My research interests include Mathematical Modeling, Quantitative Virology and Immunology with current focuses on Influenza virus, Respiratory Syncytial Virus, Parainfluenza virus and coinfections. Below are descriptions of the current projects I am working on and have previously completed.

Influenza virus infection

Seasonal and pandemic influenza A virus continues to be public health threat. However, we lack a detailed and quantitative understanding of the immune response kinetics to influenza virus infection. In this NIH funded Smith Lab project, we aim to develop an infection model combined with experimental data that investigates the innate and adaptive immune responses to influenza virus infection incorporating CD8 T cell and alveolar macrophages dynamics simultaneously.

Parainfluenza virus infection

Besides influenza virus, a number of other viruses can cause upper and lower respiratory tract infections, and may lead to severe complications, specially in infants and small children. One such important virus is the Human Parainfluenza Viruses (HPIVs). HPIVs are one of the most frequent viruses isolated in children under 5 years with acute respiratory tract infections that typically leads to pediatrics hospitalizations higher than the influenza virus burden. Yet no licensed vaccines or antiviral drugs are available against HPIVs. In this study, we used bioluminescence data from mice infected with either a high or low dose of one of two recombinant parainfluenza viruses, which exhibit either an attenuated or wild-type phenotype, to better understand the growth kinetics of HPIVs. Applying Nonlinear Mixed Effects Modeling approach, our primary aim was to quantify the phenotypic differences between the two strains and two initial viral inoculum doses, and determine the source of inter-individual heterogeneity among and within the given groups. To date, no modeling study has assessed the growth and decay dynamics of HPIVs in vivo, except our previous coinfection model that evaluated HPIV infection in vitro human airway epithelium.

Coinfections of respiratory viruses

Molecular diagnostic assays have revealed that approximately 43% of the patients hospitalized with influenza-like illness are coinfected with more than one virus from different viral families and have enhanced, reduced or unaffected disease severity compared to single viral infections. However, it is not clear how unrelated viruses interact within host and affect disease severity. To investigate the viral dynamics underlying coinfection within a host and hence, to describe mechanisms of interaction among viruses and host cells, we developed a series of mathematical models based on both stochastic and deterministic approaches. Specifically, we used our models to investigate coinfections of two viruses with Influenza, RSV, Rhinovirus, HPIV and Human Metapneumovirus. Our model predictions help elucidate the fundamental competition for resources that drives dynamics of respiratory coinfections, but there are many other factors that can change the competitive balance between the two viruses which are our future goals for investigation. For more details on the model development and results please see the publication section under the Coinfections of respiratory viruses.

Throughout this study, theory of dynamical systems, data fitting and parameter estimation, model selection theory were applied in order to analyze and parameterize each of the models with relevant infection data. In addition to these techniques, theory of birth-and-death processes, multi-type branching process, Gillespie algorithms were used to study the stochastic processes.