Diana Prieto is an Assistant Professor of Practice at the Johns Hopkins Carey School Business. She holds an MA in Statistics and a PhD in Industrial Engineering from the University of South Florida. In her research, she analyzes complex systems in public health where customer prioritization is needed to manage resource scarcity. Examples of those systems include: 1) Influenza speicmens waiting to be tested for virus confirmation, and 2) patients waiting for healthcare services. She has also served as a consultant in projects for education, transportation, and manufacturing.
- Ph. D, Industrial Engineering, University of South Florida
- MA, Statistics, University of South Florida
- MS, Industrial Engineering, Universidad Del Norte
- BS, Industrial Engineering, Universidad Del Norte
- D. Prieto, M. Soto, R. Tija, L. Peña, L. Burke, L. Miller, K. Berndt, B. Hill, J. Haghsenas, E. Maltz, M. Atwood, and E. Norman. Literature review of data-based models for identification of factors associated with racial disparities in breast cancer mortality. Health Systems, 2018. DOI: 10.1080/20476965.2018.1440925.
- M. Soto, D. Original research Prieto, and G. Munene. A Bayesian network and heuristic approach for systematic characterization of radiotherapy receipt after breast-conservation surgery. BMC Medical Informatics and Decision Making, Volume 17, Number 93, 2017.
- D. Prieto and T. K. Das. An operational epidemiological model for calibrating agent-based simulations of pandemic influenza outbreaks. Health Care Management Science, Volume 19, Issue 1, 2016.
- D. Prieto, A. Kumar, C. Kothari, and C Dickson. Systematic identification of coordination gaps in pediatric care. Frontiers in Public Health Services and Systems Research, 2016; 5(4):12-20.
- E. Meisheri, D. Prieto, P. Holvenstot, and R. VanEnk. Preliminary evaluation of the disease surveillance system during influenza outbreaks of pandemic scale. Frontiers in Public Health Services and Systems Research, 2015; 4(3):3744.
- D. Prieto, T. K. Das, A. Savachkin, A. Uribe, R. Izurieta, and S. Malavade. A systematic review to identify areas of enhancements of pandemic simulation models for operational use at provincial and local levels. BMC Public Health, Volume 12, Number 251, 2012.
- A. Uribe, A. Savachkin, T. K. Das, A. Santana, and D. Prieto. A predictive decision aid methodology for dynamic mitigation of influenza pandemics. Special issue on optimization in disaster relief, OR Spectrum (6 May 2011), pp. 1-36.
- Data Analytics
- Business Analytics
- Statistical Analysis
Honors and distinctions
- Johns Hopkins Alliance for a Healthier World Planning Award, Spring 2018.
- National Science Foundation Award, 2015 (through 2018)