
Samuel Scarpino
· Director of AI + Life SciencesNortheastern University · Artificial Intelligence
Active 2009–2024
Research topics
- Medicine
- Political Science
- Computer Science
- Environmental health
- Virology
- Business
- Sociology
- Demography
- Demographic economics
- Internal medicine
- Geography
- Economics
- Internet privacy
- Environmental planning
- Law
- Data science
- Environmental science
- Biology
- Environmental engineering
- Nursing
- Psychology
- Engineering
- Economic growth
- Public relations
Selected publications
Wastewater surveillance of pathogens can inform public health responses
Nature Medicine · 2022 · 197 citations
Senior authorCorresponding- Environmental health
- Business
- Environmental planning
The effect of eviction moratoria on the transmission of SARS-CoV-2
Nature Communications · 2021 · 94 citations
- Political Science
- Sociology
- Computer Science
Massive unemployment during the COVID-19 pandemic could result in an eviction crisis in US cities. Here we model the effect of evictions on SARS-CoV-2 epidemics, simulating viral transmission within and among households in a theoretical metropolitan area. We recreate a range of urban epidemic trajectories and project the course of the epidemic under two counterfactual scenarios, one in which a strict moratorium on evictions is in place and enforced, and another in which evictions are allowed to resume at baseline or increased rates. We find, across scenarios, that evictions lead to significant increases in infections. Applying our model to Philadelphia using locally-specific parameters shows that the increase is especially profound in models that consider realistically heterogenous cities in which both evictions and contacts occur more frequently in poorer neighborhoods. Our results provide a basis to assess eviction moratoria and show that policies to stem evictions are a warranted and important component of COVID-19 control.
Mask-wearing and control of SARS-CoV-2 transmission in the USA: a cross-sectional study
The Lancet Digital Health · 2021 · 307 citations
- Demography
- Medicine
- Environmental health
BACKGROUND: Face masks have become commonplace across the USA because of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic. Although evidence suggests that masks help to curb the spread of the disease, there is little empirical research at the population level. We investigate the association between self-reported mask-wearing, physical distancing, and SARS-CoV-2 transmission in the USA, along with the effect of statewide mandates on mask uptake. METHODS: <1). Additionally, mask-wearing in 12 states was evaluated 2 weeks before and after statewide mandates. FINDINGS: 378 207 individuals responded to the survey between June 3 and July 27, 2020, of which 4186 were excluded for missing data. We observed an increasing trend in reported mask usage across the USA, although uptake varied by geography. A logistic model controlling for physical distancing, population demographics, and other variables found that a 10% increase in self-reported mask-wearing was associated with an increased odds of transmission control (odds ratio 3·53, 95% CI 2·03-6·43). We found that communities with high reported mask-wearing and physical distancing had the highest predicted probability of transmission control. Segmented regression analysis of reported mask-wearing showed no statistically significant change in the slope after mandates were introduced; however, the upward trend in reported mask-wearing was preserved. INTERPRETATION: The widespread reported use of face masks combined with physical distancing increases the odds of SARS-CoV-2 transmission control. Self-reported mask-wearing increased separately from government mask mandates, suggesting that supplemental public health interventions are needed to maximise adoption and help to curb the ongoing epidemic. FUNDING: Flu Lab, Google.org (via the Tides Foundation), National Institutes for Health, National Science Foundation, Morris-Singer Foundation, MOOD, Branco Weiss Fellowship, Ending Pandemics, Centers for Disease Control and Prevention (USA).
Aggregated mobility data could help fight COVID-19
Science · 2020 · 460 citations
- Computer Science
- Political Science
- Business
Introduction to the article: As the coronavirus disease 2019 (COVID-19) epidemic worsens, understanding the effectiveness of public messaging and large-scale social distancing interventions is critical. The research and public health response communities can and should use population mobility data collected by private companies, with appropriate legal, organizational, and computational safeguards in place. When aggregated, these data can help refine interventions by providing near real-time information about changes in patterns of human movement.
PLoS Biology · 2020 · 284 citations
- Biology
- Environmental health
- Virology
Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the etiological agent of the Coronavirus Disease 2019 (COVID-19) disease, has moved rapidly around the globe, infecting millions and killing hundreds of thousands. The basic reproduction number, which has been widely used-appropriately and less appropriately-to characterize the transmissibility of the virus, hides the fact that transmission is stochastic, often dominated by a small number of individuals, and heavily influenced by superspreading events (SSEs). The distinct transmission features of SARS-CoV-2, e.g., high stochasticity under low prevalence (as compared to other pathogens, such as influenza), and the central role played by SSEs on transmission dynamics cannot be overlooked. Many explosive SSEs have occurred in indoor settings, stoking the pandemic and shaping its spread, such as long-term care facilities, prisons, meat-packing plants, produce processing facilities, fish factories, cruise ships, family gatherings, parties, and nightclubs. These SSEs demonstrate the urgent need to understand routes of transmission, while posing an opportunity to effectively contain outbreaks with targeted interventions to eliminate SSEs. Here, we describe the different types of SSEs, how they influence transmission, empirical evidence for their role in the COVID-19 pandemic, and give recommendations for control of SARS-CoV-2.
Recent grants
Translational Global Infectious Diseases Research Center
NIH · $5.2M · 2018–2023
Translational Global Infectious Diseases Research Center
NIH · $33.7M · 2018–2028
Frequent coauthors
- 137 shared
Benjamin M. Althouse
University of Washington
- 61 shared
Laurent Hébert‐Dufresne
University of Vermont
- 47 shared
Alessandro Vespignani
Northeastern University
- 47 shared
Brennan Klein
Northeastern University
- 37 shared
Moritz U. G. Kraemer
University of Oxford
- 36 shared
Lauren Ancel Meyers
- 34 shared
John S. Brownstein
Boston Children's Hospital
- 32 shared
Antoine Allard
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