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Here is the info for this week's meeting for those who are interested in joining. We are a friendly/funny group.
Join us online Friday, March 27th, 2:00 - 3:00 PM ET (Boston time) through:
https://wpi.zoom.us/j/389429908
See (far) below for call in details.
In this week's Collective Learning Meeting (CLM), Dr. Bob Eberlein (bob@iseesystems.com) will present:
"Understanding the Unknown: Trying to make COVID-19 discussion as transparent as possible"
Abstract: There is a great deal of fear and uncertainty associated with the COVID-19 pandemic. Some of the concern is certainly justified, but our ability to understand what is coming is very limited. Some of this is due to the nature of the disease itself. COVID-19 has a clear, but not clearly understood, period of asymptomatic transmission, and it is still not understood how many individuals can carry and pass the virus without ever showing symptoms. Further, with roughly 20% of infections being severe, the load on the health care system is, as have been evidenced in Italy, potentially overwhelming. This is, of course, exacerbated by the potential for hospital workers themselves to be overwhelmed by the disease as happened in Wuhan. Putting this together into a coherent framework with a model that transparently allows people to experiment with both policies and epidemiological assumption provides a lens into what might unfold and what can be done. The talk will focus on exploring that model and showing the ways in which it can inform our understanding of what is happening around us.
Biography: Bob Eberlein is Co-President at isee systems. He holds a PhD from MIT in Applied Economics and System Dynamics. He has been teaching, consulting, and developing software in support of System Dynamics for over 30 years. He has done modeling in a variety of areas including demography, chronic disease progression, and health care system responses.
Meeting ID: 389 429 908
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Meeting ID: 389 429 908
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Meeting ID: 389 429 908
If you are interested in presenting at our CLM, please contact Christine Tang (ctang@wpi.edu).
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Christine Tang
Worcester MA
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Original Message:
Sent: 03-21-2020 13:02
From: Christine Tang
Subject: On simulations of social distancing for COVID-19
Multiple folks from the System Dynamics (SD) community have built models. Tom Fiddaman also made a video
Tom Fiddaman's model/video
https://metasd.com/2020/03/community-coronavirus-model-bozeman/
Jeroen Struben's model
https://forio.com/app/jeroen_struben/corona-virus-covid19-seir-simulator/index.html#introduction.html
Tom Fiddaman's review of Struben's model
https://metasd.com/2020/03/interactive-coronavirus-models/
Bob Eberlein's model
https://exchange.iseesystems.com/public/isee/covid-19-simulator/index.html#page1
https://exchange.iseesystems.com/models/player/isee/covid-19-model
Bob Eberlein will be presenting his work this Friday, March 27th 2-3PM ET in a Worcester Polytechnic Institute (WPI) System Dynamics Collective Learning Meeting (CLM). I am waiting on details from him about the presentation. Please follow/join WPI SD for more info
https://twitter.com/WPISDclub
https://www.linkedin.com/groups/1916314/
Below is a link to sign up to be on the WPI System Dynamics Club mailing list:
http://eepurl.com/dzmA4j
Wish you and yours health and happiness!
Sincerely,
Christine Tang
WPI System Dynamics Social Media Manager
Interdisciplinary PhD Student in System Dynamics
------------------------------
Christine Tang
Worcester MA
Original Message:
Sent: 03-20-2020 11:45
From: Zahir Balaporia
Subject: On simulations of social distancing for COVID-19
I think this is a good System Dynamics based interactive model.
https://exchange.iseesystems.com/public/isee/covid-19-simulator
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Zahir Balaporia
ZahirBalaporia@fico.com
Original Message:
Sent: 03-19-2020 02:43
From: Rahul Saxena
Subject: On simulations of social distancing for COVID-19
They seem to contain simplifying assumptions that I would challenge:
1. Infection is modeled as binary (infected yes/no), ignores dosage effect, dosage probably drives virulence
2. Missing deaths, treating all the infected cases as the same (a proxy for fixed virulence instead of a distribution of virulence)
3. Permanent immunity after recovery, when immunity is usually variant-specific and therefore temporary against waves of variants
4. Ignores successive waves of infection
Running a better simulation with better-founded assumptions may change the decisions about social distancing. I would consider the null hypothesis to be a normally mingling population. The simulations would need to consider the base case of a variably-dosed mingling population where most infections are light doses with low virulence to assess the strategies that confer resilient immunity against waves of infection.
Is there a better simulation that you know of?
| | | These simulations show how to flatten the coronavirus growth curveThe early trickle of new coronavirus infections has turned into a steady current. By creating simple simulations... |
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