ADS & AMDS Webinar | Modelling Economic & Health Effects of COVID-19 Policies

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 𝟰: 𝗠𝗼𝗱𝗲𝗹𝗹𝗶𝗻𝗴 𝗲𝗰𝗼𝗻𝗼𝗺𝗶𝗰 𝗮𝗻𝗱 𝗵𝗲𝗮𝗹𝘁𝗵 𝗲𝗳𝗳𝗲𝗰𝘁𝘀 𝗼𝗳 𝗖𝗢𝗩𝗜𝗗-𝟭𝟵 𝗽𝗼𝗹𝗶𝗰𝘆 𝗺𝗲𝗮𝘀𝘂𝗿𝗲𝘀

𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗲
16:00 Welcome & Introduction
16:05 Talk by Kent Smetters and Alex Arnon
16:40 Q&A
17:00 End!

This presentation will be about a model that has been developed at Wharton to simulate the economic and health effects of policy decisions. For a detailed model description: https://budgetmodel.wharton.upenn.edu/issues/2020/5/1/coronavirus-reopening-simulator

𝗠𝗼𝗱𝗲𝗿𝗮𝘁𝗼𝗿𝘀
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