Barreaux, A. M. G. | Search bioRxiv

bioRxiv2026

Spatial distribution and habitat suitability of tsetse (Glossina spp.) in Cote dIvoire: An ensemble modeling approach to support targeted disease control

Background Tsetse are vectors of trypanosomes responsible for African animal trypanosomosis (AAT) and human African trypanosomiasis (HAT). While Cote dIvoire has successfully eliminated HAT as a public health problem and approaches elimination of transmission, AAT remains a major obstacle to agriculture and livestock production. Understanding the spatial distribution of tsetse is essential for prioritizing and sustaining disease control and elimination efforts. Methodology/Principal Findings Using 1,702 occurrence records from the national tsetse atlas we modeled the habitat suitability of the nine tsetse species present in Cote dIvoire. We identified suitable habitats in unsampled areas and quantified environmental constraints on tsetse distribution. Resampling the data to a 1km x 1km grid produced spatially explicit outputs at a resolution more relevant for operational planning. An ensemble modeling approach was employed integrating four algorithms--Random Forest, XGBoost, Maximum Entropy (MaxEnt), and Generalized Additive Models (GAM)-- with satellite-derived environmental and anthropogenic predictors--which achieved high predictive accuracy, area under the curve and True Skill Statistics 0.80 and 0.83, respectively. Distance to waterbodies, soil moisture, distance to protected areas, maximum land surface temperature, and sheep density were key drivers of habitat suitability. Importantly, the models identified suitable habitats in 11 administrative regions not covered by the atlas, providing an improved national tsetse risk profile. Conclusions/Significance These results provide a detailed assessment of the ecological suitability of tsetse across Cote dIvoire and their persistence in agroecological mosaics with high human and livestock densities. We offer a high-resolution blueprint for vector and disease control, particularly in areas where field data are currently lacking. We provide a robust framework for evidence-based decision-making within the Progressive Control Pathway (PCP) for AAT by enabling the identification of priority areas and resource allocation optimization to improve livestock productivity through more effective AAT control and reduce the risk of resurgence of HAT.

ecology↗

bioRxiv2026

From static thresholds to dynamic waves: How biological memory destabilizes malaria transmission Potential

The basic reproduction number R0 is central to malaria epidemiology, yet it is typically treated as a static quantity derived under memoryless assumptions for mosquito demography. In natural systems, however, mosquito populations are shaped by delayed processes such as larval development and density-dependent feedback, introducing biological memory into vector dynamics. We develop a minimal delay-based framework that incorporates this memory into the Ross-Macdonald model by describing adult mosquito abundance with a retarded differential equation. This formulation induces a time-dependent transmission potential R0(t). Using complex analysis and the argument principle, we derive an explicit stability threshold [Formula], which separates stable from oscillatory transmission regimes. Near this threshold, delayed feedback produces slow relaxation times and sustained transient oscillations, implying that transmission potential may vary intrinsically even in the absence of external forcing. To account for ecological variability, we extend this deterministic condition into a probabilistic framework and define the stability probability as [Formula]. Numerical simulations and global sensitivity analysis show that recruitment and developmental delays are the primary drivers of instability, while adult mortality has a weaker stabilizing effect. These results indicate that malaria interventions may influence not only the magnitude of malaria transmission but also its dynamical stability. By linking delay dynamics, transmission theory, and uncertainty quantification, this framework provides a basis for stability-aware modeling and interpretation of malaria transmission under ecological variability. Author summaryMalaria transmission is often summarized by a single number, R0, treated as a fixed indicator of whether transmission will increase or decline. This assumes mosquito populations respond instantly to environmental conditions. In reality, mosquitoes develop through stages where larval conditions, such as crowding, nutrition, or temperature, affect adult populations only after a delay. This creates biological memory: todays mosquitoes reflect past environments. We show that this memory can fundamentally reshape transmission dynamics. When developmental delays are included, transmission potential is no longer constant but can fluctuate over time, even in stable environments. These fluctuations can persist or amplify depending on the balance between mosquito growth, mortality, and delay. As a result, variability in mosquito abundance or malaria transmission may arise from intrinsic dynamics rather than external drivers alone. Under ecological variability, stability becomes probabilistic, allowing estimation of how likely transmission is to remain stable. Interventions that reduce larval productivity or increase adult mortality may therefore both lower transmission and make it more predictable, improving interpretation and control strategies.

ecology↗