Epidemic spreading under mutually independent intra- and inter-host pathogen evolution

Abstract

The dynamics of epidemic spreading is often reduced to the single control parameter R0 (reproduction-rate), whose value, above or below unity, determines the state of the contagion. If, however, the pathogen evolves as it spreads, R0 may change over time, potentially leading to a mutation-driven spread, in which an initially sub-pandemic pathogen undergoes a breakthrough mutation. To predict the boundaries of this pandemic phase, we introduce here a modeling framework to couple the inter-host network spreading patterns with the intra-host evolutionary dynamics. We find that even in the extreme case when these two process are driven by mutually independent selection forces, mutations can still fundamentally alter the pandemic phase-diagram. The pandemic transitions, we show, are now shaped, not just by R0, but also by the balance between the epidemic and the evolutionary timescales. If mutations are too slow, the pathogen prevalence decays prior to the appearance of a critical mutation. On the other hand, if mutations are too rapid, the pathogen evolution becomes volatile and, once again, it fails to spread. Between these two extremes, however, we identify a broad range of conditions in which an initially sub-pandemic pathogen can breakthrough to gain widespread prevalence.

Isolation may select for earlier and higher peak viral load but shorter duration in SARS-CoV-2 evolution

“…If mutations occur too slowly, the virus prevalence decays prior to the appearance of a beneficial mutation that makes transmissibility higher. On the other hand, if mutations occur too rapidly, the pathogen evolution becomes volatile and, once again, the virus fails to spread 54 . Thus, mutation-selection balance is not negligible when discussing the evolutionary and epidemic outcomes in the future.…”

From heterogeneous network to homogeneous network: the influence of structure on synergistic epidemic spreading

“…Epidemic-like spreading phenomena in networked system occur in variety of forms, for example, the spread of diseases in human contact networks [1][2][3][4], the propagation of information and products through social networks [5][6][7][8], the diffusion of malware and virus on computer networks [9] and the neural activities in the human brain [10,11]. Epidemiological models, such as the susceptible-infected-susceptible (SIS) model [12,13], have been successful at characterizing the essential dynamics behind many spreading phenomena and thus providing important insights on the control and intervention of spreading process on complex networks [14][15][16]. In classical SIS model, the transmission of disease or information is assumed to be restricted in pairs of individuals (nodes) in network, i.e.…”

Spreading processes with mutations over multilayer networks

“…Moreover, even when a sizeable fraction of the population gains immunity through vaccination or natural infection, the emergence of new variants that can evade the acquired immunity poses a continued threat to public health ( 3 , 4 ). A growing body of work ( 27 , 34 – 44 ) has highlighted the need for developing multistrain epidemiological models that account for evolutionary adaptations in the pathogen. For instance, there is a vast literature on phylodynamics ( 38 – 41 ) which examines how epidemiological and evolutionary processes interact to impact pathogen phylogenies.…”

“…For instance, there is a vast literature on phylodynamics ( 38 – 41 ) which examines how epidemiological and evolutionary processes interact to impact pathogen phylogenies. The past decade has also seen the development of network-based models to identify risk factors for the emergence of pathogens in the light of different contact patterns ( 27 , 34 , 42 , 43 ). Further, a recent study ( 34 ) demonstrated that models that do not consider evolutionary adaptations may lead to incorrect predictions about spreading processes with mutations.…”

“…Most existing network-based approaches that analyze the spread of contagions with mutations center on single-layered contact networks, where transmissibility, i.e., the probability that an infective individual passes on the infection to a contact, depends on the type of strain but not on the nature of link/contact over which the infection is transmitted ( 27 , 34 , 43 ). However, different congregate settings such as schools, hospitals, offices, and private social gatherings pose varied transmission risks ( 45 ).…”