title: An attraction-repulsion point process model for respiratory syncytial virus infections.
abstract: How is the progression of a virus influenced by properties intrinsic to individual cells? We address this question by studying the susceptibility of cells infected with two strains of the human respiratory syncytial virus (RSV-A and RSV-B) in an in vitro experiment. Spatial patterns of infected cells give us insight into how local conditions influence susceptibility to the virus. We observe a complicated attraction and repulsion behavior, a tendency for infected cells to lump together or remain apart. We develop a new spatial point process model to describe this behavior. Inference on spatial point processes is difficult because the likelihood functions of these models contain intractable normalizing constants; we adapt an MCMC algorithm called double Metropolis-Hastings to overcome this computational challenge. Our methods are computationally efficient even for large point patterns consisting of over 10,000 points. We illustrate the application of our model and inferential approach to simulated data examples and fit our model to various RSV experiments. Because our model parameters are easy to interpret, we are able to draw meaningful scientific conclusions from the fitted models.
authors: Department of Statistics, The Pennsylvania State University, State College, Pennsylvania, U.S.A.
authors: Joshua
authors: J
authors: Goldstein
authors: Department of Statistics, The Pennsylvania State University, State College, Pennsylvania, U.S.A.
authors: Murali
authors: M
authors: Haran
authors: Department of Statistics, The Pennsylvania State University, State College, Pennsylvania, U.S.A.
authors: Ivan
authors: I
authors: Simeonov
authors: Department of Statistics, The Pennsylvania State University, State College, Pennsylvania, U.S.A.
authors: John
authors: J
authors: Fricks
authors: Department of Statistics, The Pennsylvania State University, State College, Pennsylvania, U.S.A.
authors: Francesca
authors: F
authors: Chiaromonte
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001699:Biometry; D002478:Cells, Cultured; D003198:Computer Simulation; D004198:Disease Susceptibility; D006801:Humans; D008390:Markov Chains; D008954:Models, Biological; D015233:Models, Statistical; D009010:Monte Carlo Method; D018357:Respiratory Syncytial Virus Infections / Q000209:etiology* / Q000821:virology; D018113:Respiratory Syncytial Virus, Human / Q000472:pathogenicity*
keywords: Intractable normalizing constant; Markov chain Monte Carlo; Spatial point process; Virus infections