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Schematic representation of the metapopulation model with two pathogens. (a) Scheme of the metapopulation structure in patches and links representing mobility. (b) Compartmental model of the two-strain infection. A detailed description of the infection dynamics is reported in the Methods section.
Source publication
Different pathogens spreading in the same host population often generate
complex co-circulation dynamics because of the many possible interactions
between the pathogens and the host immune system, the host life cycle, and the
space structure of the population. Here we focus on the competition between two
acute infections and we address the role of...
Contexts in source publication
Context 1
... assume individuals to mix homogeneously within the local communities (also called subpopulations, patches or nodes of the metapopulation network), whereas at the global level the coupling is defined by a network of hosts' mobility fluxes. Here we adopt this scheme considering two pathogens circulating on the metapopulation network ( Figure 1a). ...
Context 2
... et al. [18] that assumes an individual recovered from one infection to have a susceptibility to the other circulating pathogen reduced by a factor σ -a schematic representa- tion of the compartmental model is reported in Figure 1b. The parameter σ quantifies the level of cross-immunity, with σ = 0 corresponding to complete cross-immunity and σ = 1 correspond- ing to no interaction. ...
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Citations
... Contagion processes can coexist and influence each other. Several instances can be found in disease spreading, whereby the presence of a certain pathogen may favor or hinder the diffusion of another one [137,138]. However, interaction between spreading processes is not limited to infectious disease, but can also be observed in social contagion, a striking example being the coupling between the COVID-19 epidemic and the adoption of safe behaviors [139]. ...
... Infectious diseases can indeed display complex comorbidity interactions, in which the presence of a pathogen impacts the individual susceptibility towards another [8], like HIV increasing susceptibility to other sexually transmitted diseases [9]. Modeling efforts in this direction include both cooperation [10][11][12] and competition [13][14][15] between diseases. However, to date, models of interacting contagion processes have been developed under two main assumptions: (i) the processes are simple contagions, and (ii) their interaction is symmetric, that is, bidirectional and of equal strength. ...
Single contagion processes are known to display a continuous transition from an epidemic-free state to an epidemic one, for contagion rates above a critical threshold. This transition can become discontinuous when two simple contagion processes are coupled in a bidirectional symmetric way. However, in many cases, the coupling is not symmetric and the nature of the processes can differ. For example, risky social behaviors—such as not wearing masks or engaging in large gatherings—can affect the spread of a disease, and their adoption dynamics via social reinforcement mechanisms are better described by complex contagion models rather than by simple contagions, more appropriate for disease spreading. Here, we consider a simplicial contagion (describing the adoption of a behavior) that unidirectionally drives a simple contagion (describing a disease propagation). We show, both analytically and numerically, that, above a critical driving strength, such a driven simple contagion can exhibit both discontinuous transitions and bistability, absent otherwise. Our results provide a route for a simple contagion process to display the phenomenology of a higher-order contagion, through a driving mechanism that may be hidden or unobservable in practical instances.
... Girvan et al. showed that four epidemiological dynamics such as periodic epidemic outbreaks were observed during the mutation of pathogens [38]. The study of Poletto et al. addressed the role of host mobility as well as cross-immunity in shaping possible dominance regimes [39]. Previous mathematical models, however, are not applicable to the current stage of pandemic transmission due to some special characteristics of SARS-CoV-2 competing variants, such as super immune escape ability and unbalanced cross-immunity levels. ...
SARS-CoV-2 has produced various variants during its ongoing evolution. The competitive behavior driven by the co-transmission of these variants has influenced the pandemic transmission dynamics. Therefore, studying the impact of competition between SARS-CoV-2 variants on pandemic transmission dynamics is of considerable practical importance. In order to formalize the mechanism of competition between SARS-CoV-2 variants, we propose an epidemic model that takes into account the co-transmission of competing variants. The model focuses on how cross-immunity influences the transmission dynamics of SARS-CoV-2 through competitive mechanisms between strains. We found that inter-strain competition affects not only both the final size and the replacement time of the variants, but also the invasive behavior of new variants in the future. Due to the limited extent of cross-immunity in previous populations, we predict that the new strain may infect the largest number of individuals in China without control interventions. Moreover, we also observed the possibility of periodic outbreaks in the same lineage and the possibility of the resurgence of previous lineages. Without the invasion of a new variant, the previous variant (Delta variant) is projected to resurgence as early as 2023. However, its resurgence may be prevented by a new variant with a greater competitive advantage.
... Epidemic spread of infectious diseases is a topic that has received much attention among computational modelers, see, e.g., Ma and Xia (2009), Diekmann et al. (2012), Siettos and Russo (2013), Heesterbeek et al. (2015), Cobey (2020). One important aspect of this process is the rise and spread of mutant variants of the pathogen (Feng et al. 2006;Day et al. 2011;Poletto et al. 2013Poletto et al. , 2015Griette et al. 2015;Gandon et al. 2016). For example, in a spatially expanding epidemic, it was shown that less virulent strains will dominate the periphery while more virulent strains will prevail at the core (Osnas et al. 2015). ...
It is well known in the literature that human behavior can change as a reaction to disease observed in others, and that such behavioral changes can be an important factor in the spread of an epidemic. It has been noted that human behavioral traits in disease avoidance are under selection in the presence of infectious diseases. Here, we explore a complementary trend: the pathogen itself might experience a force of selection to become less “visible,” or less “symptomatic,” in the presence of such human behavioral trends. Using a stochastic SIR agent-based model, we investigated the co-evolution of two viral strains with cross-immunity, where the resident strain is symptomatic while the mutant strain is asymptomatic. We assumed that individuals exercised self-regulated social distancing (SD) behavior if one of their neighbors was infected with a symptomatic strain. We observed that the proportion of asymptomatic carriers increased over time with a stronger effect corresponding to higher levels of self-regulated SD. Adding mandated SD made the effect more significant, while the existence of a time-delay between the onset of infection and the change of behavior reduced the advantage of the asymptomatic strain. These results were consistent under random geometric networks, scale-free networks, and a synthetic network that represented the social behavior of the residents of New Orleans.
... For example, it is well known that HIV increases susceptibility to other sexually transmitted diseases [9]. Several research lines have extended modelling efforts in this direction to include both cooperation [10][11][12] and competition [13][14][15] between diseases. However, to date the systematic investigation of models of interacting contagion processes has been developed under two main assumptions: (i) they consider simple contagions, and (ii) they assume the interaction between processes to be symmetric, that is, bi-directional and of equal strength. ...
Single contagion processes are known to display a continuous transition from an epidemic-free phase at low contagion rates to the epidemic state for rates above a critical threshold. This transition can become discontinuous when two simple contagion processes are coupled in a bi-directional symmetric way. However, in many cases, the coupling is not symmetric and the processes can be of a different nature. For example, social behaviors -- such as hand-washing or mask-wearing -- can affect the spread of a disease, and their adoption dynamics via social reinforcement mechanisms are better described by complex contagion models, rather than by the simple contagion paradigm, which is more appropriate for disease spreading phenomena. Motivated by this example, we consider a simplicial contagion (describing the adoption of a behavior) that uni-directionally drives a simple contagion (describing a disease propagation). We show that, above a critical driving strength, such driven simple contagion can exhibit both discontinuous transitions and bi-stability, which are instead absent in standard simple contagions. We provide a mean-field analytical description of the phase diagram of the system, and complement the results with Markov-chain simulations. Our results provide a novel route for a simple contagion process to display the phenomenology of a higher-order contagion, through a driving mechanism that may be hidden or unobservable in many practical instances.
... Studies have demonstrated that the faster pathogen is more likely to dominate the network 14-17 . When different cross-immunity levels are considered, this is not necessarily the case 18 . However, these studies did not consider the temporal ordering of events and the time-respecting paths. ...
... Mann et al. 16 showed that in this setting, clustering increases the spread of the second wave. Poletto et al. 18 considered, like us, the competition under different levels of cross-immunity, however, in a mix-homogeneous aggregated network. They found that variations in the crossimmunity level induce a transition between the presence and absence of competition. ...
The temporal dynamics of social interactions were shown to influence the spread of disease. Here, we model the conditions of progression and competition for several viral strains, exploring various levels of cross-immunity over temporal networks. We use our interaction-driven contagion model and characterize, using it, several viral variants. Our results, obtained on temporal random networks and on real-world interaction data, demonstrate that temporal dynamics are crucial to determining the competition results. We consider two and three competing pathogens and show the conditions under which a slower pathogen will remain active and create a second wave infecting most of the population. We then show that when the duration of the encounters is considered, the spreading dynamics change significantly. Our results indicate that when considering airborne diseases, it might be crucial to consider the duration of temporal meetings to model the spread of pathogens in a population.
... For instance, animals in an ecosystem may compete for food or mates; in a market economy, firms may compete over customers; sensory stimuli may compete for limited neural resources to achieve a particular task. In particular, the interaction between co-circulating pathogens and the immune system of a host may generate a complex competing dynamics [25], as well as two competing diseases spreading over the same population at the same time, where infection with either disease gives an individual subsequent immunity to both [19]. Furthermore, the decision of whether to get vaccinated or not might imply an implicit competition between cost and benefit of the vaccine by the end of the season of a seasonal disease [8]. ...
Studies of the dynamics of predator-prey systems are abundant in the literature. In an attempt to account for more realistic models, previous studies have opted for the use of discrete predator-prey systems with ratio-dependent functional response. In the present research we go a step further with the inclusion of impulsive effects in the dynamics. More concretely, by assuming that the coefficients involved in the system and the impulses are periodic , we obtain sufficient conditions for the existence of periodic solutions. We present some numerical examples to illustrate the effectiveness of our results.
... Studies have demonstrated that the faster pathogen is more likely to dominate the network [14][15][16][17]. When different cross-immunity levels are considered, this is not necessarily the case [18]. However, these studies did not consider the temporal ordering of events and the time-respecting paths. ...
... Poletto et.al. [18] considered, like us, the competition under different levels of cross-immunity, however, in a mix-homogeneous aggregated network. They found that variations in the cross-immunity level induce a transition between the presence and absence of competition. ...
The temporal dynamics of social interactions were shown to influence the spread of disease. Here, we model the progression and competition conditions of a number of pathogens with various levels of cross-immunity over temporal networks. We use our interaction-driven contagion model of Covid-19 and model some of its variants. Our results, obtained on both temporal random networks and real-world interaction data, demonstrate that temporal dynamics are crucial to the competition conditions. We consider two and three competing pathogens and show that there are conditions under which a slower pathogen will remain active and create a second wave in which it infects the majority of the population. We then show that when the duration of the encounters is considered, the spreading dynamics change significantly. Our results indicate that when considering airborne diseases, it might be crucial to take into account the duration of temporal meetings to model the spread of pathogens in a population.
... To show how individual infection histories for multiple parasite strains can inform us about the underlying transmission contact network, we conduct a simulation study, which requires alleviating two obstacles. First, we need to simulate multiple infections on a network, a task few studies have attempted [12,[37][38][39][40][41][42]. For this, we take advantage of recent developments in stochastic epidemiological modelling and implement a non-Markovian version of the well-known Gillespie algorithm [43]. ...
Interactions within a population shape the spread of infectious diseases but contact patterns between individuals are difficult to access. We hypothesised that key properties of these patterns can be inferred from multiple infection data in longitudinal follow-ups. We developed a simulator for epidemics with multiple infections on networks and analysed the resulting individual infection time series by introducing similarity metrics between hosts based on their multiple infection histories. We find that, depending on infection multiplicity and network sampling, multiple infection summary statistics can recover network properties such as degree distribution. Furthermore, we show that by mining simulation outputs for multiple infection patterns, one can detect immunological interference between pathogens (i.e. the fact that past infections in a host condition future probability of infection). The combination of individual-based simulations and analysis of multiple infection histories opens promising perspectives to infer and validate transmission networks and immunological interference for infectious diseases from longitudinal cohort data.
... [1,2,3,4,5]. One important aspect of this process is the rise and spread of mutant variants of the pathogen [6,7,8,9,10,11]. For example, in a spatially expanding epidemic, it was shown that less virulent strains will dominate the periphery while more virulent strains will prevail at the core [12]. ...
It is well known in the literature that human behavior can change as a reaction to disease observed in others, and that such behavioral changes can be an important factor in the spread of an epidemic. It has been noted that human behavioral traits in disease avoidance are under selection in the presence of infectious diseases. Here we explore a complimentary trend: the pathogen itself might experience a force of selection to become less "visible", or less "symptomatic", in the presence of such human behavioral trends. Using a stochastic SIR agent-based model, we investigated the co-evolution of two viral strains with cross-immunity, where the resident strain is symptomatic while the mutant strain is asymptomatic. We assumed that individuals exercised self-regulated social distancing (SD) behavior if one of their neighbors was infected with a symptomatic strain. We observed that the proportion of asymptomatic carriers increased over time with a stronger effect corresponding to higher levels of self-regulated SD. Adding mandated SD made the effect more significant, while the existence of a time-delay between the onset of infection and the change of behavior reduced the advantage of the asymptomatic strain. These results were consistent under random geometric networks, scale-free networks, and a synthetic network that represented the social behavior of the residents of New Orleans.
