• From Data to Impact with Dr. Maya Petersen

  • May 29 2024
  • Durée: 40 min
  • Podcast

Page de couverture de From Data to Impact with Dr. Maya Petersen

From Data to Impact with Dr. Maya Petersen

  • Résumé

  • June 18th is “Maya Petersen” day in San Francisco, in honor of her work building disease models that guided the region through the early days of COVID and saved countless lives.

    With projects spanning from developing HIV prevention strategies in East Africa to shaping new Medicaid models in California, the UC Berkeley epidemiologist is building a future where local public health leaders have the tools and data to ask and answer complex policy decisions in real time. Now that’s a world I want to live in.

    We discuss:

    • How much better our pandemic response would have been if Public Health had access to integrated and linked data
    • Her work to bring sophisticated data tools to the point of decision in East Africa
    • How California is building population management infrastructure

    San Francisco’s Director of Health, Grant Colfax, taught her an important lesson about showing up and helping:

    “I remember… saying, ‘You know what? You really need to find somebody who's an expert in this, I'm not an expert in this.’ And he said, ‘Okay, Maya, but if you're gonna find me someone it needs to be in the next 24 hours, because I need help.’ And it was just a reminder that, you know, you're not always going to be an expert, sometimes you just need to show up, do your best… be clear about your uncertainty and communicate well, and that can be… a big service”

    Relevant Links

    Local Epidemic Modeling for the San Francisco Department of Public Health

    San Francisco’s COVID strategy

    Multi-sectorial Approach to HIV in East Africa

    Maya Petersen Day in San Francisco

    Maya’s UC Berkeley page

    About Our Guest

    Dr. Maya L. Petersen is Professor of Biostatistics and Epidemiology at the University of California, Berkeley. Dr. Petersen’s methodological research focuses on the development and application of novel causal inference methods to problems in health, with an emphasis on longitudinal data and adaptive treatment strategies (dynamic regimes), machine learning methods, adaptive designs, and study design and analytic strategies for cluster randomized trials. She is a Founding Editor of the Journal of Causal Inference and serves on the editorial board of Epidemiology. Her applied work focuses on developing and evaluating improved HIV prevention and care strategies. She currently serves as co-PI (with Dr. Diane Havlir and Dr. Moses Kamya) for the Sustainable East Africa Research in Community Health consortium, and as co-PI (with Dr. Elvin Geng) for the ADAPT-R study (a sequential multiple assignment randomized trial of behavioral interventions to optimize retention in HIV care).

    Source: https://publichealth.berkeley.edu/people/maya-petersen

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