Institution
Santa Fe Institute
Nonprofit•Santa Fe, New Mexico, United States•
About: Santa Fe Institute is a nonprofit organization based out in Santa Fe, New Mexico, United States. It is known for research contribution in the topics: Population & Complex network. The organization has 558 authors who have published 4558 publications receiving 396015 citations. The organization is also known as: SFI.
Papers published on a yearly basis
Papers
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TL;DR: It is suggested that opportunities exist to produce process-based range models for many species, by using hierarchical and inverse modeling to borrow strength across species, fill data gaps, fuse diverse data sets, and model across biological and spatial scales.
Abstract: Understanding and forecasting species' geographic distributions in the face of global change is a central priority in biodiversity science. The existing view is that one must choose between correlative models for many species versus process-based models for few species. We suggest that opportunities exist to produce process-based range models for many species, by using hierarchical and inverse modeling to borrow strength across species, fill data gaps, fuse diverse data sets, and model across biological and spatial scales. We review the statistical ecology and population and range modeling literature, illustrating these modeling strategies in action. A variety of large, coordinated ecological datasets that can feed into these modeling solutions already exist, and we highlight organisms that seem ripe for the challenge.
120 citations
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01 Jan 2011TL;DR: The authors provide an overview and synthesis of the literature on how social networks influence behaviors, with a focus on diffusion and discuss some of the more prominent models of network interactions, including recent advances regarding interdependent behaviors, modeled via games on networks.
Abstract: We provide an overview and synthesis of the literature on how social networks influence behaviors, with a focus on diffusion We discuss some highlights from the empirical literature on the impact of networks on behaviors and diffusion We also discuss some of the more prominent models of network interactions, including recent advances regarding interdependent behaviors, modeled via games on networks JEL Classification Codes: D85, C72, L14, Z13
120 citations
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TL;DR: The authors investigate the design of optimal incentives to contribute to a public good under these effects, by comparison to a naive planner who assumes they are absent, and find that conventional economic incentives and social preferences may be either complements or substitutes, explicit incentives crowding in or crowding out social preferences.
119 citations
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TL;DR: A stochastic stage-structured model of the cytotoxic T lymphocyte (CTL) response to antigen and the maintenance of immunological memory that allows the probability of T cell stimulation to depend on avidity but also incorporates the notion of an antigen-independent programmed proliferative response.
119 citations
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University of Oxford1, University of Edinburgh2, Vrije Universiteit Brussel3, Katholieke Universiteit Leuven4, Imperial College London5, Northeastern University6, University of São Paulo7, University of Birmingham8, Centre national de la recherche scientifique9, University of Vermont10, Santa Fe Institute11, Royal Veterinary College12
TL;DR: In this paper, the authors investigated the spatial invasion dynamics of lineage B.1.7 by jointly analyzing UK human mobility, virus genomes, and community-based polymerase chain reaction data.
Abstract: Understanding the causes and consequences of the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern is crucial to pandemic control yet difficult to achieve because they arise in the context of variable human behavior and immunity. We investigated the spatial invasion dynamics of lineage B.1.1.7 by jointly analyzing UK human mobility, virus genomes, and community-based polymerase chain reaction data. We identified a multistage spatial invasion process in which early B.1.1.7 growth rates were associated with mobility and asymmetric lineage export from a dominant source location, enhancing the effects of B.1.1.7's increased intrinsic transmissibility. We further explored how B.1.1.7 spread was shaped by nonpharmaceutical interventions and spatial variation in previous attack rates. Our findings show that careful accounting of the behavioral and epidemiological context within which variants of concern emerge is necessary to interpret correctly their observed relative growth rates.
119 citations
Authors
Showing all 606 results
Name | H-index | Papers | Citations |
---|---|---|---|
James Hone | 127 | 637 | 108193 |
James H. Brown | 125 | 423 | 72040 |
Alan S. Perelson | 118 | 632 | 66767 |
Mark Newman | 117 | 348 | 168598 |
Bette T. Korber | 117 | 392 | 49526 |
Marten Scheffer | 111 | 350 | 73789 |
Peter F. Stadler | 103 | 901 | 56813 |
Sanjay Jain | 103 | 881 | 46880 |
Henrik Jeldtoft Jensen | 102 | 1286 | 48138 |
Dirk Helbing | 101 | 642 | 56810 |
Oliver G. Pybus | 100 | 447 | 45313 |
Andrew P. Dobson | 98 | 322 | 44211 |
Carel P. van Schaik | 94 | 329 | 26908 |
Seth Lloyd | 92 | 490 | 50159 |
Andrew W. Lo | 85 | 378 | 51440 |