Hidden pathogen-pathogen interactions in a multi-pathogen ensemble: a physics approach

Every year, respiratory viruses have the ability to cause epidemics by infecting the human respiratory tract. In specific cases, infection with one leads to partial or total immunity to another. An immediate result is the emergence of interspecific pathogen interactions: epidemics caused by different pathogens are interdependent. To study this complex dynamics, we can adopt dynamical equations similar to those adopted in statistical physics of ecology systems. In order to model the effect of cross-immunity between pathogens, we construct a pathogen-view multi-pathogen model, which focuses on the health status of the host with respect to a single pathogen at a time, rather than the complex history of exposure to all pathogens. We use this model to analyze how an ensemble of hidden (unobserved) pathogens affects the dynamics of two focal interacting pathogens and biases the estimation of the cross-immunity between them from the epidemic profile. We perform simulations using Bayesian Markov chain Monte Carlo optimization techniques and taking into account real-world conditions. The results of this work can be extended to study real epidemiological scenarios where the co-circulation of pathogens becomes important in public health interventions.