- (paper which launched "suppression" strategy in US and UK) Neil Ferguson (Imperial College, London) report: "Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand": https://www.imperial.ac.uk/media/imperial-college/medicine/sph/ide/gida-fellowships/Imperial-College-COVID19-NPI-modelling-16-03-2020.pdf
- Neil Ferguson's updated model ("...Evidence of initial success for China exiting COVID-19 social distancing policy..."): https://www.imperial.ac.uk/media/imperial-college/medicine/sph/ide/gida-fellowships/Imperial-College-COVID19-Exiting-Social-Distancing-24-03-2020.pdf
- How much "normal" risk does COVID-19 represent: https://medium.com/wintoncentre/how-much-normal-risk-does-covid-represent-4539118e1196
- https://www.cdc.gov/coronavirus/2019-ncov/index.html
- Common mistakes made with epidemiological datasets/questions: https://twitter.com/adamjkucharski/status/1241089577139535874?s=12
- Paul Romer's toy model to evaluate 2 questins: How much disruption can testing avoid (ie targeted quarantine vs. non-specific, random quarantine)? How much does it matter if the tests are inaccurate (ie high false negative rate)?
- (EXCELLENT) Why It’s So Freaking Hard To Make A Good COVID-19 Model (FiveThirtyEight): https://fivethirtyeight.com/features/why-its-so-freaking-hard-to-make-a-good-covid-19-model/
- (FiveThirtyEight) Coronavirus case counts are meaningless:
- "The number of reported COVID-19 cases is not a very useful indicator of anything unless you also know something about how tests are being conducted."
- https://fivethirtyeight.com/features/coronavirus-case-counts-are-meaningless/
- (Zeynep Tufekci; EXCELLENT) Epidemiological models don't give us certainty, they allow us to prune catastrophic branches of a tree of possibilities that lies before us
- Contact tracing applications:
- (led by Trevor Bedford @tvrb, UW): https://twitter.com/trvrb/status/1245240645003833345?s=12
- Model critique: https://www.statnews.com/2020/04/17/influential-covid-19-model-uses-flawed-methods-shouldnt-guide-policies-critics-say/
- What we should believe and not believe about R (email to Tyler Cowen): https://marginalrevolution.com/marginalrevolution/2020/04/our-best-people-are-working-on-this-problem.html
- Problems with the Stanford study on antibody prevalence:
- the study: https://www.medrxiv.org/content/10.1101/2020.04.14.20062463v1.full.pdf
- Andrew Gelman (the error bounds on the specificity of the test are problematic): https://statmodeling.stat.columbia.edu/2020/04/19/fatal-flaws-in-stanford-study-of-coronavirus-prevalence/
- https://twitter.com/natesilver538/status/1252973441193558017?s=12
- https://twitter.com/trvrb/status/1251332447691628545?s=21
- https://twitter.com/nimwegenlab/status/1251261262366900232?s=21
- https://twitter.com/richardneher/status/1251484971279233025?s=21
- https://twitter.com/jjcherian/status/1251272882203885568?s=21
- https://twitter.com/danlarremore/status/1252315100729364480?s=21
- Andrew Gelman's "hierarchical Bayesian analysis for the Santa Clara study (and other prevalence studies in the presence of uncertainty in the specificity and sensitivity of the test)": https://statmodeling.stat.columbia.edu/2020/05/20/ok-heres-a-hierarchical-bayesian-analysis-for-the-santa-clara-study-and-other-prevalence-studies-in-the-presence-of-uncertainty-in-the-specificity-and-sensitivity-of-the-test/
- How much contact is required for transmission? https://twitter.com/mugecevik/status/1257392360960516097
- (yet another) critique of (yet another) John Ioannidis pre-print on seroprevalence studies: https://twitter.com/gidmk/status/1262956011872280577
- COVID-19 Treatment/Vaccine tracker: https://milkeninstitute.org/covid-19-tracker
- (RCTs) The Magic of Randomization: http://med.stanford.edu/content/dam/sm/epidemiology-dept/documents/EpiSeminars/Collins_Peto_MagicRandomization_NEJM_2020.pdf
- Coronavirus testing: https://www.wired.com/story/everything-you-need-to-know-about-coronavirus-testing/
- Pooled testing:
- https://joshbersin.com/2020/03/coronavirus-response-people-first-economics-second/
- https://elemental.medium.com/op-ed-what-if-americans-unemployed-by-coronavirus-could-be-hired-to-fight-it-8066ac4434e0
- Historian Jonathan Boff on lessons from WW I&II:
- https://jonathanboff.wordpress.com/2020/03/13/crisis-management-for-beginners-lessons-from-two-world-wars/
- https://jonathanboff.wordpress.com/2020/03/18/crisis-management-for-beginners-ii-lessons-from-two-world-wars/
- https://jonathanboff.wordpress.com/2020/03/19/crisis-management-for-beginners-iii-how-to-use-experts/
- Proposals for "re-opening" the (US) economy:
- Ezra Klein's op-ed on the following proposals: https://www.vox.com/2020/4/10/21215494/coronavirus-plans-social-distancing-economy-recession-depression-unemployment
- (The left-leaning Center for American Progress) "A National and State Plan To End the Coronavirus Crisis": https://www.americanprogress.org/issues/healthcare/news/2020/04/03/482613/national-state-plan-end-coronavirus-crisis/
- (Geln Weyl, et al for Harvard University’s Safra Center for Ethics) "Roadmap to Pandemic Resilience": https://ethics.harvard.edu/files/center-for-ethics/files/roadmaptopandemicresilience_updated_4.20.20.pdf
- (Harvard University’s Safra Center for Ethics) "When can we go out?": https://drive.google.com/file/d/1gf21eYeNWwrR9OO5nzxn1jlv-RTmHkt0/view
- (The conservative American Enterprise Institute) National coronavirus response: A road map to reopening : https://www.aei.org/research-products/report/national-coronavirus-response-a-road-map-to-reopening/
- Paul Romer's plan ("Roadmap to Responsibly Reopen America"): https://roadmap.paulromer.net/
- Has premature de-industrialisation in the US made our country less resilient/robust? https://danwang.co/definite-optimism-as-human-capital/
- "The information environment for coronavirus is going to be the most complicated information environment we've ever been in for a natural disaster."
- Patick McKenzie (@patio11): https://twitter.com/patio11/status/1241968109671428097
- Meet the Unacknowledged Hero Who Discovered That Handwashing Saves Lives: https://lithub.com/meet-the-unacknowledged-hero-who-discovered-that-handwashing-saves-lives/
- How to protect your family (Weill Cornell MD): https://vimeo.com/399733860:
- Garbage math (garbage in, garbage out): https://twitter.com/xkcdcomic/status/1251195400884826112?s=21
- Why aren't we wearing better masks?
- An evidence review of face masks against COVID-19: https://www.pnas.org/content/118/4/e2014564118
- https://www.theatlantic.com/health/archive/2021/01/why-arent-we-wearing-better-masks/617656/
- "Micro-covids" and COVID risk points (tool for calculating risk associated with a certain activity): https://www.wired.com/story/group-house-covid-risk-points/
- Our World in Data: https://ourworldindata.org/coronavirus
- Johns Hopkins: https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6
- FT: https://www.ft.com/coronavirus-latest
- How much "normal" risk does COVID-19 represent: https://medium.com/wintoncentre/how-much-normal-risk-does-covid-represent-4539118e1196
- What's the probability that someone in your childen's school is positive for Coronavirus: https://twitter.com/wtgowers/status/1239482689452941312?s=12
- What's the optimal procedure for testing a group of n people (given unlimited tests), to minimize the time to determine who is positive and who is negative, given the following:
- unlimited tests / test processing capacity
- each test takes t time
- current baseline pctg of positive results m% (ie m% of ppl test positive)
- test false positive rate is: j%
- test false negative rate is: k%
- source: https://twitter.com/robertwiblin/status/1241088888552263680?s=12
- How much difference does it make if the test used to send people into quarantine is bad?: https://paulromer.net/covid-sim-part3/
- TLDR: "It shouldn’t really come as a surprise, but any policy that puts people who are infectious into quarantine will slow the spread of the disease."
- Discuss how estimating Case Fatality Rates (CRF) is affected by differences in testing regimes (eg. across countries): https://www.cebm.net/covid-19/global-covid-19-case-fatality-rates/
- ^ link from Oxford's Centre for Evidence-Based Medicine
- How important are so-called “superspreader” events, for the transmission of Coronavirus?: https://twitter.com/adamjkucharski/status/1245336637497913345?s=12
- How do different testing regimes affect estimates of R and case counts?: https://fivethirtyeight.com/features/coronavirus-case-counts-are-meaningless/
- Antibody tests and Bayes' rule: how do you compute the P(you are + for COVID | you test + for COVID), given an antibody test with a certain sensitivity (ie probability that a person tests positive if they are infected), and a certain specificity (ie probability that a person tests negative given that they are negative)?
- How does sensitivity & specificity affect the inferences that we can draw from seroprevalence studies & inform the number of samples we need for statistical confidence?