Modelling

COVID-19

Achieving Collective Intelligence Through Ensembles

Scott E Page & MO 410 Students

UM-Ross




As contagion of sickness makes sickness, contagion of trust can make trust. -Marianne Moore


Purpose of the Site

This site provides an overview of how models are being used by policy makers and experts during the coronavirus pandemic. The site is a collaborative undergraduate class project of MO 410 Collective Intelligence from University of Michigan. On the site, we describe some of the models being used to explain data, communicate threats, predict outcomes, evaluate policy proposals, and guide action. To the extent possible, we explain these models in plain language using graphics to clarify their core assumptions and logics.

Each model provides a lens on the spread of the virus and the potential effects of policy choices and behavioral responses. As more data becomes available, these lenses become better calibrated enabling individuals, communities, organizations, and governments to take wiser actions and more clearly communicate the logic of policy choices. We also provide links to background reading and to other model websites. For up to the minute data on the spread of COVID-19, we recommend Johns Hopkins' site.


We undertook this collaboration with four goals in mind:

1) To demonstrate how models are helping to explain and predict the spread of the COVID-19 pandemic, how they enable clearer communication and how they are helping to design better policies.

2) To show how ensembles of models and diverse models lead to more robust understanding and policies.

3) To explain in clear language the key models.

4) To use our collective energies and intelligence to produce public knowledge.

The COVID-19 pandemic produces an ever increasing flow of information. Whether these many efforts to inform and educate will produce collective understanding, effective policies, and stronger communities remains to be seen. We hope our contribution helps in some small way toward achieving collective understanding, good behaviors, and effective policies.


Organization of Site (with links)

Fatality Rate Models

Linear and Exponential Growth

The SIR Model

Why SIR Models Produce Exponential Growth

The SEIR Model

The SIS Model

Network Models

Agent Based Models







NOTE: This webpage was produced as part of a class on collective intelligence at the Ross School of Business at the University of Michigan. In that class, we study how collections of diverse, connected, adaptive parts combine to be more intelligent than their constituent members.
Scott E Page is John Seely Brown Distinguished University Professor of Complexity, Social Science, and Management at the University of Michigan, Ann Arbor, WIlliamson Family Professor of Management and an external faculty member of the Santa Fe Institute.