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

All Publications


  • Predicting the Effectiveness of Endemic Infectious Disease Control Interventions: The Impact of Mass Action versus Network Model Structure. Medical decision making : an international journal of the Society for Medical Decision Making Malloy, G. S., Goldhaber-Fiebert, J. D., Enns, E. A., Brandeau, M. L. 2021: 272989X211006025

    Abstract

    BACKGROUND: Analyses of the effectiveness of infectious disease control interventions often rely on dynamic transmission models to simulate intervention effects. We aim to understand how the choice of network or compartmental model can influence estimates of intervention effectiveness in the short and long term for an endemic disease with susceptible and infected states in which infection, once contracted, is lifelong.METHODS: We consider 4 disease models with different permutations of socially connected network versus unstructured contact (mass-action mixing) model and heterogeneous versus homogeneous disease risk. The models have susceptible and infected populations calibrated to the same long-term equilibrium disease prevalence. We consider a simple intervention with varying levels of coverage and efficacy that reduces transmission probabilities. We measure the rate of prevalence decline over the first 365 d after the intervention, long-term equilibrium prevalence, and long-term effective reproduction ratio at equilibrium.RESULTS: Prevalence declined up to 10% faster in homogeneous risk models than heterogeneous risk models. When the disease was not eradicated, the long-term equilibrium disease prevalence was higher in mass-action mixing models than in network models by 40% or more. This difference in long-term equilibrium prevalence between network versus mass-action mixing models was greater than that of heterogeneous versus homogeneous risk models (less than 30%); network models tended to have higher effective reproduction ratios than mass-action mixing models for given combinations of intervention coverage and efficacy.CONCLUSIONS: For interventions with high efficacy and coverage, mass-action mixing models could provide a sufficient estimate of effectiveness, whereas for interventions with low efficacy and coverage, or interventions in which outcomes are measured over short time horizons, predictions from network and mass-action models diverge, highlighting the importance of sensitivity analyses on model structure.HIGHLIGHTS: We calibrate 4 models-socially connected network versus unstructured contact (mass-action mixing) model and heterogeneous versus homogeneous disease risk-to 10% preintervention disease prevalence.We measure the short- and long-term intervention effectiveness of all models using the rate of prevalence decline, long-term equilibrium disease prevalence, and effective reproduction ratio.Generally, in the short term, prevalence declined faster in the homogeneous risk models than in the heterogeneous risk models.Generally, in the long term, equilibrium disease prevalence was higher in the mass-action mixing models than in the network models, and the effective reproduction ratio was higher in network models than in the mass-action mixing models.

    View details for DOI 10.1177/0272989X211006025

    View details for PubMedID 33899563

  • 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
  • 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
  • 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
  • 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