Viral coinfections of the respiratory tract

Abstract

Molecular diagnostic techniques have revealed that approximately 43% of the patients hospitalized with influenza-like illness are infected by more than one viral pathogen, sometimes leading to long-lasting infections. It is not clear how the heterologous viruses interact within the respiratory tract of the infected host to lengthen the duration of what are usually short, self-limiting infections. To investigate chronic coexistence of two viruses, we extend the basic coinfection model based on ordinary differential equations (ODEs) from our previous work to allow for susceptible cells in the respiratory tract to regenerate, and single cells to be infected simultaneously with two different respiratory viruses (superinfection) to investigate the possibility of chronic coinfections. To assess the full behavioral dynamics of coinfection, mathematical analysis along with numerical simulation is performed considering superinfection with and without cell regeneration in the model.^We use respiratory syncytial virus and influenza A virus coinfection as an example to explore model outcomes. We find a possible mechanism for chronic coinfection in the inclusion of both superinfection and cellular regeneration in the model. Our model indicates that chronic coinfection is maintained by superinfected cells since this gives slow-growing infections a chance to infect cells and continue replicating. These findings provide insight into the possible mechanisms behind long-lasting viral coinfections. We also extend our results to stochastic simulations to more accurately model events in the early stages of infection. Stochastic extinction probabilities for each viruses are calculated analytically and are verified by stochastic simulations.^Analyses of the stochastic model have shown that even if the two viruses are given the same initial growth rates, one virus can have a higher probability of extinction than the other, resulting in a different pattern of coinfection dynamics than the deterministic predictions.