Complexity science and computer algorithms can help us address privacy concerns that arise with the pandemic.
American higher education must think outside the academy in a post-pandemic world.
It is important to keep in mind that as agents we maintain bottom-up control, even if we lack decisive power.
Despite the near-universal assumption of individuality in biology, there is little agreement about what individuals are and few rigorous quantitative methods for their identification. A new approach may solve the problem by defining individuals in terms of informational processes.
Beyond our response to the pandemic itself lie the longer-term effects, including new opportunities — social, political, economic, and otherwise.
The current spike in public trust in science gives science communicators an opportunity to reach new audiences.
This time of disruption is also one of opportunity.
A complex systems perspective of viruses offers insight for controlling SARS-CoV-2 and future emerging viruses.
Typical recession and recovery economic behavior offers great stock market buying opportunities.
In their op-ed for STAT, former SFI postdoctoral fellow Laurent Hébert-Dufresne (University of Vermont) and current postdoc Vicky Chuqiao Yang, Complexity Postdoctoral Fellow and Peters Hurst Scholar, argue that if scientists hope to develop better epidemiological models, they must grasp the complex interplay between social behavior and disease.
The analogies we live by are shaping our thoughts about our current situation.
Physical distancing is necessary for reducing infections, but the timing of restrictive confinement makes all the difference.
Transmission T-007 Danielle Allen, E. Glen Weyl, and Rajiv Sethi on How to Reduce COVID-19 Mortality While Easing Economic Decline
A “mobilize and transition” strategy could reduce COVID-19 mortality while cushioning the economic decline.
We can use social media data to detect signatures of global crises, including early warning signs.
Transmission T-005: Andrew Dobson on the Need for Disease Models which Capture Key Complexities of Transmission
The disease models used to guide policy for the COVID-19 pandemic must capture key complexities of transmission.
On March 31, five speakers from epidemiology and economics discussed strategies for both public health and economic recovery, and answered questions from the SFI community.
By using transmission to our advantage, we can eliminate coronavirus through citizen-based medicine.
Getting the quarantine end game right means thinking about how to change thinking itself.
To forecast the spread of the novel coronavirus, we must attend to the quality and consistency of the data.