Pedro Nascimento de Lima

RAND Corporation

Argonne National Laboratory

About me

I am an assistant policy researcher at RAND Corporation, a Ph.D. candidate at Pardee RAND Graduate School, and a visiting graduate student at Argonne National Laboratory.

My recent research uses modeling to inform health policy. Here you will find links to my recent publications, tools and software I helped to build and more.

If you find any of this interesting and want to connect, follow me on twitter or get in touch!

Publications

My recent research leverages modeling and Decision Under Deep Uncertainty methods to inform pressing health policy decision-analytic problems.

Too Good to Be True? Evaluation of Colonoscopy Sensitivity Assumptions Used in Policy Models

This paper examines the validity of the CRC-SPIN model and colonoscopy sensitivity assumptions.

Carolyn M. Rutter, Pedro Nascimento de Lima, Jeffrey K. Lee, Jonathan Ozik

Source Document

Too Good to Be True? Evaluation of Colonoscopy Sensitivity Assumptions Used in Policy Models

Should We Mitigate or Suppress the Next Pandemic? Time-Horizons and Costs Shape Optimal Social Distancing Strategies

This paper uses a mathematical optimization procedure to find optimal social distancing strategies under a wide range of scenarios.

Sarah A Nowak, Pedro Nascimento de Lima, Raffaele Vardavas

Source Document DOI

Should We Mitigate or Suppress the Next Pandemic? Time-Horizons and Costs Shape Optimal Social Distancing Strategies

Reopening California: Seeking robust, non-Dominated COVID-19 exit strategies

This paper uses the Robust Decision Making approach to stress-test California’s COVID-19 reopening strategy considering a range of uncertainties.

Pedro Nascimento de Lima, Robert Lempert, Raffaele Vardavas, Lawrence Baker, Jeanne Ringel, Carolyn M. Rutter, Jonathan Ozik, Nicholson Collier

Code Source Document

Reopening California: Seeking robust, non-Dominated COVID-19 exit strategies

Modeling Infectious Behaviors: The Need to Account for Behavioral Adaptation in COVID-19 Models

This essay discusses the need to account for behavioral adaptation in COVID-19 models.

Raffaele Vardavas, Pedro Nascimento de Lima, Paul K. Davis, Andrew M. Parker, Lawrence Baker

Source Document

Modeling Infectious Behaviors: The Need to Account for Behavioral Adaptation in COVID-19 Models

Reopening Under Uncertainty: Stress-Testing California's COVID-19 Exit Strategy

We used the Robust Decision Making approach to stress-test California’s COVID-19 reopening strategy. This Perspective presents lessons learned from these experiments and outlines four characteristics of the best reopening strategies.

Pedro Nascimento de Lima, Raffaele Vardavas, Lawrence Baker, Jeanne Ringel, Robert Lempert, Carolyn M. Rutter, Jonathan Ozik

Source Document DOI

Reopening Under Uncertainty: Stress-Testing California's COVID-19 Exit Strategy

Will the United States Declare Freedom from COVID-19 Too Soon?

We analyzed what could happen with COVID-19 deaths in the United States if restrictions all go away on July 4. Fully reopening the economy before Biden’s vaccination target was met doubled the average number of COVID-19 deaths between Independence Day and the end of the year.

Lawrence Baker, Pedro Nascimento de Lima, Jeanne Ringel, Raffaele Vardavas

Source Document

Will the United States Declare Freedom from COVID-19 Too Soon?

Could Periodic Nonpharmaceutical Intervention Strategies Produce Better COVID-19 Health and Economic Outcomes?

This paper documents our COVID-19 transmission model and explores the performance of periodic NPI strategies.

Raffaele Vardavas, Pedro Nascimento de Lima, Lawrence Baker

Source Document

Could Periodic Nonpharmaceutical Intervention Strategies Produce Better COVID-19 Health and Economic Outcomes?

The Health and Economic Impacts of Nonpharmaceutical Interventions to Address COVID-19 - A Decision Support Tool for State and Local Policymakers

This model and tool helps local decision makers understand the impacts of Nonpharmaceutical interventions to tackle COVID-19.

Raffaele Vardavas, Aaron Strong, Jennifer Bouey, Jonathan William Welburn, Pedro Nascimento de Lima, Lawrence Baker, Keren Zhu, Michelle Priest, Lynn Hu, Jeanne S. Ringel

Source Document