- Jul 2020
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twitter.com twitter.com
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Darren L Dahly, PhD on Twitter: “Share this with whoever needs to hear it: ‘risk factors’, even strong ones that are robustly identified, are almost never useful for making actionable predictions, especially when considered in isolation. TLDR: [Big OR] does not equal [Good, Useful, or Easy Decision]” / Twitter. (n.d.). Twitter. Retrieved July 11, 2020, from https://twitter.com/statsepi/status/1249680569463721984
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projecteuclid.org projecteuclid.org
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Shmueli, G. (2010). To Explain or to Predict? Statistical Science, 25(3), 289–310.
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psyarxiv.com psyarxiv.com
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Tijdink, J. K., Luykx, J. J., van Veen, S., Vinkers, C., & Veltman, E. (2020). Challenging COVID-19 times for older psychiatric patients: Potential implications and solutions [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/z4puv
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www.ox.ac.uk www.ox.ac.uk
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Oxford leads development of risk prediction model for more tailored COVID-19 shielding advice | University of Oxford. (n.d.). Retrieved 23 June 2020, from http://www.ox.ac.uk/news/2020-06-22-oxford-leads-development-risk-prediction-model-more-tailored-covid-19-shielding
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www.jclinepi.com www.jclinepi.com
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Sperrin, M., Martin, G. P., Sisk, R., & Peek, N. (2020). Missing data should be handled differently for prediction than for description or causal explanation. Journal of Clinical Epidemiology, 0(0). https://doi.org/10.1016/j.jclinepi.2020.03.028
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www.nytimes.com www.nytimes.com
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Hubler, S. (2020, June 28). ‘We Could Be Feeling This for the Next Decade’: Virus Hits College Towns. The New York Times. https://www.nytimes.com/2020/06/28/us/coronavirus-college-towns.html
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- Jun 2020
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twitter.com twitter.com
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A Marm Kilpatrick on Twitter: “What is #herdimmunity threshold for #COVID19? A topic of much discussion due to large uncertainty & huge consequences for long term impact. New paper illustrates 1 key aspect that can reduce it substantially (spoiler: we still don’t know value). Thread. https://t.co/Sgrg1yrlOq” / Twitter. (n.d.). Twitter. Retrieved June 27, 2020, from https://twitter.com/diseaseecology/status/1275595167936868352
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twitter.com twitter.com
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(((Howard Forman))) on Twitter: “Based on current rate of daily case growth (which I est. at 3.4%), I am predicting that we will have 100k cases PER DAY on or around July 24 if we don’t do something drastic AND soon! Even with measures taken, we will almost certainly still achieve that level, though in August. https://t.co/JBvQ1OzFuI” / Twitter. (n.d.). Twitter. Retrieved June 27, 2020, from https://twitter.com/thehowie/status/1276333155524501504
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www.newscientist.com www.newscientist.com
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Liverpool, J. H., Adam Vaughan, Conrad Quilty-Harper and Layal. (n.d.). Covid-19 news: UK health leaders warn of “real risk” of a second wave. New Scientist. Retrieved June 25, 2020, from https://www.newscientist.com/article/2237475-covid-19-news-uk-health-leaders-warn-of-real-risk-of-a-second-wave/
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www.nature.com www.nature.com
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Saltelli, A., Bammer, G., Bruno, I., Charters, E., Di Fiore, M., Didier, E., Nelson Espeland, W., Kay, J., Lo Piano, S., Mayo, D., Pielke Jr, R., Portaluri, T., Porter, T. M., Puy, A., Rafols, I., Ravetz, J. R., Reinert, E., Sarewitz, D., Stark, P. B., … Vineis, P. (2020). Five ways to ensure that models serve society: A manifesto. Nature, 582(7813), 482–484. https://doi.org/10.1038/d41586-020-01812-9
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www.theguardian.com www.theguardian.com
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Helm, T., McKie, R., & Sodha, S. (2020, June 20). School closures “will trigger UK child mental health crisis.” The Observer. https://www.theguardian.com/education/2020/jun/20/school-closures-will-trigger-uk-child-mental-health-crisis
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twitter.com twitter.com
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Twitter. (n.d.). Twitter. Retrieved June 22, 2020, from https://twitter.com/JASPStats/status/1274764017752592384
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psyarxiv.com psyarxiv.com
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Antonakis, J., Bastardoz, N., & Jacquart, P. (2020). In praise of the impact factor [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/h4p9e
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www.weforum.org www.weforum.org
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How COVID-19 revealed 3 critical AI procurement blindspots. (n.d.). World Economic Forum. Retrieved June 22, 2020, from https://www.weforum.org/agenda/2020/06/how-covid-19-revealed-3-critical-blindspots-ai-governance-procurement/
Tags
- chatbots
- is:blog
- lang:en
- COVID-19
- AI
- risk
- diligence
- fairness
- app
- transparency
- diagnostics
- prediction
- procurement
- citation
- contact tracing
- blindspot
Annotators
URL
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www.jclinepi.com www.jclinepi.com
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van Smeden, M., Groenwold, R.H.H., & Moons, K. GM. (2020, June 18). A cautionary note on the use of the missing indicator method for handling missing data in prediction research. Journal of Clinical Epidemiology. https://doi.org/10.1016/j.jclinepi.2020.06.007
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psyarxiv.com psyarxiv.com
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Zickfeld, J., Schubert, T. W., Herting, A. K., Grahe, J. E., & Faasse, K. (2020, April 16). Predictors of Health-Protective Behavior and Changes Over Time During the Outbreak of the COVID-19 Pandemic in Norway. https://doi.org/10.31234/osf.io/6vgf4
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psyarxiv.com psyarxiv.com
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Abdelrahman, M. K. (2020, April 14). Personality Traits, Risk Perception and Social Distancing During COVID-19. https://doi.org/10.31234/osf.io/6g7kh
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www.newscientist.com www.newscientist.com
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Hamzelou, J. (2020, June 17). How many of us are likely to have caught the coronavirus so far? New Scientist. https://www.newscientist.com/article/mg24632873-000-how-many-of-us-are-likely-to-have-caught-the-coronavirus-so-far/
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Pickford, J. (2020, June 16). UK creative industries risk ‘cultural catastrophe’ without support. https://www.ft.com/content/aa93b17b-55ea-4273-aff0-80b6d92c2c8d
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royalsociety.org royalsociety.org
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Rapid Assistance in Modelling the Pandemic: RAMP | Royal Society. (n.d.). Retrieved June 14, 2020, from https://royalsociety.org/topics-policy/Health-and-wellbeing/ramp/
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eml.berkeley.edu eml.berkeley.edu
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DellaVigna, S & Linos E. (2020). RCTs to scale: Comprehensive evidence from two nudge units. UC Berkeley. https://eml.berkeley.edu/~sdellavi/wp/NudgeToScale2020-03-20.pdf
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fivethirtyeight.com fivethirtyeight.com
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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/
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Wise, J. (2020). Covid-19: Surveys indicate low infection level in community. BMJ, m1992. https://doi.org/10.1136/bmj.m1992
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www.researchgate.net www.researchgate.net
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Mclachlan, S., Lucas, P., Kudakwashe Dube, Hitman, G. A., Osman, M., Kyrimi, E., Neil, M., & Fenton, N. E. (2020). The fundamental limitations of COVID-19 contact tracing methods and how to resolve them with a Bayesian network approach. https://doi.org/10.13140/RG.2.2.27042.66243
Tags
- app
- likelihood
- containment
- network model
- Bayesian
- prediction
- is:preprint
- lang:en
- COVID-19
- limitation
- contact tracing
- digital solution
Annotators
URL
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www.tandfonline.com www.tandfonline.com
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Efron, B. (2020). Prediction, Estimation, and Attribution. Journal of the American Statistical Association, 115(530), 636–655. https://doi.org/10.1080/01621459.2020.1762613
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www.healthdata.org www.healthdata.org
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COVID-19 estimate downloads. (2020, April 2). Institute for Health Metrics and Evaluation. http://www.healthdata.org/covid/data-downloads
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twitter.com twitter.com
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Angner, E. (2020, May 11). "Terrific assessment of projections of demand for Swedish ICU beds. The first two panels are model-based projections by academics; the third is a simple extrapolation by the public-health authority; the fourth is the actual outcome /1." Twitter. https://twitter.com/SciBeh/status/1260121561861939200
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psyarxiv.com psyarxiv.com
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Sjåstad, H., Teigen, K. H., & Van Bavel, J. J. (2020, June 5). The best-case heuristic in risk prediction: Hopes and fears in a global health pandemic (COVID-19). https://doi.org/10.31234/osf.io/pcj4f
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psyarxiv.com psyarxiv.com
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Mandel, D. R. (2020). Studies Past and Future of the Past and Future [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/qyk58
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arxiv.org arxiv.org
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Kempfert, K., Martinez, K., Siraj, A., Conrad, J., Fairchild, G., Ziemann, A., Parikh, N., Osthus, D., Generous, N., Del Valle, S., & Manore, C. (2020). Time Series Methods and Ensemble Models to Nowcast Dengue at the State Level in Brazil. ArXiv:2006.02483 [q-Bio, Stat]. http://arxiv.org/abs/2006.02483
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psyarxiv.com psyarxiv.com
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McDonald, K., Ding, T., French, P., Jones, P. B., Baio, G., Kirkbride, J. B., & Wohland, P. (2020, April 27). Forecasting population need for mental health care: a Bayesian methodology applied to the epidemiology of psychotic disorders in England. Retrieved from psyarxiv.com/bvcgu
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medium.com medium.com
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Pigliucci, M. (2020, May 19). No, predictions are not overrated. On some scientists’ strange attitude toward philosophy. Medium. https://medium.com/science-and-philosophy/no-predictions-are-not-overrated-on-some-scientists-strange-attitude-toward-philosophy-60dfd5c2cb83
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www.houstonchronicle.com www.houstonchronicle.com
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Blackman, J., & Goldenstein, T. (2020, May 21). Using cellphone data, national study predicts huge June spike in Houston coronavirus cases. HoustonChronicle.Com. https://www.houstonchronicle.com/news/houston-texas/houston/article/Using-cellphone-data-national-study-predicts-15286096.php
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arxiv.org arxiv.org
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Poiitis, M., Vakali, A., & Kourtellis, N. (2020). On the Aggression Diffusion Modeling and Minimization in Online Social Networks. ArXiv:2005.10646 [Physics]. http://arxiv.org/abs/2005.10646
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bmcmedresmethodol.biomedcentral.com bmcmedresmethodol.biomedcentral.com
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Boulesteix, A., Strobl, C. Optimal classifier selection and negative bias in error rate estimation: an empirical study on high-dimensional prediction. BMC Med Res Methodol 9, 85 (2009). https://doi.org/10.1186/1471-2288-9-85
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nymag.com nymag.com
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Walsh, J. D. (2020, May 11). The Coming Disruption to College. Intelligencer. https://nymag.com/intelligencer/2020/05/scott-galloway-future-of-college.html
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psyarxiv.com psyarxiv.com
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Witt, S., Seehagen, S., & Zmyj, N. (2020). Stress affects the prediction of others’ behavior [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/jbswq
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psyarxiv.com psyarxiv.com
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Sun, R., Balabanova, A., Bajada, C. J., Liu, Y., Kriuchok, M., Voolma, S., … Fadhlia, T. N. (2020, June 2). Measuring emotion experience and wellbeing during COVID19 across the world. https://doi.org/10.31234/osf.io/r7xaz
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www.metascience2019.org www.metascience2019.org
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Yang Yang: The Replicability of Scientific Findings Using Human and Machine Intelligence (Video). Metascience 2019 Symposium. https://www.metascience2019.org/presentations/yang-yang/
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www.pnas.org www.pnas.org
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Yang, Y., Youyou, W., & Uzzi, B. (2020). Estimating the deep replicability of scientific findings using human and artificial intelligence. Proceedings of the National Academy of Sciences, 117(20), 10762–10768. https://doi.org/10.1073/pnas.1909046117
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ftalphaville.ft.com ftalphaville.ft.com
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Is the “science” behind the lockdown any good? (n.d.). Financial Times. Retrieved June 2, 2020, from http://ftalphaville.ft.com/2020/05/21/1590091709000/It-s-all-very-well--following-the-science---but-is-the-science-any-good--/
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- May 2020
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Yuan, L. (2020, May 27). Amnesia Nation: Why China Has Forgotten Its Coronavirus Outbreak. The New York Times. https://www.nytimes.com/2020/05/27/business/china-coronavirus-amnesia.html
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www.medrxiv.org www.medrxiv.org
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Arenas, A., Cota, W., Gomez-Gardenes, J., Gomez, S., Granell, C., Matamalas, J. T., Soriano-Panos, D., & Steinegger, B. (2020). Derivation of the effective reproduction number R for COVID-19 in relation to mobility restrictions and confinement [Preprint]. Epidemiology. https://doi.org/10.1101/2020.04.06.20054320
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www.thelancet.com www.thelancet.com
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Hargreaves, J., Davey, C., Hargreaves, J., Davey, C., Auerbach, J., Blanchard, J., Bond, V., Bonell, C., Burgess, R., Busza, J., Colbourn, T., Cowan, F., Doyle, A., Hakim, J., Hensen, B., Hosseinipour, M., Lin, L., Johnson, S., Masuka, N., … Yekeye, R. (2020). Three lessons for the COVID-19 response from pandemic HIV. The Lancet HIV, S2352301820301107. https://doi.org/10.1016/S2352-3018(20)30110-7
Tags
- infection
- policy
- school closure
- pharmaceutical
- HIV
- government
- poverty
- gender
- physical distancing
- trajectory
- lang:en
- COVID-19
- health equity
- inequality
- quarentine
- socioeconomic status
- collective behavior
- is:article
- response
- vaccine
- prediction
- social distancing
- health system
- behavior
Annotators
URL
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www.futurelearn.com www.futurelearn.com
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FutureLearn. Pandemics, Modelling, and Policy—Online Course. https://www.futurelearn.com/courses/pandemics-modelling-and-policy
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psyarxiv.com psyarxiv.com
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Clarke, E. J. R., Klas, A., & Dyos, E. (2020, May 15). Using RWASDO Subfactors to Predict Reactions to COVID-19 Restrictions in Australia. https://doi.org/10.31234/osf.io/b56qj
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www.theguardian.com www.theguardian.com
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Boseley, S. (2020, May 12). Millions with health conditions at risk from Covid-19 “if forced back to work.” The Guardian. https://www.theguardian.com/world/2020/may/12/millions-with-health-conditions-at-risk-from-covid-19-if-forced-back-to-work
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psyarxiv.com psyarxiv.com
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Ryan, W., & Evers, E. (2020). Logarithmic Axis Graphs Distort Lay Judgment. https://doi.org/10.31234/osf.io/cwt56
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www.theguardian.com www.theguardian.com
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Dunford, J. (2020, May 28). ‘Things have to change’: Tourism businesses look to a greener future. The Guardian. https://www.theguardian.com/travel/2020/may/28/things-had-to-change-tourism-businesses-look-to-a-greener-future
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www.theguardian.com www.theguardian.com
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Correspondent, H. D. S. (2020, May 27). Cancer undiagnosed “for nearly 2,000 people in UK every week.” The Guardian. https://www.theguardian.com/society/2020/may/28/cancer-undiagnosed-for-nearly-2000-people-in-uk-every-week
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www.repository.cam.ac.uk www.repository.cam.ac.uk
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Toxvaerd, F. M. O. (2020). Equilibrium Social Distancing [Working Paper]. Faculty of Economics, University of Cambridge. https://doi.org/10.17863/CAM.52489
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www.nature.com www.nature.com
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Zdeborová, L. (2020). Understanding deep learning is also a job for physicists. Nature Physics, 1–3. https://doi.org/10.1038/s41567-020-0929-2
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www.digital-democracy.org www.digital-democracy.org
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By putting our data in the corporate cloud, we are that kid. We are giving corporations and governments a way to see where we are looking towards, and they can predict our future and decide whether and how to intervene or subvert.
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Imagine you’re in a room with a newly ambulatory toddler who has just locked their sights into something, you can see their eyes, and you know their future and where they are heading in the minutes ahead.
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twitter.com twitter.com
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Carl T. Bergstrom on Twitter
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www.thelancet.com www.thelancet.com
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Schwalbe, N., & Wahl, B. (2020). Artificial intelligence and the future of global health. The Lancet, 395(10236), 1579–1586. https://doi.org/10.1016/S0140-6736(20)30226-9
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www.washingtonpost.com www.washingtonpost.com
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Thebault, R., & Hauslohner, A. (2020, May 24). A deadly ‘checkerboard’: Covid-19’s new surge across rural America. Washington Post. https://www.washingtonpost.com/nation/2020/05/24/coronavirus-rural-america-outbreaks/
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journals.sagepub.com journals.sagepub.com
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Batty, M. (2020). The Coronavirus crisis: What will the post-pandemic city look like?: Environment and Planning B: Urban Analytics and City Science, 47(4), https://doi.org/10.1177/2399808320926912
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psyarxiv.com psyarxiv.com
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Gollwitzer, A., Martel, C., Brady, W. J., Knowles, E., & Van Bavel, J. J. (2020). Partisan Differences in Physical Distancing Predict Infections and Mortality During the Coronavirus Pandemic [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/t3yxa
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www.washingtonpost.com www.washingtonpost.com
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Stevens, H. & Muyskens, J. (2020 May 14). See how experts use disease modeling to predict coronavirus cases after states reopen. Washington Post. https://www.washingtonpost.com/graphics/2020/health/disease-modeling-coronavirus-cases-reopening/
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science.sciencemag.org science.sciencemag.org
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Cobey, S. (2020). Modeling infectious disease dynamics. Science, 368(6492), 713–714. https://doi.org/10.1126/science.abb5659
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en.wikipedia.org en.wikipedia.org
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human beings use their intellect to guide their actions and predict the consequences of their actions
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As it is possible to deduce another's intentions, the assumption of agency allows one to extrapolate from those intentions what actions someone else is likely to perform.
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www.nyteknik.se www.nyteknik.se
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Forskare: ”Se upp med komplexa coronamodeller – de kan överträffa verkligheten”. (2020 April 24). Ny Teknik. https://www.nyteknik.se/opinion/forskare-se-upp-med-komplexa-coronamodeller-de-kan-overtraffa-verkligheten-6994339
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arxiv.org arxiv.org
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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
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psyarxiv.com psyarxiv.com
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Zhang, S., Sun, S., Jahanshahi, A. A., Alvarez-Risco, A., Ibarra, V. G., Li, J., & Patty-Tito, R. M. (2020, May 19). Measuring COVID-19 Organizational Support of Individual Healthcare Workers – A Test with 712 Healthcare Workers in Peru, Ecuador, and Bolivia. https://doi.org/10.31234/osf.io/wpcf4
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psyarxiv.com psyarxiv.com
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Leary, A., Dvorak, R., De Leon, A., Peterson, R., & Troop-Gordon, W. (2020). COVID-19 Social Distancing [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/mszw2
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Kennedy, B., Atari, M., Davani, A. M., Hoover, J., Omrani, A., Graham, J., & Dehghani, M. (2020, May 7). Moral Concerns are Differentially Observable in Language. https://doi.org/10.31234/osf.io/uqmty
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www.nature.com www.nature.com
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Vespignani, A., Tian, H., Dye, C. et al. Modelling COVID-19. Nat Rev Phys (2020). https://doi.org/10.1038/s42254-020-0178-4
Tags
- infection
- policy
- challenge
- epidemiology
- pharmaceutical
- forecast
- China
- containment measures
- lang:en
- COVID-19
- complex network
- transmission dynamics
- emergency
- quarentine
- war time
- is:article
- public health
- modeling
- mathematics
- antibody testing
- prediction
- superspreading
- computational modeling
- contact tracing
- intervention
- open data
- isolation
Annotators
URL
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Fast Science and Philosophy of Science | Jacob Stegenga. (2020, May 11). BSPS. http://www.thebsps.org/auxhyp/fast-science-stegenga/
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psyarxiv.com psyarxiv.com
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Fischer, H., & Said, N. (2020, May 12). Metacognition_ClimateChange_Fischer&Said_Preprint. https://doi.org/10.31234/osf.io/fd6gy
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psyarxiv.com psyarxiv.com
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Trueblood, J., Sussman, A., O'Leary, D., & Holmes, W. (2020, April 21). A Tale of Two Crises: Financial Fragility and Beliefs about the Spread of COVID-19. https://doi.org/10.31234/osf.io/xfrz3
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www.iubenda.com www.iubenda.com
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It’s important to note that where the GDPR applies, intended use factors into whether or not consent is required as even statistical data can fall under “profiling” or “monitoring” depending on how the data is being used.
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www.thelancet.com www.thelancet.com
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Wells, C. R., Stearns, J. K., Lutumba, P., & Galvani, A. P. (2020). COVID-19 on the African continent. The Lancet Infectious Diseases, 0(0). https://doi.org/10.1016/S1473-3099(20)30374-1
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www.replicationmarkets.com www.replicationmarkets.com
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Replication Markets – Reliable research replicates…you can bet on it. (n.d.). Retrieved May 7, 2020, from https://www.replicationmarkets.com/
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psyarxiv.com psyarxiv.com
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Bondy, E., Baranger, D. A., Balbona, J. V., Sputo, K., Paul, S. E., Oltmanns, T., & Bogdan, R. (2020, April 30). Neuroticism and reward-related ventral striatum activity: Probing vulnerability to stress-related depression. Retrieved from psyarxiv.com/5wd3k
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www.fil.ion.ucl.ac.uk www.fil.ion.ucl.ac.uk
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Friston, K. J., Parr, T., Zeidman, P., Razi, A., Flandin, G., Daunizeau, J., Hulme, O. J., Billig, A. J., Litvak, V., Moran, R. J., Price, C. J., & Lambert, C. (2020). Dynamic causal modelling of COVID-19. ArXiv:2004.04463 [q-Bio]. http://arxiv.org/abs/2004.04463
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www.iubenda.com www.iubenda.com
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In case you’re implementing any ADM process, you have to tell your users.
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gdpr-info.eu gdpr-info.eu
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Tags
Annotators
URL
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psyarxiv.com psyarxiv.com
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Gollwitzer, A., Martel, C., Marshall, J., Höhs, J. M., & Bargh, J. A. (2020, May 5). Connecting Self-Reported Social Distancing to Real-World Behavior at the Individual and U.S. State Level. https://doi.org/10.31234/osf.io/kvnwp
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Jach, H., & Smillie, L. (2020, May 5). Testing the Information-Seeking Theory of Openness/Intellect. https://doi.org/10.31234/osf.io/zqcjw
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statmodeling.stat.columbia.edu statmodeling.stat.columbia.edu
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Statistical Modeling, Causal Inference, and Social Science. (2020 April 22). Blog Post: New analysis of excess coronavirus mortality; also a question about poststratification. https://statmodeling.stat.columbia.edu/2020/04/22/analysis-of-excess-coronavirus-mortality-also-a-question-about-poststratification/
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psyarxiv.com psyarxiv.com
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Pieper, D. (2020, May 1). Challenging social systems under the threat of pollution: Replication and extension of Eadeh and Chang (2019). Retrieved from psyarxiv.com/axbj4
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psyarxiv.com psyarxiv.com
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Di Giorgio, E., Di Riso, D., Mioni, G., & Cellini, N. (2020, April 30). The interplay between mothers’ and children behavioral and psychological factors during COVID-19: An Italian study. Retrieved from psyarxiv.com/dqk7h
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psyarxiv.com psyarxiv.com
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Horstmann, K. T., Rauthmann, J. F., Sherman, R. A., & Ziegler, M. (2020, April 30). Unveiling an Exclusive Link: Predicting Behavior with Personality, Situation Perception, and Affect in a Pre-Registered Experience Sampling Study. Retrieved from psyarxiv.com/ztw2n
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psyarxiv.com psyarxiv.com
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Barrafrem, K., Västfjäll, D., & Tinghög, G. (2020, April 30). Financial well-being, COVID-19, and the financial better-than-average-effect. Retrieved from psyarxiv.com/tkuaf
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- Apr 2020
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psyarxiv.com psyarxiv.com
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Romano, A., Sotis, C., Dominioni, G., & Guidi, S. (2020). COVID-19 Data: The Logarithmic Scale Misinforms the Public and Affects Policy Preferences [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/42xfm
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Leitner, S. (2020, April 18). On the dynamics emerging from pandemics and infodemics. https://doi.org/10.31234/osf.io/nqru6
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psyarxiv.com psyarxiv.com
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Wolff, W., Martarelli, C., Schüler, J., & Bieleke, M. (2020, April 17). High boredom proneness and low trait self-control impair adherence to social distancing guidelines during the COVID-19 pandemic. https://doi.org/10.31234/osf.io/jcf95
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Etilé, F., Johnston, D., Frijters, P., & Shields, M. (2020, April 16). Psychological Resilience to Major Socioeconomic Life Events. https://doi.org/10.31234/osf.io/vp48c
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psyarxiv.com psyarxiv.com
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Cavojova, V., Šrol, J., & Mikušková, E. B. (2020, April 15). Scientific reasoning as a predictor of health-related beliefs and behaviors in the time of COVID-19. https://doi.org/10.31234/osf.io/tfy5q
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psyarxiv.com psyarxiv.com
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Conway, L. G., III, Woodard, S. R., Zubrod, A., & Chan, L. (2020, April 13). Why are Conservatives Less Concerned about the Coronavirus (COVID-19) than Liberals? Testing Experiential Versus Political Explanations. https://doi.org/10.31234/osf.io/fgb84
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Rosenfeld, D. L. (2020, April 22). Political Ideology and the Outbreak of COVID-19 in the United States. https://doi.org/10.31234/osf.io/jrpfd
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Han, L., Lin, Z., Tang, M., Zhou, J., Zou, Y., & Guan, S. (2020). Impact of contact preference on social contagions on complex networks. Physical Review E, 101(4), 042308. https://doi.org/10.1103/PhysRevE.101.042308
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www.cdc.gov www.cdc.gov
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CDC. (2020, February 11). Coronavirus Disease 2019 (COVID-19). Centers for Disease Control and Prevention. https://www.cdc.gov/coronavirus/2019-ncov/covid-data/forecasting-us.html
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fivethirtyeight.com fivethirtyeight.com
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Boice, J. (2020 March 26). Experts say the coronavirus outlook has worsened, but the trajectory is still unclear. FiveThirtyEight. https://fivethirtyeight.com/features/experts-say-the-coronavirus-outlook-has-worsened-but-the-trajectory-is-still-unclear/
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psyarxiv.com psyarxiv.com
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Kirkegaard, E., Taji, W., & Gerritsen, A. (2020, April 5). Predicting a Pandemic: testing crowd wisdom and expert forecasting amidst the novel COVID-19 outbreak. https://doi.org/10.31234/osf.io/2d75g
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arxiv.org arxiv.org
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Taleb, N. N. (2019). On the Statistical Differences between Binary Forecasts and Real World Payoffs. ArXiv:1907.11162 [Physics, q-Fin]. http://arxiv.org/abs/1907.11162
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arxiv.org arxiv.org
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Liu, D., Clemente, L., Poirier, C., Ding, X., Chinazzi, M., Davis, J. T., Vespignani, A., & Santillana, M. (2020). A machine learning methodology for real-time forecasting of the 2019-2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic models. ArXiv:2004.04019 [Cs, q-Bio, Stat]. http://arxiv.org/abs/2004.04019
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www.bmj.com www.bmj.com
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Wynants, L., Van Calster, B., Bonten, M. M. J., Collins, G. S., Debray, T. P. A., De Vos, M., Haller, M. C., Heinze, G., Moons, K. G. M., Riley, R. D., Schuit, E., Smits, L. J. M., Snell, K. I. E., Steyerberg, E. W., Wallisch, C., & van Smeden, M. (2020). Prediction models for diagnosis and prognosis of covid-19 infection: Systematic review and critical appraisal. BMJ, m1328. https://doi.org/10.1136/bmj.m1328
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ij-healthgeographics.biomedcentral.com ij-healthgeographics.biomedcentral.com
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Kamel Boulos, M.N., Geraghty, E.M. Geographical tracking and mapping of coronavirus disease COVID-19/severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic and associated events around the world: how 21st century GIS technologies are supporting the global fight against outbreaks and epidemics. Int J Health Geogr 19, 8 (2020). https://doi.org/10.1186/s12942-020-00202-8
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Resnick, B. (2020 April 10). Why it's so hard to see into the future of Covid-19. Vox. https://www.vox.com/science-and-health/2020/4/10/21209961/coronavirus-models-covid-19-limitations-imhe
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arxiv.org arxiv.org
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Kerzendorf, W. E., Patat, F., Bordelon, D., van de Ven, G., & Pritchard, T. A. (2020). Distributed peer review enhanced with natural language processing and machine learning. Nature Astronomy. https://doi.org/10.1038/s41550-020-1038-y
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psyarxiv.com psyarxiv.com
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Plohl, N., & Musil, B. (2020, April 6). Modeling compliance with COVID-19 prevention guidelines: The critical role of trust in science. https://doi.org/10.31234/osf.io/6a2cx
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science.sciencemag.org science.sciencemag.org
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Kissler, S. M., Tedijanto, C., Goldstein, E., Grad, Y. H., & Lipsitch, M. (2020). Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period. Science, eabb5793. https://doi.org/10.1126/science.abb5793
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www.cell.com www.cell.com
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Callaghan, S. (2020). COVID-19 Is a Data Science Issue. Patterns, 100022. https://doi.org/10.1016/j.patter.2020.100022
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cmmid.github.io cmmid.github.io
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Russel, T.W., Hellewell, J., Abbott, S., Golding, N., Gibbs, H., Jarvis, C.I., van Zandvoort, K., Flasche, S., Eggo, R., Edmunds, W.J., Kucharski, A.J., (2020). Using a delay-adjusted case fatality ratio to estimate under-reporting. CMMID. https://cmmid.github.io/topics/covid19/severity/global_cfr_estimates.html
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www.nature.com www.nature.com
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Antov, M.I., Plog, E., Bierwirth, P. et al. Visuocortical tuning to a threat-related feature persists after extinction and consolidation of conditioned fear. Sci Rep 10, 3926 (2020). https://doi.org/10.1038/s41598-020-60597-z
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Punn, N. S., Sonbhadra, S. K., & Agarwal, S. (2020). COVID-19 Epidemic Analysis using Machine Learning and Deep Learning Algorithms [Preprint]. Health Informatics. https://doi.org/10.1101/2020.04.08.20057679
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www.washingtonpost.com www.washingtonpost.com
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Bump, P., Wan, W. (2020 April 8). A leading model now estimates tens of thousands fewer covid-19 deaths by summer. The Washington Post. https://www.washingtonpost.com/politics/2020/04/08/leading-model-now-estimates-tens-thousands-fewer-covid-19-deaths-by-summer/
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users.ox.ac.uk users.ox.ac.uk
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Bird, S., Nielsen, B. (2020 April 20). Now-casting of Covid-19 deaths in English Hospitals. http://users.ox.ac.uk/~nuff0078/Covid/index.htm
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Watts, D. J., Beck, E. D., Bienenstock, E. J., Bowers, J., Frank, A., Grubesic, A., Hofman, J., Rohrer, J. M., & Salganik, M. (2018). Explanation, prediction, and causality: Three sides of the same coin? [Preprint]. Open Science Framework. https://doi.org/10.31219/osf.io/u6vz5
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www.pnas.org www.pnas.org
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Salganik, M. J., Lundberg, I., Kindel, A. T., Ahearn, C. E., Al-Ghoneim, K., Almaatouq, A., Altschul, D. M., Brand, J. E., Carnegie, N. B., Compton, R. J., Datta, D., Davidson, T., Filippova, A., Gilroy, C., Goode, B. J., Jahani, E., Kashyap, R., Kirchner, A., McKay, S., … McLanahan, S. (2020). Measuring the predictability of life outcomes with a scientific mass collaboration. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1915006117
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jamanetwork.com jamanetwork.com
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Jewell, N. P., Lewnard, J. A., & Jewell, B. L. (2020). Predictive Mathematical Models of the COVID-19 Pandemic: Underlying Principles and Value of Projections. JAMA. https://doi.org/10.1001/jama.2020.6585
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- Feb 2020
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www.fmassari.com www.fmassari.com
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The rational expectation and thelearning-from-price literatures argue that equilibrium prices are accurate becausethey reveal and aggregate the information of all market participants. The MarketSelection Hypothesis,MSH, proposes instead that prices become accurate becausethey eventually reflect only the beliefs of the most accurate agent. The Wisdomof the Crowd argument,WOC, however suggests that market prices are accuratebecause individual, idiosyncratic errors are averaged out by the price formationmechanism
Three models (arguments for) drivers of market efficiency
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www.sciencedirect.com www.sciencedirect.com
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0.506 (0.532)
can we find any measures of dispersion here?
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For the 12 studies with an original p < 0.005, 10 (83%) replicated. For the 12 studies with an original p > 0.005, only 1 (8%) replicated. Further work is needed to test if prediction markets outperform predictions based only on the initial p-value, to test if the market also aggregates other information important for reproducibility.
p values may capture all the information?
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- Dec 2019
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www.oraclum.co.uk www.oraclum.co.uk
- Oct 2019
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conference.nber.org conference.nber.org
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We live in an age of paradox. Systems using artificial intelligence match or surpass human level performance in more and more domains, leveraging rapid advances in other technologies and driving soaring stock prices. Yet measured productivity growth has fallen in half over the past decade, and real income has stagnated since the late 1990s for a majority of Americans. Brynjolfsson, Rock, and Syverson describe four potential explanations for this clash of expectations and statistics: false hopes, mismeasurement, redistribution, and implementation lags. While a case can be made for each explanation, the researchers argue that lags are likely to be the biggest reason for paradox. The most impressive capabilities of AI, particularly those based on machine learning, have not yet diffused widely. More importantly, like other general purpose technologies, their full effects won't be realized until waves of complementary innovations are developed and implemented. The adjustment costs, organizational changes and new skills needed for successful AI can be modeled as a kind of intangible capital. A portion of the value of this intangible capital is already reflected in the market value of firms. However, most national statistics will fail to capture the full benefits of the new technologies and some may even have the wrong sign
This is for anyone who is looking deep in economics of artificial intelligence or is doing a project on AI with respect to economics. This paper entails how AI might effect our economy and change the way we think about work. the predictions and facts which are stated here are really impressive like how people 30 years from now will be lively with government employment where everyone will get equal amount of payment.
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- Jun 2019
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fairmodel.econ.yale.edu fairmodel.econ.yale.edu
- May 2019
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www.pwc.com www.pwc.com
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By 2050, the world’s urban population will have increased by some 44%
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- Oct 2018
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We opt for an embedding method, since wehypothesize less independent behaviors than individuals inthe system. Embedding methods are especially adapted toproducepersonalizedpredictions (e.g. collaborative filteringapplications), by making the assumption that the behaviorfrom an individual can be predicted by collecting data frommany users
choice of embedding methods
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www.sciencedirect.com www.sciencedirect.com
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Motif models for RNA-binding proteins
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- Sep 2018
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link.springer.com link.springer.com
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Estimating the prevalence of functional exonic splice regulatory information
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academic.oup.com academic.oup.com
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Prediction of clustered RNA-binding protein motif sites in the mammalian genome
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www.mnemotext.com www.mnemotext.com
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We’ve got lots of telephones already. Can’t you think of anything else for your birthday? Something very special?
This part of the dialogue creates a good view of how consumerism will be just as prominent as today if not more. when he says we've got a lot of telephones, it might suggest that they are extremely reliant on technology so in a sense the movie had correctly predicted our current addiction and reliance on mobile phones.
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- Mar 2018
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www.rc.umd.edu www.rc.umd.edu
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But I lived, and was to live for ever!
Maybe it's because he only drank half that he is (maybe) starting to age now.
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- Oct 2017
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www.mnemotext.com www.mnemotext.com
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All our steps in creating or absorbing material of the record proceed through one of the senses—the tactile when we touch keys, the oral when we speak or listen, the visual when we read. Is it not possible that some day the path may be established more directly?
Throughout reading this article I couldn't help but laugh at the dramatic irony of our experience as students in 2017 reading Bush's predictions for the future of mechanics and technology in 1991. He speaks with a bit of wonderment, obviously trying to shock the reader with ideas considered fantastical at the time and then undermining them as "not so fantastic;" the joke of course being that even his most fantastic ideas seem rudimentary to us. That is, until I reached this part of the article, where Bush's predictions seem to have closely aligned with those of our own in the modern era. In this quote he seems to suggest a total departure from the tactile, oral, visual, and so on. Whether explicitly or not, Bush is referencing transcendence of machine (mechanical or digital) which, when you think about it, is a "fantastic" notion even by our standards of technology in 2017.
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- Sep 2017
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www.thenation.com www.thenation.com
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customer relations, where live emotion estimation could be used when a customer phones into a call center.
Mark Andrejevic has an interesting article about this kind of cross-over between homeland security and fields like marketing and advertising. He calls the use of emotional data in sentiment analysis and predictive analytics "affective economics."
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sites.google.com sites.google.com
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His head being turned back, he passed a crook of the road, and looking forward again, beheld the figure of a man, in grave and decent attire, seated at the foot of an old tree. He arose, at Goodman Brown's approach, and walked onward, side by side with him.
The traveler could be a work acquaintance. In the 2 sentences afterwards, by context clues, they seem to know each other fair enough by not only name but also of Brown's wife whom in past text had said they're only 3 months married.
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- Oct 2016
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americanliterature.com americanliterature.com
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She clenched her fists
she is a little emotional. i think she will kill him.
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A cuckoo clock!
I predict that it is not a normal cuckoo clock
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- Jul 2016
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earthobservatory.nasa.gov earthobservatory.nasa.gov
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In April 1950, Charney’s group made a series of successful 24-hour forecasts over North America, and by the mid-1950s, numerical forecasts were being made on a regular basis.
Roughly 50 years from initial efforts to first successful forecasts.
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Charney determined that the impracticality of Richardson’s methods could be overcome by using the new computers and a revised set of equations, filtering out sound and gravity waves in order to simplify the calculations and focus on the phenomena of most importance to predicting the evolution of continent-scale weather systems.
The complexity of the forecasting problem was initially overcome in the 1940's both by an improved rate of calculation (using computers) and by simplifying the models to focus on the most important factors.
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Courageously, Richardson reported his results in his book Weather Prediction by Numerical Process, published in 1922.
Despite failing to predict the weather accurately, Richardson posted his results publicly. This is an important step in allowing the improvement of forecasting because it makes it possible to learn what works and what doesn't more quickly. See also Brian McGill's 6th P of Good Prediction
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Despite the advances made by Richardson, it took him, working alone, several months to produce a wildly inaccurate six-hour forecast for an area near Munich, Germany. In fact, some of the changes predicted in Richardson’s forecast could never occur under any known terrestrial conditions.
Nice concise description of the poor performance and impracticality of early weather forecasting.
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arxiv.org arxiv.org
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Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks
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- Jun 2016
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fractastical.github.io fractastical.github.io
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known to be effective: prediction markets
Known by whom? In what circumstances? These are key points.
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- Feb 2016
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www.ncbi.nlm.nih.gov www.ncbi.nlm.nih.gov
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we predicted that there would be diagnosis-by-sex interactions in subcortical structures rich in sex hormone receptors, particularly in the amygdala and hippocampus.
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we predicted, based on previous reports, that youths with BPD would have abnormal NA volumes22,23 and that youths with SZ would have reduced thalamic volumes.24,33 We also predicted that the BPD with psychosis group would share features with the BPD without psychosis and the primary psychotic disorder groups
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