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svscarpino's profile
Sam Scarpino #BLM
Sam Scarpino #BLM
Sam Scarpino #BLM
@svscarpino

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Sam Scarpino #BLM

@svscarpino

Scientist. Managing Director @RockefellerFdn & @ppi_insights; Faculty @Northeastern, @sfiscience, @uvmcomplexity; Cofounder @globaldothealth. He/him.

Boston, MA
scarpino.github.io
Joined February 2014

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    Sam Scarpino #BLM‏ @svscarpino 5 Nov 2020

    While we all anxiously await news from NV, GA, & PA, our (@LHDnets, @BMAlthouse, & @all_are) paper on pandemic risk assessment that goes *beyond R0* is out @RSocPublishing Interface. 1/10 https://royalsocietypublishing.org/doi/10.1098/rsif.2020.0393 …pic.twitter.com/i9Cb6Ou7Yq

    6:25 AM - 5 Nov 2020
    • 26 Retweets
    • 86 Likes
    • lstmpsmdrns💉💉 Christie Aschwanden Marlin Dr. Ossi Mokryn - ScanLab Paige Miller liubov Sangeeta Jayadevan Juniper Lovato Ollin D. Langle-Chimal
    3 replies 26 retweets 86 likes
      1. New conversation
      2. Sam Scarpino #BLM‏ @svscarpino 5 Nov 2020

        Sam Scarpino #BLM Retweeted Sam Scarpino #BLM

        Building from foundational work in math. epi. and network science, we show how super-spreading creates havoc for pandemic risk predictions based on R0 alone and then derive a method for correcting the predictions. 2/10https://twitter.com/svscarpino/status/1253112811569451008?s=20 …

        Sam Scarpino #BLM added,

        Sam Scarpino #BLM @svscarpino
        Replying to @RELenski @trvrb
        Exactly right! We (@LHDnets, @all_are, @BMAlthouse) show analytically that stochastic effects, higher moments, and networks have a *huge* effect on #COVID19 outbreak size. https://arxiv.org/abs/2002.04004  pic.twitter.com/uP9TgQh4ut
        1 reply 1 retweet 13 likes
        Show this thread
      3. Sam Scarpino #BLM‏ @svscarpino 5 Nov 2020

        This paper includes what I think is the most intuitive explanation for how higher moments in the distribution of secondary infections affects epidemic risk that I've read (@LHDnets & @all_are wrote the following lines). 3/10

        1 reply 0 retweets 7 likes
        Show this thread
      4. Sam Scarpino #BLM‏ @svscarpino 5 Nov 2020

        "The alternating sign of contribution from high-order moments in equation (3.3) can be interpreted as follows. A disease needs a high average number of secondary infections (high κ1 = R0) to spread, but," 4/10

        1 reply 0 retweets 9 likes
        Show this thread
      5. Sam Scarpino #BLM‏ @svscarpino 5 Nov 2020

        "given that average, a disease with small variance in secondary infections will spread much more reliably and be less likely to stochastically die out." 5/10

        1 reply 1 retweet 9 likes
        Show this thread
      6. Sam Scarpino #BLM‏ @svscarpino 5 Nov 2020

        "Given a variance, a disease with high skewness (i.e. with positive deviation contributing to most of the variance) will be more stable than a disease with negative skewness (i.e. with most deviations being towards small secondary infections)." 6/10

        1 reply 2 retweets 9 likes
        Show this thread
      7. Sam Scarpino #BLM‏ @svscarpino 5 Nov 2020

        "Given a skewness, a disease will be more stable if it has frequent small positive deviations rather than infrequent large deviations—hence a smaller kurtosis—as stochastic die out could easily occur before any of those large infrequent deviations occur." 7/10

        1 reply 0 retweets 8 likes
        Show this thread
      8. Sam Scarpino #BLM‏ @svscarpino 5 Nov 2020

        Sam Scarpino #BLM Retweeted Sam Scarpino #BLM

        Thinking about the implications of this work led us to write a second paper, on how de-coupling the risk of infection from transmission breaks the friendship paradox, which most (non-mass-action) herd immunity thresholds rely on. 8/10https://twitter.com/svscarpino/status/1264934828631261190?s=20 …

        Sam Scarpino #BLM added,

        Sam Scarpino #BLM @svscarpino
        Does it matter whether your risk of infection & transmission of #COVID19 are correlated? Yes, especially for super-spreading events, herd immunity, epidemic size, & contact tracing. New work w/ @all_are, @LHDnets, @BMAlthouse, & Cris Moore from @sfiscience https://arxiv.org/abs/2005.11283  pic.twitter.com/dN3MfYdbKz
        2 replies 0 retweets 10 likes
        Show this thread
      9. Sam Scarpino #BLM‏ @svscarpino 5 Nov 2020

        And, as we point out in the pre-print, these results highlight the *critical* importance of backwards case investigation and cluster busting for controlling diseases like #COVID19. https://arxiv.org/abs/2005.11283  9/10

        1 reply 1 retweet 11 likes
        Show this thread
      10. Sam Scarpino #BLM‏ @svscarpino 5 Nov 2020

        Sam Scarpino #BLM Retweeted Laurent Hébert-Dufresne

        Lastly, we further extended this line of reasoning to derive thresholds for controlling diseases w/ NPIs, which accounts for super-spreading and stochasticity, and arrive at a number between 10 - 15 secondary infections for #COVID19. 10/10https://twitter.com/LHDnets/status/1263432599873236992?s=20 …

        Sam Scarpino #BLM added,

        Laurent Hébert-Dufresne @LHDnets
        Beyond epidemic size, this new preprint with the @IDMOD_ORG team (and others) looks at what heterogeneity means for the probability of local outbreaks and for their control. Superspreading events are network effects to be targeted and mitigated. https://covid.idmod.org/data/Stochasticity_heterogeneity_transmission_dynamics_SARS-CoV-2.pdf … https://twitter.com/joel_c_miller/status/1263286943078420480 … pic.twitter.com/tyDs5aJDrl
        0 replies 0 retweets 11 likes
        Show this thread
      11. End of conversation
      1. New conversation
      2. james staub‏ @mmuddywires 5 Nov 2020
        Replying to @svscarpino @LHDnets and

        first glance thought this was a contemporary score from @NotationIsGreat

        1 reply 0 retweets 2 likes
      3. Sam Scarpino #BLM‏ @svscarpino 5 Nov 2020
        Replying to @mmuddywires @LHDnets and

        🤣

        0 replies 0 retweets 0 likes
      4. End of conversation

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