Project Director: Bert Baumgaertner

Project Team: Joseph DeAguero

How pathogens spread through a population can be complicated by a number of factors. One of them is pathogen interaction during co-infection. Here infection by one pathogen can change host susceptibility to a second, or being co-infected can change a host’s infectivity compared to a singly infected host. A second factor is that infection can alter behavior both for biological reasons—for example, when sickness makes a host-less active—and, in humans especially, for social reasons—when, for example, sick people self-isolate. These behavioral responses, in turn, change the patterns of interactions that drive transmission dynamics. A third closely related factor is that patterns of spatial aggregation around environmental features like food and water, or for humans, institutions like schools and home, can create an intricate network of interactions that strongly affect how infections spread. This project will focus on how the transmission dynamics of the population are affected by the interactions of co-infecting pathogens, the environment, and social factors that influence behavior. The main approach to this research makes use of agent-based modeling, a computational framework comprised of individuals, an environment, and rules for how individuals interact with the environment and each other.

Project Director: Christine Parent

Project Team: Tanya Miura, Jake Ferguson, Andrea Gonzalez-Gonzalez, Jagdish Patel, JT Van Leuven, Ashley DeAguero

With the increasing global mobility of human populations, individuals are being exposed to an increasing diversity of viruses. Many approaches are used to study viral coinfections at different organizational levels, ranging from very detailed molecular studies of specific viruses to epidemiological studies at the population level, but very few systems offer the possibility to study the multi-level dynamics of viral coinfection, from molecules to communities.

Our research is building on the host-virus system of Drosophila and associated viruses to leverage the advantages of studying large host and viral populations, powerful genetic tools, and ready access to sequencing technology. In collaboration with the CMCI, we will focus on questions typically not easily addressed in other experimental systems. Specifically, we will establish a tractable invertebrate model of viral infection and co-infection, and develop mathematical models to understand how viruses interact with each other and their host to ultimately affect the host pathology and population dynamics.

Project Director: Tanya Miura

Project Team: Craig Miller, Onesmo Balemba, Jake Ferguson, Jagdish Patel, JT Van Leuven, Andres Gonzalez

Viral infections in the lower respiratory tract cause severe disease and are responsible for a majority of pediatric hospitalizations, approximately 20% of which are infected by more than one viral pathogen. Clinical data indicate that disease severity can be enhanced, reduced or be unaffected by viral co-infection. However, it is not clear how unrelated viruses interact within the context of a complex host to determine disease severity. The long-term goal of this research is to uncover the causal relationships between co-infection and the resulting respiratory disease severity. Variables that will potentially predict disease severity include viral strains, doses, timing, viral competition, genetic variation in the host, and the immune response. The proposed research will develop a murine model with cellular and organismal components and a human in vitro model to test the central hypothesis that respiratory viral co-infections change disease severity both by direct viral interactions and by modulating host responses. Statistical and stochastic modeling will reveal the complex interactions between heterologous viruses within their shared target cells and host organisms.