Coinfection: Doing the Math

Coinfection of hosts with more than one microorganism is ubiquitous in natural systems, but its ef ects are not simple. Upon coinfection with multiple parasites (def ned here as infectious organisms that cause harm to their hosts), a range of within-host outcomes may occur, including host pathological changes and immune responses as well as ef ects on the individual parasites (Fig. 1). Some outcomes have ef ects at the population level, while others are conf ned to individuals (1, 2). Now, Shrestha et al. use a mathematical approach to dissect the confounding ef ects of coinfection with the inf uenza virus and the bacterium Streptococcus pneumoniae (3). In laboratory models and in studies of individual host pathology or immune response, coinfection of en results in host or parasite responses beyond the simple additive ef ects of the two species (1, 4, 5). However, the extent to which such changes scale to population-level ef ects is a matter for debate (6). Coinfection-induced changes in host susceptibility and parasite-transmission potential can be observed in laboratory settings, but of en, epidemiological signatures of these ef ects (such as changes in infectionpeak height or duration) are equivocal. Biotic and abiotic environmental factors inf uence host-to-host parasite transmission (e.g., climate and vector availability). Once an infectious agent reaches a new prospective host, his or her susceptibility could be af ected by host genetics, physical condition, and behavior. T ere are two possible consequences of such environmental and host inf uences: (i) these factors break the link between the individual-host and the population-level ef ects of coinfection or (ii) coinfection properties in one host could be retained in subsequent hosts, thus scaling the ef ects to the population level; still, the epidemiological signatures of these ef ects could be obscured by inf uencing factors (6). In many systems, and particularly in humans, experimentation cannot be used as a method to tease apart these possibilities. Instead, mathematical tools must be developed to achieve this aim (7). In laboratory investigations with animal models, inf uenza has been shown to increase both susceptibility and pathological response to subsequent pneumococcal infection (5, 8); similarly, there is little doubt that inf uenza virus exacerbates the pathology that results from pneumococcal coinfection in human subjects (9). However, epidemiological studies of coinfected human populations have not yielded such clear results, leading to questions of whether and how the withinhost dynamics of the coinfection scale to the population level. In their new work, Shrestha et al. take a mathematical approach by using a mechanistic transmission model within a Bayesian likelihood-based inference framework to determine the role of within-host coinfection dynamics. T e authors model inf uenza virus as a potential driver of the epidemiological dynamics of Streptococcus pneumoniae infection in human populations. T is approach is based on a fairly simple and well-known structure, an adapted SIRS model (where S = susceptible, I = infected, and R = recently recovered). However, the model has been applied in a new way to address questions about the scaling of coinfection dynamics from the individual to population level. T e SIRS model takes into account the coinfection with inf uenza by subdividing the susceptible and infected compartments of the model into inf uenza-infected and uninfected hosts. T is model is then applied to two years of weekly epidemiological records of inf uenza and pneumococcal pneumonia hospitalizations in Illinois, USA. Using this framework, the authors formally tested three potential hypotheses for the role of inf uenza in driving the pneumococcal epidemiology. T e three alternative hypotheses are not mutually exclusive; all have the potential to be supported or indeed, unsupported (suggesting no ef ect of inf uenza): (i) T e transmission hypothesis assumes that individuals recently infected with inf uenza will have a higher contribution to pneumococcal M O D E L I N G