Spatial dynamics of synergistic coinfection in rock-paper-scissors models

Author:

Menezes J.12ORCID,Rangel E.34ORCID

Affiliation:

1. School of Science and Technology, Federal University of Rio Grande do Norte 1 , P.O. Box 1524, Natal 59072-970, RN, Brazil

2. Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam 2 , Science Park 904, 1098 XH Amsterdam, The Netherlands

3. Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte 3 , Av. Senador Salgado Filho 300, Natal 59078-970, Brazil

4. Edmond and Lily Safra International Neuroscience Institute, Santos Dumont Institute 4 , Av Santos Dumont 1560, 59280-000 Macaiba, RN, Brazil

Abstract

We investigate the spatial dynamics of two-disease epidemics reaching a three-species cyclic model. Regardless of their species, all individuals are susceptible to being infected with two different pathogens, which spread through person-to-person contact. We consider that the simultaneous presence of multiple infections leads to a synergistic amplification in the probability of host mortality due to complications arising from any of the co-occurring diseases. Employing stochastic simulations, we explore the ramifications of this synergistic coinfection on spatial configurations that emerge from stochastic initial conditions. Under conditions of pronounced synergistic coinfection, we identify the emergence of zones inhabited solely by hosts affected by a singular pathogen. At the boundaries of spatial domains dominated by a single disease, interfaces of coinfected hosts appear. The dynamics of these interfaces are shaped by curvature-driven processes and display a scaling behavior reflective of the topological attributes of the underlying two-dimensional space. As the lethality linked to coinfection diminishes, the evolution of the interface network’s spatial dynamics is influenced by fluctuations stemming from waves of coinfection that infiltrate territories predominantly occupied by a single disease. Our analysis extends to quantifying the implications of synergistic coinfection at both the individual and population levels Our outcomes show that organisms’ infection risk is maximized if the coinfection increases the death due to disease by 30% and minimized as the network dynamics reach the scaling regime, with species populations being maximum. Our conclusions may help ecologists understand the dynamics of epidemics and their impact on the stability of ecosystems.

Publisher

AIP Publishing

Subject

Applied Mathematics,General Physics and Astronomy,Mathematical Physics,Statistical and Nonlinear Physics

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