Diana Prieto is an Associate Professor of Industrial Engineering at Pontificia Universidad Católica de Valparaíso, and an Assistant Professor by courtesy at Johns Hopkins Carey Business School. She holds an M.A. in Statistics and a Ph.D. in Industrial Engineering from the University of South Florida. In her research, Professor Prieto analyzes public health systems where patient prioritization is needed to manage resource scarcity. Examples of these systems include influenza specimens waiting to be tested for virus confirmation, and patients waiting for healthcare and caregiving services. She has also served as a consultant in projects for education, transportation, and manufacturing.

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Grants

1. Principal Investigator: Sampling criteria for monitoring influenza emergencies under constrained testing capabilities. US National Science Foundation (NSF): $204,646
2. Principal Investigator: Planning the implementation of data-driven computational technologies to reduce waiting lists in a health delivery network for low income patients in Chile. Johns Hopkins Alliance for a Healthier World: $25,000

Publications

1. Soto-Ferrari, M., Carrasco-Pena, A. & Prieto, D. EpiForecaster: a novel deep learning ensemble optimization approach to combining forecasts for emerging epidemic outbreaks. Stochastic Environmental Research and Risk Assessment, 2025; 39, 675–695. 
   
2. O. Uthman, F. Lyngse, S. Anjorin, B. Hauer, S. Hakki, D. Martinez, Y. Ge, J. Jonnerby, C. Halle, G. Lin, A. Lalvani, J. Loss, K. Madon, L. Martinez, L. Meyer, K. Page, D. Prieto, A. Hayman,Y. Shen, J. Wurm, U. Buchholz, et al. Susceptibility and infectiousness of SARS-CoV-2 in children versus adults, by variant (wild-type, alpha, delta): A systematic review and meta-analysis of household contact studies. PLoS ONE, 2024; 19(9).
   
3. M. Soto, A. Carrasco, and D. Prieto. AGGFORCLUS: A hybrid methodology integrating forecasting with clustering to assess mitigation plans and contagion risk in pandemic outbreaks: the COVID-19 Case Study. Journal of Business Analytics, 2023; 6(3):217-242.
   
4. R. Paul, D. Han, E. Dedoncker and D. Prieto. Dynamic downscaling and daily nowcasting from influenza surveillance data. Statistics in Medicine, 2022; 41(21): 4159-4175.
   
5. D. Martinez, E. Klein, C. Parent, D. Prieto, B. Bigelow, R. Saxton, K. Page. Latino Household Transmission of SARS-CoV-2. Clinical Infectious Diseases, 2021.
6.  Y. Gu, R. VanEnk, R. Paul, S. Peters, E. Dedoncker, G. Stoltman, and D. Prieto. Accuracy of State-level Surveillance During Emerging Outbreaks of Respiratory Viruses: A Model-based Assessment. Medical Decision Making, 2021; 41(8):1004-1016. doi:10.1177/0272989X21102227
7. D. Martinez, H. Zhang, M. Bastias, F. Feijoo, J. Hinson, R. Martinez, J. Dunstan, S. Levin, and D. Prieto. Prolonged wait time is associated with increased mortality for Chilean waiting list patients with non-prioritized conditions. BMC Public Health, Volume 19, Number 233, 2019. 
8. 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.
9. M. Soto-Ferrari, D. 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
10. 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, 2016
11. 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.