Josh Goldstein is a Postdoctoral Associate at SDAL. His research focuses on spatial modeling of infectious disease , Markov chain Monte Carlo methods, and generating synthetic populations. He and received his PhD in statistics at Penn State University in 2015.
His thesis research focusus on developing methods for modeling infectious diseases. The classes of models include compartmental SIR models with a stochastic observation process, spatial gradient models building on a Gaussian process framework, and Markov spatial point process models. The scale of the dynamics he studies vary from the cellular level (respiratory syncytial virus infections) to local populations in Africa (rotavirus infections) to county-level data in the U.S. (epidemics and invasive species).
- Goldstein J, Haran M, Simeonov I, Fricks J, Chiaromonte F. An attraction-repulsion point process model for respiratory syncytial virus infections. Biometrics. 2015;71(2):376-385. https://doi.org/10.1111/biom.12267.
- Pires B, Goldstein J, Higdon D, Korkmaz G, Keller S, Shipp S, Hamall K, Koehler A, Reese S, Sabin P, Ba S. A Bayesian Simulation Approach for Supply Chain Synchronization. In: 2016 WINTER SIMULATION CONFERENCE (WSC). IEEE; 2016:3698-3699. https://doi.org/10.1109/WSC.2016.7822406.
- Park J, Goldstein J, Haran M, Ferrari M. An Ensemble Approach to Predicting the Impact of Vaccination on Rotavirus Disease in Niger. arXiv. 2017. http://arxiv.org/abs/1705.02423v1.
- ThePennsylvania State University, Statistics, Ph.D., 2015
- Lafayette College, Mathematics, Physics, B.S., 2007