Vector Traits Shape Disease Persistence: A Predator Prey Approach to Dengue

TL;DR

The study reframes dengue transmission as a predator–prey problem where the Aedes vector is the prey and the dengue virus is the predator, with vector competence $v_c$ driving within-vector infection dynamics. By incorporating Holling-type functional responses and an explicit pathogen density, the authors derive conditions for disease-free global stability and characterize endemic equilibria, finding that persistent dengue requires higher $v_c$ when nonlinearity increases from Type I to III. A key result is a fundamental trade-off: vectors can evolve increased transmission potential but cannot proportionally enhance immune defenses, shaping endemic risk under tropical and subtropical conditions. These findings offer theoretical bounds and mechanistic insights that can inform vector-control strategies and highlight the need to integrate vector traits with ecological and climate context in disease management.

Abstract

Dengue continues to pose a major global threat, infecting nearly 390 million people annually. Recognizing the pivotal role of vector competence (vc), recent research focuses on mosquito parameters to inform transmission modeling and vector control strategies.This study models interactions between Aedes vectors and dengue pathogens, highlighting vc as a key driver of within vector infection dynamics and endemic persistence. Using a predator prey framework, we show that endemic conditions emerge naturally from the biological interplay between the vectors strategies to pathogen pressure and we prove global stability of such conditions. Our results reveal that under tropical and subtropical environmental pressures, the innate immune system of vectors cannot offset high vc during endemic outbreaks, highlighting a fundamental biological trade off, vectors can evolve increased transmission potential but cannot enhance immune capacity. This constraint defines the limits of their evolutionary response to pathogen driven selection and drives instability in disease transmission dynamics.

Figures (6)

• Figure 1: The transmission structures: (a) Schematic diagram of the coupled SIR (block 1)–SI (block 2) system. Vector transition from susceptibility to infection is driven by $I_h$ (red dashed line), while human transition from $S_h$ to $I_h$ is driven by $I_v$ (purple dashed line). (b) The infected human population drives vector–pathogen interactions, shifting vectors from susceptible to infected. The solid double-sided arrow denotes the predation relationship between the pathogen and susceptible vectors.
• Figure 2: Fitness trade-off between resistance (immune defense) and tolerance (reproduction) within the vector's body after taking a viremic blood meal. Red arrows represent the flow of trade-off when resistance is increased, whereas blue arrows show the same flow when the tolerance is increased.
• Figure 3: The stability of $E_3$ is illustrated for functional responses (a) Type I ($q=0$), (b) Type II ($q=1$), and (c) Type III ($q=2$) in model \ref{['holling-specific']} - \ref{['final model']}. The trajectories of $E_3$ and $E_2$ are traced as $v_c$ increases beyond their respective transcritical bifurcation thresholds; $v_c = 36.51\%,38.49\%,\text{ and }52.91\%$ respectively. Values for other parameters were set at $\mu_v=0.166, \beta_v=0.375, E_p=2, K=1, \delta=0.1$, $\alpha=2$ (references of the literature are given in Table \ref{['Table: parameter definition']}).
• Figure 4: Variation in the vector competence ($v_c$) threshold required for endemicity across Holling functional response types, illustrating how resource-dependent consumption rates elevate the competency level needed for a stable endemic state. The red points are the $v_c$ thresholds from bifurcation and the blue solid like is a linear fit ($v_c = 0.3382 + 0.0841q$) with shaded area depicting uncertainty quantification (95% credible interval). The $v_c$ with their serotype are marked at $q=1$ for sub-tropical countries and $q=2$ for tropical countries, where dengue is prevalent.
• Figure 5: Comparison of simulations of the system \ref{['holling-specific']}-\ref{['final model']}. Model simulations for the system for (a) $q=0$, (b) $q=1$, and (c) $q=2$. The parameter values used are $v_c=0.9, \mu_v=0.166, \beta_v=0.375, E_p=2, K=1, \delta=0.1$ and $\alpha=2$.

Theorems & Definitions (11)

• Theorem 3.1
• proof
• Definition 1
• Theorem 3.2
• proof
• Theorem 3.3
• Definition 2
• Definition 3
• Theorem 3.4
• proof