- Mar 2022
-
psyarxiv.com psyarxiv.com
-
Villanueva, Cynthia, Stevi Ibonie, Emily Jensen, Lucca Eloy, Jordi Quoidbach, Angela Bryan, Sidney D’Mello, and June Gruber. ‘Emotion Differentiation and Bipolar Risk in Emerging Adults Before and During the COVID-19 Pandemic’. PsyArXiv, 19 February 2022. https://doi.org/10.31234/osf.io/xya43.
-
- Feb 2022
-
Local file Local file
-
Dweck shows convincingly thatthe most reliable predictor for long-term success is having a “growthmindset.” To actively seek and welcome feedback, be it positive ornegative, is one of the most important factors for success (andhappiness) in the long run. Conversely, nothing is a bigger hindranceto personal growth than having a “fixed mindset.” Those who fearand avoid feedback because it might damage their cherishedpositive self-image might feel better in the short term, but will quicklyfall behind in actual performance (Dweck 2006; 2013).
Carol Dweck shows that the most reliable predictor for long-term success is what she calls having a "growth mindset" or the ability to take feedback and change.
This seems related to the idea of endergonic reactions and the growth of complexity as well as the idea of the meaning of life.
What do these systems all have in common? What are their differences? What abstractions can we make from them?
Relate this to https://hypothes.is/a/pdWppIX5EeyhR0NR19OjCQ
-
- Jan 2022
-
academic.oup.com academic.oup.com
-
Yonker, L. M., Boucau, J., Regan, J., Choudhary, M. C., Burns, M. D., Young, N., Farkas, E. J., Davis, J. P., Moschovis, P. P., Bernard Kinane, T., Fasano, A., Neilan, A. M., Li, J. Z., & Barczak, A. K. (2021). Virologic Features of Severe Acute Respiratory Syndrome Coronavirus 2 Infection in Children. The Journal of Infectious Diseases, 224(11), 1821–1829. https://doi.org/10.1093/infdis/jiab509
-
- Nov 2021
-
www.theatlantic.com www.theatlantic.com
-
It’s not just the hyper-social and the flirtatious who have found themselves victims of the New Puritanism. People who are, for lack of a more precise word, difficult have trouble too. They are haughty, impatient, confrontational, or insufficiently interested in people whom they perceive to be less talented. Others are high achievers, who in turn set high standards for their colleagues or students. When those high standards are not met, these people say so, and that doesn’t go over well. Some of them like to push boundaries, especially intellectual boundaries, or to question orthodoxies. When people disagree with them, they argue back with relish.
How much of this can be written down to differing personal contexts and lack of respect for people's humanity? Are the neurodivergent being punished in these spaces?
Applebaum provides a list of potential conflict areas of cancel culture outside of power dynamics.
-
Once it was not just okay but admirable that Chua and Rubenfeld had law-school students over to their house for gatherings. That moment has passed. So, too, has the time when a student could discuss her personal problems with her professor, or when an employee could gossip with his employer. Conversations between people who have different statuses—employer-employee, professor-student—can now focus only on professional matters, or strictly neutral topics. Anything sexual, even in an academic context—for example, a conversation about the laws of rape—is now risky.
Is it simply the stratification of power and roles that is causing these problems? Is it that some of this has changed and that communication between people of different power levels is the difficulty in these cases?
I have noticed a movement in pedagogy spaces that puts the teacher as a participant rather than as a leader thus erasing the power structures that previously existed. This exists within Cathy Davidson's The New Education where teachers indicate that they're learning as much as their students.
-
- Oct 2021
-
www.jstor.org www.jstor.org
-
Team syntegrity and democratic group decision making: theory and practice
Team Syntegrity
Stafford Beer created Team Syntegrity as a methodology for social interaction that predisposes participants towards shared agreement among varied and sometimes conflicting interests, without compromising the legitimate claims and integrity of those interests. This paper outlines the methodology and the underlying philosophy, describing several applications in a variety of countries and contexts, indicating why such an approach causes us to re-think more traditional approaches to group decision processes, and relating Team Syntegrity to other systems approaches.
Shared by Kirby Urner in the Trimtab Book Club
-
- Jun 2021
-
arxiv.org arxiv.org
-
Reis, E. F. dos, & Masuda, N. (2021). Metapopulation models imply non-Poissonian statistics of interevent times. ArXiv:2106.10348 [Physics]. http://arxiv.org/abs/2106.10348
-
- Apr 2021
-
psyarxiv.com psyarxiv.com
-
Betsch, Cornelia, and Philipp Sprengholz. ‘The Human Factor between Airborne Pollen Concentrations and COVID-19 Disease Dynamics’. PsyArXiv, 16 April 2021. https://doi.org/10.31234/osf.io/hw9gf.
-
- Mar 2021
-
www.scientificamerican.com www.scientificamerican.com
-
Oreskes, N. (n.d.). Jeffrey Epstein’s Harvard Connections Show How Money Can Distort Research. Scientific American. https://doi.org/10.1038/scientificamerican0920-84
-
- Feb 2021
-
psyarxiv.com psyarxiv.com
-
Jacobson, N. C., Price, G., Song, M., Wortzman, Z., Nguyen, N. D., & Klein, R. J. (2020, October 27). Machine Learning Models Predicting Daily Affective Dynamics Via Personality and Psychopathology Traits. https://doi.org/10.31234/osf.io/2zgv6
-
- Jan 2021
-
psyarxiv.com psyarxiv.com
-
Dideriksen, C., Christiansen, M. H., Tylén, K., Dingemanse, M., & Fusaroli, R. (2020, October 12). Building common ground: Quantifying the interplay of mechanisms that promote understanding in conversations. https://doi.org/10.31234/osf.io/a5r74
-
-
-
Landry. N., Restrepo. J. G. (2020).The effect of heterogeneity on hypergraph contagion models. Physics and Society. Retrieved from: https://arxiv.org/abs/2006.15453
-
- Nov 2020
-
www.ncbi.nlm.nih.gov www.ncbi.nlm.nih.gov
-
by attaching acceptor and donor to different domains of a target protein, the interdomain dynamics can be monitored
-
-
www.michellekimconsulting.com www.michellekimconsulting.com
-
What’s truly sad (but not shocking) about this whole situation is that this person, James Damore, a Havard educated, seemingly well-intentioned fella, had steadfast beliefs based on his complete misunderstanding of how “sexism” or “discrimination” actually work.And that’s the problem with the way we talk about diversity and inclusion in the business world.People are learning about unconscious bias WITHOUT the foundational knowledge of the cycle of socialization.People are learning about microaggressions WITHOUT the context of power dynamics.People are learning about “diversity programs” WITHOUT true understanding of concepts such as privilege or allyship.
While there are some people with good intents in the [[DEI]] space - it's starting to become apparent that there are some [[foundational concepts]] that we are missing, such as understanding how [[cycle of socialization]] impacts [[unconscious bias]]
or not understanding the role of [[power dynamics]] and [[microaggression]]
-
- Oct 2020
-
-
Maia, H. P., Ferreira, S. C., & Martins, M. L. (2020). Adaptive network approach for emergence of societal bubbles. ArXiv:2010.08635 [Nlin, Physics:Physics]. http://arxiv.org/abs/2010.08635
-
-
covid-19.iza.org covid-19.iza.org
-
COVID-19 and the Labor Market. (n.d.). IZA – Institute of Labor Economics. Retrieved October 10, 2020, from https://covid-19.iza.org/publications/dp13569/
-
-
-
Ghavasieh, A., Nicolini, C., & De Domenico, M. (2020). Statistical physics of complex information dynamics. ArXiv:2010.04014 [Cond-Mat, Physics:Physics]. http://arxiv.org/abs/2010.04014
-
-
link.aps.org link.aps.org
-
Gaisbauer, F., Olbrich, E., & Banisch, S. (2020). Dynamics of opinion expression. Physical Review E, 102(4), 042303. https://doi.org/10.1103/PhysRevE.102.042303
-
-
link.aps.org link.aps.org
-
Burda, Z., Kotwica, M., & Malarz, K. (2020). Ageing of complex networks. Physical Review E, 102(4), 042302. https://doi.org/10.1103/PhysRevE.102.042302
-
-
www.pnas.org www.pnas.org
-
Karatayev, Vadim A., Madhur Anand, and Chris T. Bauch. ‘Local Lockdowns Outperform Global Lockdown on the Far Side of the COVID-19 Epidemic Curve’. Proceedings of the National Academy of Sciences 117, no. 39 (29 September 2020): 24575–80. https://doi.org/10.1073/pnas.2014385117.
-
- Sep 2020
-
-
Chen, Q., & Porter, M. A. (2020). Epidemic Thresholds of Infectious Diseases on Tie-Decay Networks. ArXiv:2009.12932 [Physics]. http://arxiv.org/abs/2009.12932
-
-
arxiv.org arxiv.org
-
James, N., & Menzies, M. (2020). Human and financial cost of COVID-19. ArXiv:2009.11660 [Physics, q-Fin]. http://arxiv.org/abs/2009.11660
-
-
twitter.com twitter.com
-
Pausal Živference on Twitter. (n.d.). Twitter. Retrieved September 26, 2020, from https://twitter.com/PausalZ/status/1309208611265093632
-
-
science.sciencemag.org science.sciencemag.org
-
Saad-Roy, C. M., Wagner, C. E., Baker, R. E., Morris, S. E., Farrar, J., Graham, A. L., Levin, S. A., Mina, M. J., Metcalf, C. J. E., & Grenfell, B. T. (2020). Immune life history, vaccination, and the dynamics of SARS-CoV-2 over the next 5 years. Science. https://doi.org/10.1126/science.abd7343
-
-
www.imperial.ac.uk www.imperial.ac.uk
-
COVID-19 reports. (n.d.). Imperial College London. Retrieved September 17, 2020, from http://www.imperial.ac.uk/medicine/departments/school-public-health/infectious-disease-epidemiology/mrc-global-infectious-disease-analysis/covid-19/covid-19-reports/
-
-
-
Giles, J. R., Erbach-Schoenberg, E. zu, Tatem, A. J., Gardner, L., Bjørnstad, O. N., Metcalf, C. J. E., & Wesolowski, A. (2020). The duration of travel impacts the spatial dynamics of infectious diseases. Proceedings of the National Academy of Sciences, 117(36), 22572–22579. https://doi.org/10.1073/pnas.1922663117
-
-
academic.oup.com academic.oup.com
-
Kahn, R., Kennedy-Shaffer, L., Grad, Y. H., Robins, J. M., & Lipsitch, M. (n.d.). Potential Biases Arising from Epidemic Dynamics in Observational Seroprotection Studies. American Journal of Epidemiology. https://doi.org/10.1093/aje/kwaa188
-
-
github.com github.com
-
aaronpeikert. (2020). Aaronpeikert/reproducible-research [TeX]. https://github.com/aaronpeikert/reproducible-research (Original work published 2019)
-
-
-
Miyoshi, S., Jusup, M., & Holme, P. (2020). Flexible imitation mechanisms suppress epidemics through better vaccination. ArXiv:2009.00443 [Physics, q-Bio]. http://arxiv.org/abs/2009.00443
-
-
arxiv.org arxiv.org
-
Carmona, H. A., de Noronha, A. W. T., Moreira, A. A., Araujo, N. A. M., & Andrade Jr, J. S. (2020). Cracking urban mobility. ArXiv:2008.13644 [Cond-Mat, Physics:Physics]. http://arxiv.org/abs/2008.13644
-
- Aug 2020
-
link.aps.org link.aps.org
-
Perez, I. A., Di Muro, M. A., La Rocca, C. E., & Braunstein, L. A. (2020). Disease spreading with social distancing: A prevention strategy in disordered multiplex networks. Physical Review E, 102(2), 022310. https://doi.org/10.1103/PhysRevE.102.022310
-
-
-
Waites, W., Cavaliere, M., Manheim, D., Panovska-Griffiths, J., & Danos, V. (2020). Scaling up epidemiological models with rule-based modelling. ArXiv:2006.12077 [q-Bio]. http://arxiv.org/abs/2006.12077
-
-
covid-19.iza.org covid-19.iza.org
-
Dynamics of Social Mobility during the COVID-19 Pandemic in Canada. COVID-19 and the Labor Market. (n.d.). IZA – Institute of Labor Economics. Retrieved August 4, 2020, from https://covid-19.iza.org/publications/dp13376/
-
-
-
Moya, C., Cruz y Celis Peniche, P. D., Kline, M. A., & Smaldino, P. (2020). Dynamics of Behavior Change in the COVID World [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/kxajh
-
-
www.medrxiv.org www.medrxiv.org
-
Starnini, M., Aleta, A., Tizzoni, M., & Moreno, Y. (2020). Impact of the accuracy of case-based surveillance data on the estimation of time-varying reproduction numbers. MedRxiv, 2020.06.26.20140871. https://doi.org/10.1101/2020.06.26.20140871
-
-
www.biorxiv.org www.biorxiv.org
-
Sun, F., Wang, X., Tan, S., Dan, Y., Lu, Y., Zhang, J., Xu, J., Tan, Z., Xiang, X., Zhou, Y., He, W., Wan, X., Zhang, W., Chen, Y., Tan, W., & Deng, G. (2020). SARS-CoV-2 Quasispecies provides insight into its genetic dynamics during infection. BioRxiv, 2020.08.20.258376. https://doi.org/10.1101/2020.08.20.258376
-
-
link.aps.org link.aps.org
-
Velásquez-Rojas, F., Ventura, P. C., Connaughton, C., Moreno, Y., Rodrigues, F. A., & Vazquez, F. (2020). Disease and information spreading at different speeds in multiplex networks. Physical Review E, 102(2), 022312. https://doi.org/10.1103/PhysRevE.102.022312
-
-
-
Sun, K., Wang, W., Gao, L., Wang, Y., Luo, K., Ren, L., Zhan, Z., Chen, X., Zhao, S., Huang, Y., Sun, Q., Liu, Z., Litvinova, M., Vespignani, A., Ajelli, M., Viboud, C., & Yu, H. (2020). Transmission heterogeneities, kinetics, and controllability of SARS-CoV-2. MedRxiv, 2020.08.09.20171132. https://doi.org/10.1101/2020.08.09.20171132
-
-
www.medrxiv.org www.medrxiv.org
-
Cevik, M., Tate, M., Lloyd, O., Maraolo, A. E., Schafers, J., & Ho, A. (2020). SARS-CoV-2, SARS-CoV-1 and MERS-CoV viral load dynamics, duration of viral shedding and infectiousness: A living systematic review and meta-analysis. MedRxiv, 2020.07.25.20162107. https://doi.org/10.1101/2020.07.25.20162107
-
-
arxiv.org arxiv.org
-
Ran, Y., Deng, X., Wang, X., & Jia, T. (2020). A generalized linear threshold model for an improved description of the spreading dynamics. Chaos: An Interdisciplinary Journal of Nonlinear Science, 30(8), 083127. https://doi.org/10.1063/5.0011658
-
-
www.nber.org www.nber.org
-
Bisin, A., & Moro, A. (2020). Learning Epidemiology by Doing: The Empirical Implications of a Spatial-SIR Model with Behavioral Responses (Working Paper No. 27590; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27590
-
-
www.nber.org www.nber.org
-
Alon, T., Kim, M., Lagakos, D., & VanVuren, M. (2020). How Should Policy Responses to the COVID-19 Pandemic Differ in the Developing World? (Working Paper No. 27273; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27273
-
-
-
Krueger, D., Uhlig, H., & Xie, T. (2020). Macroeconomic Dynamics and Reallocation in an Epidemic (Working Paper No. 27047; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27047
-
-
-
Jinjarak, Y., Ahmed, R., Nair-Desai, S., Xin, W., & Aizenman, J. (2020). Accounting for Global COVID-19 Diffusion Patterns, January-April 2020 (Working Paper No. 27185; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27185
-
-
-
Radicchi, F., & Bianconi, G. (2020). Epidemic plateau in critical SIR dynamics with non-trivial initial conditions. ArXiv:2007.15034 [Cond-Mat, Physics:Physics, q-Bio]. http://arxiv.org/abs/2007.15034
-
- Jul 2020
-
www.medrxiv.org www.medrxiv.org
-
Golding, N., Russell, T. W., Abbott, S., Hellewell, J., Pearson, C. A. B., Zandvoort, K. van, Jarvis, C. I., Gibbs, H., Liu, Y., Eggo, R. M., Edmunds, J. W., & Kucharski, A. J. (2020). Reconstructing the global dynamics of under-ascertained COVID-19 cases and infections. MedRxiv, 2020.07.07.20148460. https://doi.org/10.1101/2020.07.07.20148460
-
-
-
Reher, D. S., Requena, M., de Santis, G., Esteve, A., Bacci, M. L., Padyab, M., & Sandström, G. (2020). The COVID-19 pandemic in an aging world [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/bfvxt
-
-
-
bahri, muhamad. (2020). The nexus impacts of the Covid-19: A qualitative perspective [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/yj8c9
-
-
-
Cazabet, R., Boudebza, S., & Rossetti, G. (2020). Evaluating Community Detection Algorithms for Progressively Evolving Graphs. ArXiv:2007.08635 [Physics]. http://arxiv.org/abs/2007.08635
-
-
www.nationalgeographic.com www.nationalgeographic.com
-
See how a sneeze can launch germs much farther than 6 feet. (2020, April 17). Science. https://www.nationalgeographic.com/science/2020/04/coronavirus-covid-sneeze-fluid-dynamics-in-photos/
-
-
-
Shafer, K., Milkie, M., & Scheibling, C. (2020). The Division of Labour Before & During the COVID-19 Pandemic in Canada [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/24j87
-
-
osf.io osf.io
-
Dou, Z., Stefanovski, D., Galligan, D., Lindem, M., Rozin, P., Chen, T., & Chao, A. M. (2020). The COVID-19 Pandemic Impacting Household Food Dynamics: A Cross-National Comparison of China and the U.S. [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/64jwy
-
-
osf.io osf.io
-
Schraff, D. (2020). Political trust during the Covid-19 pandemic: Rally around the flag or lockdown effects? [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/pu47c
-
-
link.aps.org link.aps.org
-
Sayama, H. (2020). Enhanced ability of information gathering may intensify disagreement among groups. Physical Review E, 102(1), 012303. https://doi.org/10.1103/PhysRevE.102.012303
-
-
-
Flushing “can propel viral infection 3ft into air.” (2020, June 16). BBC News. https://www.bbc.com/news/health-53047819
-
-
aip.scitation.org aip.scitation.org
-
Coronavirus cases may be linked to brain complications, study finds. (n.d.). Thetowntalk. Retrieved July 5, 2020, from https://www.usatoday.com/story/news/health/2020/06/27/coronavirus-cases-may-linked-brain-complications-study-finds/3270615001/
-
-
-
Althouse, B. M., Wenger, E. A., Miller, J. C., Scarpino, S. V., Allard, A., Hébert-Dufresne, L., & Hu, H. (2020). Stochasticity and heterogeneity in the transmission dynamics of SARS-CoV-2. ArXiv:2005.13689 [Physics, q-Bio]. http://arxiv.org/abs/2005.13689
-
- Jun 2020
-
-
Mohseni-Kabir, A., Pant, M., Towsley, D., Guha, S., & Swami, A. (2020). Percolation Thresholds for Robust Network Connectivity. ArXiv:2006.14496 [Cond-Mat, Physics:Physics]. http://arxiv.org/abs/2006.14496
-
-
doi.org doi.org
-
Willem, L., Hoang, T. V., Funk, S., Coletti, P., Beutels, P., & Hens, N. (2020). SOCRATES: An online tool leveraging a social contact data sharing initiative to assess mitigation strategies for COVID-19 [Preprint]. Epidemiology. https://doi.org/10.1101/2020.03.03.20030627
-
-
psyarxiv.com psyarxiv.com
-
Schmidt, A., Brose, A., Kramer, A., Schmiedek, F., Witthöft, M., & Neubauer, A. (2020). Cyclical Across-Day Dynamics of Corona-Related Media Exposure and Worries in People’s Everyday Lives During the COVID-19 Pandemic. https://doi.org/10.31234/osf.io/rea57
-
-
twitter.com twitter.com
-
Mason Porter on Twitter: “I am here to help. https://t.co/JBQbTAPTQX” / Twitter. (n.d.). Twitter. Retrieved June 17, 2020, from https://twitter.com/masonporter/status/1273054551583555585
-
-
www.medscape.com www.medscape.com
-
Dean, N. E. (2020, June 15). Asymptomatic Transmission? We Just Don’t Know. Medscape. Retrieved June 16, 2020, from http://www.medscape.com/viewarticle/932246
-
-
-
Murphy, C., Laurence, E., & Allard, A. (2020). Deep learning of stochastic contagion dynamics on complex networks. ArXiv:2006.05410 [Cond-Mat, Physics:Physics, Stat]. http://arxiv.org/abs/2006.05410
-
-
threader.app threader.app
-
Balloux, F. (2020, May 22) A thread written by @BallouxFrancois. Threader. https://threader.app/thread/1263745877702737920
-
-
-
Khamsi, R. (2020, March 14). They Say Coronavirus Isn’t Airborne—But It’s Definitely Borne by Air. Wired. https://www.wired.com/story/they-say-coronavirus-isnt-airborne-but-its-definitely-borne-by-air/
-
-
fivethirtyeight.com fivethirtyeight.com
-
Koerth, M. (2020, March 31). 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/
-
-
-
Della Rossa, F., & DeLellis, P. (2020). Stochastic master stability function for noisy complex networks. Physical Review E, 101(5), 052211. https://doi.org/10.1103/PhysRevE.101.052211
-
-
-
Ortiz, E., García-Pérez, G., & Serrano, M. Á. (2020). Geometric detection of hierarchical backbones in real networks. ArXiv:2006.03207 [Physics]. http://arxiv.org/abs/2006.03207
-
-
jamanetwork.com jamanetwork.com
-
Cheng, H.-Y., Jian, S.-W., Liu, D.-P., Ng, T.-C., Huang, W.-T., Lin, H.-H., & for the Taiwan COVID-19 Outbreak Investigation Team. (2020). Contact Tracing Assessment of COVID-19 Transmission Dynamics in Taiwan and Risk at Different Exposure Periods Before and After Symptom Onset. JAMA Internal Medicine. https://doi.org/10.1001/jamainternmed.2020.2020
-
-
arxiv.org arxiv.org
-
Yu, Y. W., Delvenne, J.-C., Yaliraki, S. N., & Barahona, M. (2020). Severability of mesoscale components and local time scales in dynamical networks. ArXiv:2006.02972 [Physics]. http://arxiv.org/abs/2006.02972
-
-
-
Velásquez-Rojas, F., da Silva, P. C. V., Connaughton, C., Moreno, Y., Rodrigues, F. A., & Vazquez, F. (2020). Disease and information spreading at different speeds in multiplex networks. ArXiv:2006.01965 [Physics]. http://arxiv.org/abs/2006.01965
-
-
www.ages.at www.ages.at
-
Epidemiologische Abklärung am Beispiel COVID-19. (n.d.). AGES - Österreichische Agentur für Gesundheit und Ernährungssicherheit. https://www.ages.at/service/service-presse/pressemeldungen/epidemiologische-abklaerung-am-beispiel-covid-19/
-
-
iaciac.github.io iaciac.github.io
-
Iacopini, I. (2020, June 3). Networks beyond pairwise interactions: Structure and dynamics. Iacopo Iacopini. https://iaciac.github.io/post/beyond/
-
-
science.sciencemag.org science.sciencemag.org
-
Zhang, J., Litvinova, M., Liang, Y., Wang, Y., Wang, W., Zhao, S., Wu, Q., Merler, S., Viboud, C., Vespignani, A., Ajelli, M., & Yu, H. (2020). Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China. Science, eabb8001. https://doi.org/10.1126/science.abb8001
-
-
-
Eroglu, D. (2020). Revealing Dynamics, Communities, and Criticality from Data. Physical Review X, 10(2). https://doi.org/10.1103/PhysRevX.10.021047
-
- May 2020
-
psyarxiv.com psyarxiv.com
-
Golino, H., Christensen, A. P., Moulder, R. G., Kim, S., & Boker, S. M. (2020, April 14). Modeling latent topics in social media using Dynamic Exploratory Graph Analysis: The case of the right-wing and left-wing trolls in the 2016 US elections. https://doi.org/10.31234/osf.io/tfs7c
-
-
www.thelancet.com www.thelancet.com
-
Rybniker, J., & Fätkenheuer, G. (2020). Importance of precise data on SARS-CoV-2 transmission dynamics control. The Lancet Infectious Diseases, S1473309920303595. https://doi.org/10.1016/S1473-3099(20)30359-5
-
-
www.thelancet.com www.thelancet.com
-
Liu, Y., Eggo, R. M., & Kucharski, A. J. (2020). Secondary attack rate and superspreading events for SARS-CoV-2. The Lancet, 395(10227), e47. https://doi.org/10.1016/S0140-6736(20)30462-1
-
-
wellcomeopenresearch.org wellcomeopenresearch.org
-
Endo, A., Centre for the Mathematical Modelling of Infectious Diseases COVID-19 Working Group, Abbott, S., Kucharski, A. J., & Funk, S. (2020). Estimating the overdispersion in COVID-19 transmission using outbreak sizes outside China. Wellcome Open Research, 5, 67. https://doi.org/10.12688/wellcomeopenres.15842.1
-
-
science.sciencemag.org science.sciencemag.org
-
Cobey, S. (2020). Modeling infectious disease dynamics. Science, 368(6492), 713–714. https://doi.org/10.1126/science.abb5659
-
-
www.pnas.org www.pnas.org
-
Stadnytskyi, V., Bax, C. E., Bax, A., & Anfinrud, P. (2020). The airborne lifetime of small speech droplets and their potential importance in SARS-CoV-2 transmission. Proceedings of the National Academy of Sciences, 202006874. https://doi.org/10.1073/pnas.2006874117
-
-
-
Pichler, Anton, Marco Pangallo, R. Maria del Rio-Chanona, François Lafond, and J. Doyne Farmer. “Production Networks and Epidemic Spreading: How to Restart the UK Economy?” ArXiv:2005.10585 [Physics, q-Fin], May 21, 2020. http://arxiv.org/abs/2005.10585.
Tags
- GDP
- lang:en
- supply
- input-output constraints
- economics
- economic growth
- production network
- reopening industry
- unemployment
- is:article
- work from home
- COVID-19
- United Kingdom
- epidemiology
- consumption
- production
- demand
- inventory dynamics
- social distincing
- transmission rate
- epidemic spreading
- industry
Annotators
URL
-
-
-
Johnson, N.F., Velásquez, N., Restrepo, N.J. et al. The online competition between pro- and anti-vaccination views. Nature (2020). https://doi.org/10.1038/s41586-020-2281-1
-
-
-
Masuda, N., & Holme, P. (2020). Small inter-event times govern epidemic spreading on networks. Physical Review Research, 2(2), 023163. https://doi.org/10.1103/PhysRevResearch.2.023163
-
-
www.annualreviews.org www.annualreviews.org
-
Edelmann, A., Wolff, T., Montagne, D., & Bail, C. A. (2020). Computational Social Science and Sociology. Annual Review of Sociology, 46(1), annurev-soc-121919-054621. https://doi.org/10.1146/annurev-soc-121919-054621
-
-
arxiv.org arxiv.org
-
Tuninetti, M., Aleta, A., Paolotti, D., Moreno, Y., & Starnini, M. (2020). Prediction of scientific collaborations through multiplex interaction networks. ArXiv:2005.04432 [Physics]. http://arxiv.org/abs/2005.04432
-
-
sfi-edu.s3.amazonaws.com sfi-edu.s3.amazonaws.com
-
The Santa Fe Institute - SFI Transmission PDF
-
-
www.medrxiv.org www.medrxiv.org
-
Buitrago-Garcia, D. C., Egli-Gany, D., Counotte, M. J., Hossmann, S., Imeri, H., Salanti, G., & Low, N. (2020). The role of asymptomatic SARS-CoV-2 infections: Rapid living systematic review and meta-analysis [Preprint]. Epidemiology. https://doi.org/10.1101/2020.04.25.20079103
-
-
www.nejm.org www.nejm.org
-
Arons, M. M., Hatfield, K. M., Reddy, S. C., Kimball, A., James, A., Jacobs, J. R., Taylor, J., Spicer, K., Bardossy, A. C., Oakley, L. P., Tanwar, S., Dyal, J. W., Harney, J., Chisty, Z., Bell, J. M., Methner, M., Paul, P., Carlson, C. M., McLaughlin, H. P., … Jernigan, J. A. (2020). Presymptomatic SARS-CoV-2 Infections and Transmission in a Skilled Nursing Facility. New England Journal of Medicine, NEJMoa2008457. https://doi.org/10.1056/NEJMoa2008457
-
-
www.cdc.gov www.cdc.gov
-
Ghinai, I., Woods, S., Ritger, K. A., McPherson, T. D., Black, S. R., Sparrow, L., Fricchione, M. J., Kerins, J. L., Pacilli, M., Ruestow, P. S., Arwady, M. A., Beavers, S. F., Payne, D. C., Kirking, H. L., & Layden, J. E. (2020). Community Transmission of SARS-CoV-2 at Two Family Gatherings—Chicago, Illinois, February–March 2020. MMWR. Morbidity and Mortality Weekly Report, 69(15), 446–450. https://doi.org/10.15585/mmwr.mm6915e1
-
-
www.thelancet.com www.thelancet.com
-
Yong, S. E. F., Anderson, D. E., Wei, W. E., Pang, J., Chia, W. N., Tan, C. W., Teoh, Y. L., Rajendram, P., Toh, M. P. H. S., Poh, C., Koh, V. T. J., Lum, J., Suhaimi, N.-A. M., Chia, P. Y., Chen, M. I.-C., Vasoo, S., Ong, B., Leo, Y. S., Wang, L., & Lee, V. J. M. (2020). Connecting clusters of COVID-19: An epidemiological and serological investigation. The Lancet Infectious Diseases, S1473309920302735. https://doi.org/10.1016/S1473-3099(20)30273-5
-
-
www.medrxiv.org www.medrxiv.org
-
Zhu, Y., Bloxham, C. J., Hulme, K. D., Sinclair, J. E., Tong, Z. W. M., Steele, L. E., Noye, E. C., Lu, J., Chew, K. Y., Pickering, J., Gilks, C., Bowen, A. C., & Short, K. R. (2020). Children are unlikely to have been the primary source of household SARS-CoV-2 infections [Preprint]. Epidemiology. https://doi.org/10.1101/2020.03.26.20044826
-