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Efforts by theoreticians to understand pathogen coexistence have focused on population structure, while unrealistically assuming constant conditions. Andreasen & Dwyer show that pathogen coexistence can arise because of seasonality, a mechanism that affects a broad range of systems
Have you ever been ill over the winter with a cold or the flu, started to recover, and then ended up catching a different “bug” before you were fully better? If you’ve ever thought about why so many more people become ill during the winter than do during the summer, you’ve noticed that seasonality can drive the transmission of disease.
The dynamics of seasonal pathogen coexistence are complex. To allow for coexistence, non-seasonal models must invoke spatial variation in host or pathogen densities or population structures. However, Dr. Viggo Andreasen of Roskilde University, Denmark and Dr. Greg Dwyer of the University of Chicago, USA, find that pathogen coexistence can instead be explained by temporal variation in host reproduction and pathogen transmission, and thus by seasonality.
In their study, Andreasen and Dwyer constructed a model in which pathogen fitness depends on both infectiousness during the epidemic period and survival in inter-epidemic periods. They then used their model to show how seasonality in host reproduction can foster strain coexistence. Seasonal epidemic periods are driven by an influx of susceptible new hosts due to reproduction, which provide an increased opportunity for infection. Coexistence is possible because strain fitness can be higher in either the epidemic period (just after the host reproduces) or the inter-epidemic period, permitting strains to pursue different infection strategies.
In the first pair of strategies that enable coexistence, one pathogen has very high inter-epidemic survival while the other pathogen has low inter-epidemic survival but infects more hosts and thus has a higher reproductive number. The strain with a higher reproductive number will be pushed toward extinction during inter-epidemic periods, but it can rebound during the epidemic period. A second pair of strategies that enable coexistence involve differences in initial epidemic fitness and reproductive rate. In this case, the strain with a lower reproductive number infects more hosts per unit time, and so it has a higher initial fitness that counterbalances its lower reproductive number. Seasonality can allow high-infectiousness/low-survival pathogen strains to coexist with low-infectiousness/high-survival pathogen strains in these two different ways.
Andreasen and Dwyer’s work thus highlights the importance of seasonal variation in driving pathogen coexistence. Further, their work shows that population structure is not a necessary precondition for pathogen coexistence. It turns out timing is everything, even for pathogens.
Host-pathogen models usually explain the coexistence of pathogen strains by invoking population structure, meaning host or pathogen variation across space or individuals; most models, however, neglect the seasonal variation typical of host-pathogen interactions in nature. To determine the extent to which seasonality can drive pathogen coexistence, we constructed a model in which seasonal host reproduction fuels annual epidemics, which are in turn followed by interepidemic periods with no transmission, a pattern seen in many host-pathogen interactions in nature. In our model, a pathogen strain with low infectiousness and high interepidemic survival can coexist with a strain with high infectiousness and low interepidemic survival: seasonality thus permits coexistence. This seemingly simple type of coexistence can be achieved through two very different pathogen strategies, but understanding these strategies requires novel mathematical analyses. Standard analyses show that coexistence can occur if the competing strains differ in terms of R0, the number of new infections per infectious life span in a completely susceptible population. A novel mathematical method of analyzing transient dynamics, however, allows us to show that coexistence can also occur if one strain has a lower R0 than its competitor but a higher initial fitness &lambda0, the number of new infections per unit time in a completely susceptible population. This second strategy allows coexisting pathogens to have quite similar phenotypes, whereas coexistence that depends on differences in R0 values requires that coexisting pathogens have very different phenotypes. Our novel analytic method suggests that transient dynamics are an overlooked force in host-pathogen interactions.
Madeline Eppley is a PhD student at Northeastern University studying the relationship between spatial and temporal evolution in marine systems. They use eastern oyster genomics to investigate patterns of adaptation to environment and disease across space and time. When they’re not tackling cool science, they tackle people as a rugby player for Northeastern.