Dealing with COVID-19 in Theory and Practice

Organizer

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A W L

Description

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The extraordinary impact of COVID-19 requires equally extraordinary measures, which are the focus of this workshop. Four major themes will be represented: Public Health Challenges, The Role of Data Science, Measuring and Managing Economic Impact, and The Path Forward. One unique aspect of this workshop is the breadth of participants, bringing together stakeholders from the most relevant communities, including biomedical experts, epidemiologists, public health officials, economists, business professionals, and bioethicists. One of the key objectives of this workshop is to encourage collaboration across disciplines that have practical impact in the near term.

Schedule

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Video & Resources

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Session I: Public Health

Moderator: Rena Conti (Boston University)
Date: October 29, 2020

Presenters

• Sarah E. Cobey (University of Chicago)
  Reconstructing a pandemic from very messy observations
  (Starts at 00:07:00)
• Nigel Goldenfeld (UIUC) and Sergei Maslov (UIUC)
  Models of COVID-19 spread and mitigation on a university campus and beyond (

  We present modeling of the COVID-19 epidemic in Illinois, USA, capturing the implementation of a Stay-at-Home order and scenarios for its eventual release. We use a non-Markovian age-of-infection model that is capable of handling long and variable time delays without changing its model topology. Bayesian estimation of model parameters is carried out using Markov Chain Monte Carlo (MCMC) methods. This framework allows us to treat all available input information, including both the previously published parameters of the epidemic and available local data, in a uniform manner. To accurately model deaths as well as demand on the healthcare system, we calibrate our predictions to total and in-hospital deaths as well as hospital and ICU bed occupancy by COVID-19 patients. We apply this model not only to the state as a whole but also its sub-regions in order to account for the wide disparities in population size and density.

  We discuss the role of heterogeneity on the evolution of COVID-19, highlighting the difference between short-term overdispersion and long term persistent heterogeneity. Like other models of heterogeneity, we show that the herd immunity threshold is suppressed, but unlike other models, we show that the resulting saturation is fragile or transient, and can change over time due to varying levels of individual social activity.

  Lastly, we present an overview of efforts to reopen our university by using high-throughput surveillance testing of the student and staff population.

  Abstract)
  (Starts at 01:10:00)
• Rupam Bhattacharyya, Bhramar Mukherjee, and Maxwell Salvatore (University of Michigan) and Debashree Ray (Johns Hopkins)
  Predictions, role of interventions and implications of a national lockdown on the COVID-19 outbreak in India (

  India, the world’s largest democracy with 1.34 billion people, has undergone five phases of lockdown from March 25-June 30. The virus curve appears to have turned the corner only recently. In this group presentation we will discuss an extended SIR model for predicting case-counts in India. We will evaluate the national lockdown as a non-pharmaceutical intervention through various public health relevant metrics, and illustrate that regional variation makes the concept of a national peak nebulous. We will briefly touch upon seroprevalence surveys and what they tell us, and describe recent methodological innovations regarding incorporating selective and imperfect viral testing in an extended SEIR model for COVID-19. Final, we end by talking about going from numerical analysis to strategic visioning.

  Abstract)
  Harvard Data Science Review Paper, COV-IND-19 Study Group
  (Starts at 02:15:50)

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Session II: Data Science I

Moderator: Xiao-Li Meng (Harvard University)
Date: October 29, 2020

Presenters

• Mihaela van der Schaar (Cambridge University)
  Interpretable AutoML: powering the machine learning revolution in healthcare in the era of Covid-19 and beyond (

  Medicine stands apart from other areas where AI can be applied. While we have seen advances in other fields with lots of data, it is not the volume of data that makes medicine so hard, it is the challenges arising from extracting actionable information from the complexity of the data. It is these challenges that make medicine the most exciting area for anyone who is really interested in the frontiers of machine learning – giving us real-world problems where the solutions are ones that are societally important and which potentially impact on us all. Think Covid 19!

  In this talk I will show how AI and machine learning are transforming medicine and how medicine is driving new advances in machine learning, including new methodologies in automated machine learning, interpretable and explainable machine learning, dynamic forecasting, and causal inference. I will also discuss our experiences in implementing such AI solutions nationally, in the UK, in order to fight the current Covid 19 pandemic as well as how they can be adapted for international use.

  Abstract)
  Van der Schaar Lab COVID-19 site
  (Starts at 00:01:45)
• Michael Jordan (Berkeley)
  On Identifying and Mitigating Bias in the Estimation of the Covid-19 Case Fatality Rate
  Harvard Data Science Review Paper
  (Starts at 01:02:30)

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Session III: Data Science II

Moderator: Sean Khozin (Janssen R&D, Johnson & Johnson)
Date: October 29, 2020

Presenters

• Xiao-Li Meng (Harvard University)
  From COVID-19 Testing to Election Prediction: How Small Are Our Big Data? (

  The term “Big Data” emphasizes data quantity, not quality. What will be the effective sample size when we take into account the deterioration of data quality because of, for example, the selection bias in COVID-19 testing or the non-response bias in 2016 US Election polling results? This talk provides an answer to such questions, based on the concept of data defect index (ddi) developed in Meng (2018) Statistical paradises and paradoxes in big data (I): Law of large populations, big data paradox, and the 2016 US presidential election. Annals of Applied Statistics, 685-726. It will also discuss briefly the application of ddi for 2020 US Election, as reported in Isakov and Kuriwaki (2020) Towards Principled Unskewing: Viewing 2020 Election Polls Through a Corrective Lens from 2016. Harvard Data Science Review.

  Abstract)
  Meng (2018) Statistical paradises and paradoxes in big data (I): Law of large populations, big data paradox, and the 2016 US presidential election. Annals of Applied Statistics, 685-726
  Isakov and Kuriwaki (2020) Towards Principled Unskewing: Viewing 2020 Election Polls Through a Corrective Lens from 2016. Harvard Data Science Review
  (Starts at 00:04:30)
• Daniel Weitzner (MIT)
  Tracking COVID-19 with Smartphones: Awash in Data, Seeking Insight, Earning Trust
  (Starts at 00:49:30)

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Session IV: Economic Impact

Moderator: Andrew Lo (MIT)
Date: October 30, 2020

Presenters

• Andrew Atkeson (UCLA)
  What does it mean to say that “The path of the economy will depend significantly on the course of the virus.”? (

  Policymakers at the Federal Reserve have added the sentence “The path of the economy will depend significantly on the course of the virus” to their official statement after each meeting of the Federal Open Market Committee. What do they mean by this statement? Don’t we as a society face a choice between disease mitigation and the economy? Couldn’t we have a faster economic recovery if we were willing to tolerate more deaths from COVID-19? In this talk I review recent evidence on the experience of a large number of countries and states of the United States with COVID deaths and economic performance. I interpret that data using a simple SIR model of the epidemic that incorporates the response of private economic behavior to COVID deaths. I use that model to show how “luck” and “policy” have both played an important role in shaping the diversity of outcomes observed across regions over the past six months. I then use that model to show how epi-macro models of this kind deliver forecasts of a long and slow economic recovery from this pandemic.

  Abstract)
  Slides
  (Starts at 00:02:30)
• Andrew Metrick (Yale University)
  The Economic Policy Response to Covid-19: Lessons Learned (So Far)
  (Starts at 01:05:30)
• Emil Verner (MIT)
  Is There a Tradeoff between Public Health Interventions and the Economy in a Pandemic? (

  The COVID-19 pandemic has sparked urgent questions about the economic effects of a pandemic and the associated policy responses. A crucial question is whether and to what extent there is a tradeoff between non-pharmaceutical interventions (NPIs) to reduce mortality and economic activity. This presentation presents evidence from recent research on the economic impact of non-pharmaceutical interventions from both the 1918 Influenza Pandemic and the current COVID-19 pandemic. A growing body of evidence suggests that NPIs that reduce mortality are only responsible for a small fraction of the decline in economic activity. Instead, fear and uncertainty from the virus itself are responsible for the majority of the economic damage.

  Abstract)
  (Starts at 02:07:30)

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Session V: The Path Forward

Moderator: Andrew Lo (MIT)
Date: October 30, 2020

Presenters

• Arthur Caplan (NYU)
  Covid-19: The Ethics of EUAs and Vaccine Development (

  Should early approval be given for the first vaccine candidates to prevent harm from covid? Who will decide this issue—companies, DSMBs, states, FDA or all of these. If early access is desirable should it be under an EUA or an EA and what is the difference? And if there is early access who goes first? Will early access jeopardize ongoing and soon to start phase 3 trials?

  Abstract)
  Slides
  (Starts at 00:01:00)
• Monique K. Mansoura (MITRE Corporation)
  An Industrial Base for Global Health Security – A 21st Century ImperativeVaccine ethics challenges
  (Starts at 00:18:30)
  
• Jim Robinson (CEPI)
  Incentivizing Late-Stage Vaccine Development and Manufacturing for Epidemic and Pandemic Response
  Slides
  (Starts at 00:52:00)
• Rena Conti (Boston University)
  Generic drug repurposing for National Security and Health Improvement – the COVID-19 Pandemic and Beyond (

  COVID19 is an all-out attack on our lives and our economy. We need a proportionate all-in response. Generic drug repurposing should be part of that response.

  Authors: Rena Conti, Associate Professor, Department of Markets, Public Policy and Law, Questrom School of Business Boston University
  Susan Athey, Economics of Technology Professor, Stanford Graduate School of Business
  Richard Frank, Professor of Health Economics, Harvard Medical School
  Jonathan Gruber, Ford Professor of Economics, MIT

  Abstract)
  (Starts at 01:33:00)