I gave a presentation at University of Pittsburgh (virtually of course) on 9/24/20, and discussed my motivation and vision for this blog, summarized some key facts I think we have learned, and highlighted the key role statisticians and other data scientists have in this pandemic and other major societal problems, to use our expertise and perspective to help policy-makers, decision-makers and the public navigate all of the noise to see what insights the data are revealing to us over time.
Our profession is far too shy and hesitant to engage with the broader society on these issues, but our perspective is crucial to effective evidence-based decisions, especially in a divided society in which many people seem to get their information from ideological echo-chambers that cherry pick data that supports their view points and fails to objectively assess and communicate what information can be reliably gleaned from the emerging data. We as a profession need to seek a "seat at the table" of decision making to ensure this important perspective is heard, and we need to make sure that our reward system is aligned to incentivize societal engagement and impact for our professionals.
Some of the topics I discuss include:
What I see as two misguided extremes in terms of viewpoints on this pandemic: denial and alarmism, and how I see our societal division and these extremes interfering with societal cooperation in dealing with this pandemic, given we all want the same things -- virtually no one wants more people affected or killed by the virus, or for businesses to go under or kids be kept from school, and virtually everyone wants our lives to get back to normal as soon as possible. The fact that we can't come together even under these circumstances shows how fractured and dysfunctional our society has become in its divisiveness.
What I think we have learned about how the virus spreads, and what this means in terms of which precautionary steps are most important to avoid getting infected.
Assessment of what various countries in the world have done to mitigate the spread, and how these strategies have worked.
I overview some hybrid statistical-epidemiological modeling done by PolicyLab collaborators to predict surging counties that has been used as a primary tool by the White House pandemic response team to help prioritize at risk regions for them to focus their mitigation efforts.
Discussion of the hazards of "wartime science", i.e. the need to rapidly learn and disseminate information about the virus to help the millions now affected, while trying to retain the rigor and reproducibility of scientific research. I highlight some cases where that process failed.
Mention of emergency use authorizations given thus far, and make some comments about the importance of the scientific agencies to be enabled to work unfettered by the viewpoints and agendas of political leaders in the government.
Brief mention of factors affecting disease severity, immunity, and vaccines (ran out of time to go into more details), and discussion of best and worst case scenarios I envision for the remainder of 2020 and into 2021 and beyond.
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