Time and dose-dependent risk of pneumococcal pneumonia following influenza: a model for within-host interaction between influenza andStreptococcus pneumoniae

Abstract

A significant fraction of seasonal and in particular pandemic influenza deaths are attributed to secondary bacterial infections. In animal models, influenza virus predisposes hosts to severe infection with both Streptococcus pneumoniae and Staphylococcus aureus. Despite its importance, the mechanistic nature of the interaction between influenza and pneumococci, its dependence on the timing and sequence of infections as well as the clinical and epidemiological consequences remain unclear. We explore an immunemediated model of the viral -bacterial interaction that quantifies the timing and the intensity of the interaction. Taking advantage of the wealth of knowledge gained from animal models, and the quantitative understanding of the kinetics of pathogen-specific immunological dynamics, we formulate a mathematical model for immune-mediated interaction between influenza virus and S. pneumoniae in the lungs. We use the model to examine the pathogenic effect of inoculum size and timing of pneumococcal invasion relative to influenza infection, as well as the efficacy of antivirals in preventing severe pneumococcal disease. We find that our model is able to capture the key features of the interaction observed in animal experiments. The model predicts that introduction of pneumococcal bacteria during a 4-6 day window following influenza infection results in invasive pneumonia at significantly lower inoculum size than in hosts not infected with influenza. Furthermore, we find that antiviral treatment administered later than 4 days after influenza infection was not able to prevent invasive pneumococcal disease. This work provides a quantitative framework to study interactions between influenza and pneumococci and has the potential to accurately quantify the interactions. Such quantitative understanding can form a basis for effective clinical care, public health policies and pandemic preparedness.

Crossing the scale from within-host infection dynamics to between-host transmission fitness: a discussion of current assumptions and knowledge

“…The question of genetic and antigenic diversity, evolution and its relation to transmission has been addressed theoretically [36,44], but again experimental information is sparse [113]. The multi-genotype view also encompasses competition between unrelated pathogens, an area that has been explored somewhat in models [114] but for which data will be even harder to obtain.…”

Host‐pathogen kinetics during influenza infection and coinfection: insights from predictive modeling

“…It also begins to reveal the relationship between these rates and the strength needed to induce a change in the dynamics (eg, with drug therapy or coinfection). Further investigating how changing the rates affects outcome, for example, through sensitivity analysis, has generated predictions about the response to therapy or coinfection with other pathogens . Collectively, these types of analyses reveal aspects of influenza biology that are not immediately available from the experimental or clinical data alone.…”

Mathematical modeling of postcoinfection with influenza A virus and <em>Streptococcus pneumoniae</em>, with implications for pneumonia and COPD-risk assessment

“…The accuracy of the predictions obtained from mathematical modeling studies depends on the accuracy of the estimates for parameters governing the model dynamics. Good parameter estimates are needed to better understand and model the potential spread of influenza and SP coinfection 15,17. Therefore, interpretation of available data from experimental studies provides a platform to link mathematical models, such as infection dynamics, corresponding responses, and efficacy of different control measures for influenza and secondary bacterial coinfection.…”

“…Several studies have investigated the time course of susceptibility to SP infection after IAV infection and estimated that on average these individuals developed coinfection within 6.2 days (1.3–11.1 days) after IAV infection 5,9,13,15,17. This indicates that secondary SP infection may occur concurrently with or shortly after influenza infection 5.…”

“…They indicated a short-lived but strong interaction (~100-fold) of increased susceptibility to pneumococcal pneumonia postinfluenza infection. Meanwhile, they derived an immunomediated model quantifying virus–bacteria interaction and proposed advice on clinical management that antiviral treatment should be administered no later than 4 days after influenza infection 17. In addition, Smith et al15,18 established a virus–bacteria–host kinetic model to investigate interaction during coinfection based on various bacterial inoculum sizes (100 CFU versus 1,000 CFU D39) and virus strains (PR8 versus PR8-PB1-F2[1918]).…”