Abstract
Untold numbers of lives are lost every year to infectious diseases. Although most of the research of these diseases isolates a single infection at a time, experimental studies have analyzed how a host with multiple infections will have altered severity of the diseases. Some studies suggested that coinfection with rhinovirus (RV) and influenza virus (PR8) can reduce the severity of influenza, even at normally lethal doses. Mice given RV 2-14 days before influenza were completely protected against mortality and had reduced morbidity. Isolating the component of the immune response that causes this protection would be monumental in combating the flu and other diseases. Visualization and modeling of data can help researchers gain important insights and aid in explaining phenomena in the data. Multivariate mixed effects modeling is a powerful tool to predict multiple correlated response variables simultaneously while also accounting for variance between cross sectional and/or longitudinal random variables. Severeness of infection can be predicted using flow cytometry data with a multivariate mixed effects model. Regression modeling can then be analyzed in an attempt to identify the component of the immune system responsible for coinfection immunity. Flow cytometry was used to characterize immune cells in infected tissue to obtain the dataset. Although narrowing down the cause of coinfection immunity to a single cell or interaction has proved difficult, modeling with flow cytometry data as a predictor has proved extremely powerful in predictive capabilities which implies a correlation between these cells and the effect of coinfection exists.