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Samuel Scarpino

Assistant Professor in the Network Science Institute at Northeastern University.

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(2021) One outstanding path from A to B. Nature Physics (with Shugars S).

(2021) Data curation during a pandemic and lessons learned from COVID-19. Nature Computational Science (with Kraemer MUG, Scarpino SV, Marivate V, Gutierrez B, Xu B, Lee G, Hawkins JB, Rivers C, Pigott DM, Katz R, and Brownstein JS).

(2020) Probing COVID’s complexity in real time. Science.

(2020) Leading with data on the path to normalcy. Tableau Blog.

(2020) Applications of predictive modeling during the early stages of the COVID-19 epidemic The Lancet Digital Health (with Poletto C and Volz E).

(2020) Modelling COVID-19: Realistic models require better data. Nature Reviews Physics (with Shrestha M).

(2020) Sampling bias and incorrect rooting make phylogenetic network tracing of SARS-COV-2 infections unreliable. PNAS (with Mavian C, Pond SK, Marini S, Magalis BR, Vandamme AM, Dellicour S, Houldcroft C, Villabona-Arenas J, Paisie TK, Trovao NS, Boucher C, Zhang Y, Scheuermann RH, Gascuel O, Tsan-Yuk Lam T, Suchard MA, Abecasis A, Wilkinson E, de Oliveira T, Bento AI, Schmidt KA, Martin D, Hadfield J, Faria N, Grubaugh ND, Neher RA, Baele G, Lemey P, Stadler T, Albert J, Crandall KA, Leitner T, Stamatakis A, Prosperi M, & Salemi M).

(2020) Aggregated Mobility Data Could Help Fight COVID-19. Science (with Buckee CO, Balsari S, Chan J, Crosas M, Dominici F, Gasser U, Grad YH, Grenfell B, Halloran ME, Kraemer MUG, Lipsitch M, Metcalf CJE, Meyers LA, Perkins TA, Santillana M, Scarpino SV, Viboud C, Wesolowski A, & Schroeder A).

(2020) Mobile phone data and COVID-19: Missing an opportunity? Science Advances (with Oliver N, Letouzé E, Sterly H, Delataille S, Nadai MD, Lepri B, Lambiotte R, Benjamins R, Cattuto C, Colizza V, de Cordes N, Fraiberger SP, Koebe T, Lehmann S, Murillo J, Pentland A, Pham PN, Pivetta F, Salah AA, Saramäki J, Tizzoni M, Verhulst S, Vinck P).

(2020) COVID-19: Keep calm and wash your hands. Medium.

(2020) Open access epidemiological data from the COVID-19 outbreak. The Lancet Infectious Diseases (with Xu B, Kraemer MUG, and the Open COVID-19 Data Curation Group).

(2019) Uncovering the hidden cost of bed bugs. PNAS (with Althouse BM).

(2019) Why it’s so difficult for scientists to predict the next outbreak of a dangerous disease. The Conversation (with Ogbunu CB and Harp R).

(2018) Modelling the trajectory of disease outbreaks works. Nature (with Rivers CM).

(2018) Epidemic Spreading: Don’t Close the Gates. Nature Physics.

(2017) The flu vaccine is effective. Medium.

(2015) 3 graphs that help show why Ebola goes viral or dies out. Nautilus.

(2014) Mathematical models: A key tool for outbreak response.(with Lofgren ET, Halloran ME, Rivers CM, Drake JM, Porco TC, Lewis BL, Yang W, Vespignani A, Shaman J, Eisenberg JNS, Eisenberg MC, Marathe MV, Alexander KA, Meza R, Ferrari MJ, Hyman JM, Meyers LA, and Eubank SG) Proc. Natl. Acad. Sci. USA.

(2014) Ebola: models do more than forecast. (with Rivers C, Alexander K, Bellan S, Del Valle S, Drake JM, Eisenberg JN, Eubank S, Ferrari M, Halloran ME, Galvani A, Lewis BL, Lewnard J, Lofgren E, Macal C, Marathe M, Ndeffo Mbah M, Meyers LA, Meza R, Park A, Porco T, Shaman J, Vespignani A, and Yang W) Nature.

(2014) Ebola: Mobility data. (with Halloran EM, Vespignani A, Bharti N, Feldstein LR, Alexander K, Ferrari M, Shaman J, Drake JM, Porco T, Eisenberg J, DeValle S, Lofgren E, Eisenberg M, Gao D, Hyman JM, Eubank S, and Longini IM) Science.

(2014) The mathematics of stopping Ebola. Santa Fe New Mexican.