The dynamics of disease mediated invasions by hosts with immune reproductive tradeoff

Indirect interaction between an endemic and an invading pathogen: A case study of Plasmodium and Usutu virus dynamics in a shared bird host population

Infectious disease agents can influence each other's dynamics in shared host populations. We consider such influence for two mosquito-borne infections where one pathogen is endemic at the time that a second pathogen invades. We regard a setting where the vector has a bias towards biting host individuals infected with the endemic pathogen and where there is a cost to co-infected hosts. As a motivating case study, we regard Plasmodium spp., that cause avian malaria, as the endemic pathogen, and Usutu virus (USUV) as the invading pathogen. Hosts with malaria attract more mosquitoes compared to susceptible hosts, a phenomenon named vector bias. The possible trade-off between the vector-bias effect and the co-infection mortality is studied using a compartmental epidemic model. We focus first on the basic reproduction number R0 for Usutu virus invading into a malaria-endemic population, and then explore the long-term dynamics of both pathogens once Usutu virus has become established. We find that the vector bias facilitates the introduction of malaria into a susceptible population, as well as the introduction of Usutu in a malaria-endemic population. In the long term, however, both a vector bias and co-infection mortality lead to a decrease in the number of individuals infected with either pathogen, suggesting that avian malaria is unlikely to be a promoter of Usutu invasion. This proposed approach is general and allows for new insights into other negative associations between endemic and invading vector-borne pathogens.

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

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.

Invasion potential of hornets (Hymenoptera: Vespidae: Vespa spp.)

Hornets are large, predatory wasps that have the potential to alter biotic communities and harm honey bee colonies once established in non-native locations. Mated, diapausing females (gynes) can easily be transported to new habitats, where their behavioral flexibility allows them to found colonies using local food and nest materials. Of the 22 species in the genus Vespa, five species are now naturalized far from their endemic populations and another four have been detected either in nature or during inspections at borders of other countries. By far the most likely pathway of long-distance dispersal is the transport of gynes in transoceanic shipments of goods. Thereafter, natural dispersal of gynes in spring and accidental local transport by humans cause shorter-range expansions and contribute to the invasion process. Propagule pressure of hornets is unquantified, although it is likely low but unrelenting. The success of introduced populations is limited by low propagule size and the consequences of genetic founder effects, including the extinction vortex linked to single-locus, complementary sex determination of most hymenopterans. Invasion success is enhanced by climatic similarity between source locality and introduction site, as well as genetic diversity conferred by polyandry in some species. These and other factors that may have influenced the successful establishment of invasive populations of V. velutina, V. tropica, V. bicolor, V. orientalis, and V. crabro are discussed. The highly publicized detections of V. mandarinia in North America and research into its status provide a real-time example of an unfolding hornet invasion.

A 'Portfolio of Model Approximations' approach to understanding invasion success with vector-borne disease

The central challenge of mathematical modeling of real-world systems is to strike an appropriate balance between insightful abstraction and detailed accuracy. Models in mathematical epidemiology frequently tend to either extreme, focusing on analytically provable boundaries in simplified, mass-action approximations, or else relying on calculated numerical solutions and computational simulation experiments to capture nuance and details specific to a particular host-disease system. We propose that there is value in an approach striking a slightly different compromise in which a detailed but analytically difficult system is modeled with careful detail, but then abstraction is applied to the results of numerical solutions to that system, rather than to the biological system itself. In this 'Portfolio of Model Approximations' approach, multiple levels of approximation are used to analyze the model at different scales of complexity. While this method has the potential to introduce error in the translation from model to model, it also has the potential to produce generalizable insight for the set of all similar systems, rather than isolated, tailored results that must be started anew for each next question. In this paper, we demonstrate this process and its value with a case study from evolutionary epidemiology. We consider a modified Susceptible-Infected-Recovered model for a vector-borne pathogen affecting two annually reproducing hosts. From observing patterns in simulations of the system and exploiting basic epidemiological properties, we construct two approximations of the model at different levels of complexity that can be treated as hypotheses about the behavior of the model. We compare the predictions of the approximations to the simulated results and discuss the trade-offs between accuracy and abstraction. We discuss the implications for this particular model, and in the context of mathematical biology in general.

Spatial organisation plasticity reduces disease infection risk in rock-paper-scissors models

We study a three-species cyclic game system where organisms face a contagious disease whose virulence may change by a pathogen mutation. As a responsive defence strategy, organisms' mobility is restricted to reduce disease dissemination in the system. The impact of the collective self-preservation strategy on the disease infection risk is investigated by performing stochastic simulations of the spatial version of the rock-paper-scissors game. Our outcomes show that the mobility control strategy induces plasticity in the spatial patterns with groups of organisms of the same species inhabiting spatial domains whose characteristic length scales depend on the level of dispersal restrictions. The spatial organisation plasticity allows the ecosystems to adapt to minimise the individuals' disease contamination risk if an eventual pathogen alters the disease virulence. We discover that if a pathogen mutation makes the disease more transmissible or less lethal, the organisms benefit more if the mobility is not strongly restricted, thus forming large spatial domains. Conversely, the benefits of protecting against a pathogen causing a less contagious or deadlier disease are maximised if the average size of groups of individuals of the same species is significantly limited, reducing the dimensions of groups of organisms significantly. Our findings may help biologists understand the effects of dispersal control as a conservation strategy in ecosystems affected by epidemic outbreaks.