Bio

Research Area: Health Policy

I develop computational models to improve decision-making in health policy. Specifically, I utilize complex networks, machine learning, data analytics, and economic modeling in my methodology. Applications include emerging, zoonotic, neglected, and sexually transmitted communicable infectious diseases that pose a risk as a result of naturally occurring emergence or biological attack.

Honors & Awards

  • Charles and Katherine Lin Fellowship, Department of Management Science & Engineering, Stanford University (July 2019)
  • Lee B. Lusted Student Prize in Health Services, Outcomes, and Policy Research, Society for Medical Decision Making (October 2019)
  • Excellence in Undergraduate Research Award, Purdue Policy Research Institute (May 2017)

Education & Certifications

  • BS, Purdue University, Industrial Engineering

Contact

  • Academic
    malloyg@stanford.edu
    University - Student Department: Management Science and Engineering Position: Graduate

Additional Info

  • Mail Code: 6019

All Publications

  • Effectiveness of interventions to reduce COVID-19 transmission in a large urban jail: a model-based analysis. BMJ open Malloy, G. S., Puglisi, L., Brandeau, M. L., Harvey, T. D., Wang, E. A. 2021; 11 (2): e042898

    Abstract

    OBJECTIVES: We aim to estimate the impact of various mitigation strategies on COVID-19 transmission in a US jail beyond those offered in national guidelines.DESIGN: We developed a stochastic dynamic transmission model of COVID-19.SETTING: One anonymous large urban US jail.PARTICIPANTS: Several thousand staff and incarcerated individuals.INTERVENTIONS: There were four intervention phases during the outbreak: the start of the outbreak, depopulation of the jail, increased proportion of people in single cells and asymptomatic testing. These interventions were implemented incrementally and in concert with one another.PRIMARY AND SECONDARY OUTCOME MEASURES: The basic reproduction ratio, R 0 , in each phase, as estimated using the next generation method. The fraction of new cases, hospitalisations and deaths averted by these interventions (along with the standard measures of sanitisation, masking and social distancing interventions).RESULTS: For the first outbreak phase, the estimated R 0 was 8.44 (95% credible interval (CrI): 5.00 to 13.10), and for the subsequent phases, R 0,phase 2 =3.64 (95% CrI: 2.43 to 5.11), R 0,phase 3 =1.72 (95% CrI: 1.40 to 2.12) and R 0,phase 4 =0.58 (95% CrI: 0.43 to 0.75). In total, the jail's interventions prevented approximately 83% of projected cases, hospitalisations and deaths over 83 days.CONCLUSIONS: Depopulation, single celling and asymptomatic testing within jails can be effective strategies to mitigate COVID-19 transmission in addition to standard public health measures. Decision makers should prioritise reductions in the jail population, single celling and testing asymptomatic populations as additional measures to manage COVID-19 within correctional settings.

    View details for DOI 10.1136/bmjopen-2020-042898

    View details for PubMedID 33597139

  • COST-EFFECTIVENESS OF INTERVENTIONS TO PREVENT PLAGUE IN MADAGASCAR Malloy, G., Andrews, J., Brandeau, M. L., Goldhaber-Fiebert, J. D. SAGE PUBLICATIONS INC. 2020: E116–E117

    View details for Web of Science ID 000509275600104

  • PREDICTING THE EFFECTIVENESS OF INTERVENTIONS FOR INFECTIOUS DISEASE CONTROL: THE ROLE OF MODEL STRUCTURE Malloy, G., Goldhaber-Fiebert, J. D., Enns, E. A., Brandeau, M. L. SAGE PUBLICATIONS INC. 2020: E118

    View details for Web of Science ID 000509275600105

  • PREDICTING THE EFFECTIVENESS OF INTERVENTIONS FOR INFECTIOUS DISEASE CONTROL: THE ROLE OF MODEL STRUCTURE Malloy, G., Goldhaber-Fiebert, J. D., Enns, E. A., Brandeau, M. L. SAGE PUBLICATIONS INC. 2020: E377

    View details for Web of Science ID 000509275600314

  • Estimation of COVID-19 Basic Reproduction Ratio in a Large Urban Jail in the United States. Annals of epidemiology Puglisi, L. B., Malloy, G. S., Harvey, T. D., Brandeau, M. L., Wang, E. A. 2020

    View details for DOI 10.1016/j.annepidem.2020.09.002

    View details for PubMedID 32919033