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Modelling Economic & Health Effects of COVID-19 Policies

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Amsterdam Data Science (ADS) and Amsterdam Medical Data Science (AMDS) are co-hosting a new lecture series in collaboration with Elsevier and Google to explore The Power and Weakness of Data and Modelling in COVID-19.
Aims:
- Showcase the power and limitations of data centred approaches
- Jointly understand and learn from the different COVID approaches and views
- Shape the time for Data Science research/education after the lock-down
Lecture 3: Modelling Economic and Health Effects of COVID-19 Policy Measures
4:45 Presentation by Kent Smetters & Alex Arnon
30:20 Q&A
Moderator:
Mark Siebert (Elsevier)
Speakers:
Kent Smetters is the Boettner Chair Professor at the University of Pennsylvania’s Wharton School and a Faculty Research Fellow at the National Bureau of Economic Research.
Alex Arnon is Senior Analyst at the Penn-Wharton Budget Model
Title: Simulating Business Re-openings on Health and Economic Variables
Abstract: Using a wide range of geocoded daily data, the Penn-Wharton Budget Model (PWBM) coronavirus simulator is an integrated economics-epidemiological model that jointly projects health variables (symptomatic infections, asymptomatic infections, cases, and deaths) and economic variables (GDP and jobs) at the U.S. national, state, and (in many cases) county levels.
Principal component analysis is combined with diff-in-diff analysis to extract signals while separating causation from correlation. Standard epidemiological-only models are adaptive in approach, thereby requiring myopic “hammer and dance” policy making with naïve projections. In contrast, the PWBM model allows for prospective projections, as the viral replication factor (R) is jointly estimated with economic variables, differentiated at the state and (often) county level.
We show that the relationship between social distance and R has diminished substantially over the past couple months. Personal behavioral choices (e.g., wearing masks, outdoor versus indoor gatherings, etc.), rather than government policy, are now the biggest drivers of health variables.
Date: Wednesday 24 June 2020
Time: 16:00-17:00
Aims:
- Showcase the power and limitations of data centred approaches
- Jointly understand and learn from the different COVID approaches and views
- Shape the time for Data Science research/education after the lock-down
Lecture 3: Modelling Economic and Health Effects of COVID-19 Policy Measures
4:45 Presentation by Kent Smetters & Alex Arnon
30:20 Q&A
Moderator:
Mark Siebert (Elsevier)
Speakers:
Kent Smetters is the Boettner Chair Professor at the University of Pennsylvania’s Wharton School and a Faculty Research Fellow at the National Bureau of Economic Research.
Alex Arnon is Senior Analyst at the Penn-Wharton Budget Model
Title: Simulating Business Re-openings on Health and Economic Variables
Abstract: Using a wide range of geocoded daily data, the Penn-Wharton Budget Model (PWBM) coronavirus simulator is an integrated economics-epidemiological model that jointly projects health variables (symptomatic infections, asymptomatic infections, cases, and deaths) and economic variables (GDP and jobs) at the U.S. national, state, and (in many cases) county levels.
Principal component analysis is combined with diff-in-diff analysis to extract signals while separating causation from correlation. Standard epidemiological-only models are adaptive in approach, thereby requiring myopic “hammer and dance” policy making with naïve projections. In contrast, the PWBM model allows for prospective projections, as the viral replication factor (R) is jointly estimated with economic variables, differentiated at the state and (often) county level.
We show that the relationship between social distance and R has diminished substantially over the past couple months. Personal behavioral choices (e.g., wearing masks, outdoor versus indoor gatherings, etc.), rather than government policy, are now the biggest drivers of health variables.
Date: Wednesday 24 June 2020
Time: 16:00-17:00