Individual Variation Affects Outbreak Magnitude and Predictability in an Extended Multi-Pathogen SIR Model of Pigeons Vising Dairy Farms

TL;DR

This study addresses zoonotic risk at the wildlife–livestock interface by developing a spatially explicit, multi-pathogen SEIRD framework for pigeons and indoor cows, implemented as an agent-based simulation informed by field data. The core finding is that individual heterogeneity in pigeon movement, modeled as exploration–exploitation dynamics, strongly modulates outbreak magnitude and predictability, and that co-infections across multiple pathogens yield non-intuitive interactions absent in single-pathogen models. Sensitivity analyses show that increasing the number of pathogens or the diversity of movement behaviors raises $E[R_t]$, the peak infection, and cow mortality, while movement reductions in infected pigeons mitigate spread. The work highlights the importance of incorporating host behavioral heterogeneity and multi-pathogen interactions for realistic outbreak predictions in the wildlife–livestock interface and supports One Health–oriented risk assessment and intervention planning.

Abstract

Zoonotic disease transmission between animals and humans is a growing risk and the agricultural context acts as a likely point of transition, with individual heterogeneity acting as an important contributor. Thus, understanding the dynamics of disease spread in the wildlife-livestock interface is crucial for mitigating these risks of transmission. Specifically, the interactions between pigeons and in-door cows at dairy farms can lead to significant disease transmission and economic losses for farmers; putting livestock, adjacent human populations, and other wildlife species at risk. In this paper, we propose a novel spatio-temporal multi-pathogen model with continuous spatial movement. The model expands on the Susceptible-Exposed-Infected-Recovered-Dead (SEIRD) framework and accounts for both within-species and cross-species transmission of pathogens, as well as the exploration-exploitation movement dynamics of pigeons, which play a critical role in the spread of infection agents. In addition to model formulation, we also implement it as an agent-based simulation approach and use empirical field data to investigate different biologically realistic scenarios, evaluating the effect of various parameters on the epidemic spread. Namely, in agreement with theoretical expectations, the model predicts that the heterogeneity of the pigeons' movement dynamics can drastically affect both the magnitude and stability of outbreaks. In addition, joint infection by multiple pathogens can have an interactive effect unobservable in single-pathogen SIR models, reflecting a non-intuitive inhibition of the outbreak. Our findings highlight the impact of heterogeneity in host behavior on their pathogens and allow realistic predictions of outbreak dynamics in the multi-pathogen wildlife-livestock interface with consequences to zoonotic diseases in various systems.

Figures (9)

• Figure 1: Two images of pigeons in a cowshed located in Israel, taken on the site of the empirical data collection of orr_base_paper. The images show the high proximity of pigeons to cows and their food (facilitating transmission), and the high density and mobility of pigeons (underscoring their between-site transmission potential). Photos by Tovale Solomon.
• Figure 2: A schematic view of transition between disease stages, shown for $k=2$. The red arrows indicate that from this stage, the animal might die from the disease. In a similar manner, the orange, black, blue, and green arrows indicate exposure, infection, recovery, and immunity decay, respectively. Notably, several states are duplicated for ease of reading (e.g. 2;,1,).
• Figure 3: A schematic view of a pigeon movement in a 2-dimensional space with the exploitation vector ($b(t)$) at a specific time and over time (marked by the gray line) and the exploration vector ($\frac{\partial^2 P_{s,e,i,r}(t,x,y)}{\partial x^2} + \frac{\partial^2 P_{s,e,i,r}(t,x,y)}{\partial t^2}$) which looking for resources (such as food). For our model, all these alternative explored sites can be condensed into a single one as they do not directly affect transmission dynamics.
• Figure 4: A schematic view of the synthetic scenario generation process.
• Figure 5: Epidemic metrics as a function of the pigeon population size and number of cowsheds with 50 cows at each one. The results are shown as a mean of $n=1000$ simulation realizations. Notably, all three metrics show an increase with a growing number of pigeons, in interaction with the number of cowsheds