of reduced infection risk among previously infected seronegatives, and why was an SIRS model not considered? How might findings be expected to differ under an SIRS model?
Shouldn't disease-induced host death be included in the serocatalytic model? A high RVF mortality rate has been estimated, and FOI is relatively high, suggesting a non-negligible impact of RVF death on seroprevalence dynamics, and indeed possibly a greater impact than seroreversion.
It is helpful that the authors have described findings from the previously conducted household survey, which is a key foundation for the model, but it needs to be made clearer what work was already conducted as part of the previous study, in particular the Methods sections RVFV seroprevalence & household survey data and Epidemiological setting & cattle population structure. Same for the sections Study Area and Data Collection in the appendix.
The study limitations paragraph is vague. What modelling assumptions have introduced the greatest uncertainty, and what implications could this have for study conclusions?
Two main issues with the simulations of a ban on transhuman movement:
The introduction rightly highlights the importance of pastoral lifestyles for subsistence farmers in the Gambia. It therefore seems likely that transhumant movement bans would have great socioeconomic and ethical challenges in addition to obvious practical challenges. Is such an intervention even a remote possibility?
The model's structure, including homogenous mixing within each ecoregion and step-change seasonality, allows for estimation of generalized transmission rates at a macro scale. However, it greatly simplifies the movement process itself and assumes that transhumant cattle movement is the only mechanism for RVF reintroduction into the Sahel region. The model is therefore likely to misrepresent the potential impacts of movement bans on transmission. As studies, for example, in healthcare settings have shown, where fine-scaled contact data are available, incorporating the specific and complex nature of inter-individual contact can change not only the magnitude but the direction of intervention impacts relative to predictions from a model with homogenous mixing assumptions. Conclusions from this work regarding the impacts of movement bans, therefore, seem poorly supported.
This model seems perhaps better suited to exploring, for example, cattle vaccination, and potential differential efficiency when targeting T herds relative to M or L.
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Author response:
(1) Stable annual dynamics vs. episodic outbreaks
We agree that RVF is classically described as producing periodic epidemics interspersed with long inter-epidemic periods, often linked to extreme rainfall events. Our model predicts more regular seasonal dynamics, which reflects the endemic transmission patterns we have observed in The Gambia through serological surveys. In the revision, we will:
Clarify that while epidemics occur in other parts of sub-Saharan Africa, our results may indicate a different epidemiological narrative in The Gambia, with sustained but low-level circulation (hyperendemicity).
Discuss how model assumptions (e.g. seasonality, homogenous mixing) may bias results toward stable dynamics.
Highlight the implications of this for interpretation and for public health decision-making.
(2) Use of network …
Author response:
(1) Stable annual dynamics vs. episodic outbreaks
We agree that RVF is classically described as producing periodic epidemics interspersed with long inter-epidemic periods, often linked to extreme rainfall events. Our model predicts more regular seasonal dynamics, which reflects the endemic transmission patterns we have observed in The Gambia through serological surveys. In the revision, we will:
Clarify that while epidemics occur in other parts of sub-Saharan Africa, our results may indicate a different epidemiological narrative in The Gambia, with sustained but low-level circulation (hyperendemicity).
Discuss how model assumptions (e.g. seasonality, homogenous mixing) may bias results toward stable dynamics.
Highlight the implications of this for interpretation and for public health decision-making.
(2) Use of network analysis
We acknowledge the reviewer’s concern. The network analysis was conducted descriptively to characterize cattle movement patterns and the structure of herd connections, but it was not formally incorporated into the model. In revisions we will:
Clarify this distinction in the manuscript to avoid overinterpretation.
Emphasize the need for future modelling work using finer-scale movement data, which could support more realistic herd metapopulation dynamics and better capture heterogeneity in transmission.
(3) RVFV reproductive impacts
While RVF outbreaks are known to cause abortions and neonatal deaths, these occur during relatively rare epidemics. In the Gambian context, where we’re not observing such large episodic outbreaks but rather low-level circulation, the annual impact of RVF infection on births is likely modest compared to baseline herd turnover. Moreover, cattle demography is partly managed, with replacement and movement buffering birth rates against short-term losses.
Our model includes birth as a constant demographic process, it’s reasonable to assume stable population since we are not explicitly modelling outbreak-scale reproductive losses. This is consistent with other RVF transmission models that adopt a similar simplifying assumption. However, we will acknowledge this simplification as a limitation in the revised manuscript.
(4) Missing ODEs for M herds in the dry season
We thank the reviewer for identifying this omission. The ODEs for M herds in the dry season were not included in the appendix due to an oversight, though demographic turnover was incorporated in the model code. We will add the missing equations to the appendix.
(5) Role of immunity loss and model structure (SIR vs. SIRS)
We acknowledge that the decline of detectable antibodies over time (seropositivity decay/seroreversion) is an important consideration in RVFV serology, but whether this reflects true loss of protective immunity after natural infection remains unknown. Biologically, it is plausible that infected cattle develop long-lasting protection, as suggested by studies in humans, but there is an absence of longitudinal field data. From a modelling perspective, our aim was to predict age-seroprevalence curve dependent on FOI estimates and assess its ability to reproduce observed cross-sectional seroprevalence patterns. We therefore adopted a parsimonious SIR framework, treating loss of seropositivity as a potential explanation for the observed age disparity rather than modelling it as loss of immunity. In revisions we will:
Clarify this rationale, emphasising that there is no direct evidence for waning immunity following natural RVFV infection in cattle, although evidence of seropositivity decay has been suggested in human.
Further discuss the seropositivity decay rates predicted in our survey and their possible relation to test sensitivity.
Highlight that while a SIRS structure could generate different long-term dynamics, evaluating this requires stronger evidence for true immunity loss; we consider this an important future modelling direction.
(6) RVFV induced mortality in serocatalytic model
We thank the reviewer for this comment. Disease-induced mortality was included in the serocatalytic model through the mortality parameter (γ), but we recognise that this might not have been sufficiently clear in the text. In revisions we will clarify in the Methods and Appendix.
(7) Clarifying previous vs. current study components
We will revise the Methods and Appendix to make clearer distinctions between our previous work (e.g. household survey data collection, seroprevalence estimates) and the analyses undertaken for this manuscript (e.g. model development and fitting).
(8) Limitations paragraph
We will expand the limitations section to specifically identify the assumptions contributing most to uncertainty. We will then outline how these may bias transmission dynamics and intervention estimates.
(9) Movement ban simulations & suitability of model for vaccination interventions
We appreciate the reviewer’s concerns regarding the movement ban simulation. On reassessment, we agree that our model structure might not be ideally suited to exploring them. In the revised manuscript, we will remove this analysis and emphasize how our modelling framework is more suited to exploring cattle vaccination scenarios, including targeting of specific herd types (e.g. T vs. M vs. L). We note that we are currently developing separate work focused on vaccination strategies in cattle, where this model structure might be more directly applicable, and will reserve a deeper investigation of vaccination interventions for that forthcoming publication.
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