- Dec 2023
- Jul 2023
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The representation of objects in terms of XML elements and attributes.
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Annotators
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- Jan 2023
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www.complexityexplorer.org www.complexityexplorer.org
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https://www.youtube.com/watch?v=HwkRfN-7UWI
Seven Principles of Data Feminism
- Examine power
- Challenge power
- Rethink binaries and hierarchies
- Elevate emotion an embodiment
- Embrace pluralism
- Consider context
- Make labor visible
Abolitionist movement
There are some interesting analogies to be drawn between the abolitionist movement in the 1800s and modern day movements like abolition of police and racial justice, etc.
Topic modeling - What would topic modeling look like for corpuses of commonplace books? Over time?
wrt article: Soni, Sandeep, Lauren F. Klein, and Jacob Eisenstein. “Abolitionist Networks: Modeling Language Change in Nineteenth-Century Activist Newspapers.” Journal of Cultural Analytics 6, no. 1 (January 18, 2021). https://doi.org/10.22148/001c.18841. - Brings to mind the difference in power and invisible labor between literate societies and oral societies. It's easier to erase oral cultures with the overwhelm available to literate cultures because the former are harder to see.
How to find unbiased datasets to study these?
aspirational abolitionism driven by African Americans in the 1800s over and above (basic) abolitionism
Tags
- data science
- abolitionists
- aspirational abolitionism
- dodging the memory hole
- Data Feminism
- Lauren F. Klein
- intersectional feminism
- invisible labor
- defunding police
- operationalization
- power frameworks
- slavery
- Catherine D'Ignazio
- orality vs. literacy
- algorithms
- watch
- emotional labor
- topic modeling
Annotators
URL
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- Nov 2022
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www.prisma.io www.prisma.io
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with Prisma you never create application models in your programming language by manually defining classes, interfaces, or structs. Instead, the application models are defined in your Prisma schema
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- Aug 2022
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psyarxiv.com psyarxiv.com
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Petersen, M. B., Rasmussen, M. S., Lindholt, M. F., & Jørgensen, F. J. (2021). Pandemic Fatigue and Populism: The Development of Pandemic Fatigue during the COVID-19 Pandemic and How It Fuels Political Discontent across Eight Western Democracies. PsyArXiv. https://doi.org/10.31234/osf.io/y6wm4
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- Apr 2022
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twitter.com twitter.com
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James 💙 Neill - 😷 🇪🇺🇮🇪🇬🇧🔶. (2021, December 31). Latest Covid primary diagnosis supplement data, by region. London? #Omicron is #NotMild https://england.nhs.uk/statistics/statistical-work-areas/covid-19-hospital-activity/ https://t.co/gUBo7sTZT5 [Tweet]. @jneill. https://twitter.com/jneill/status/1476856305411366930
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- Mar 2022
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twitter.com twitter.com
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Eric Topol. (2022, February 28). The Omicron crisis in Hong Kong. This is a graph of fatalities, not cases @OurWorldInData https://t.co/JbZCM3SNUq [Tweet]. @EricTopol. https://twitter.com/EricTopol/status/1498327329096933381
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www.nature.com www.nature.com
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Adam, D. (2022). COVID’s true death toll: Much higher than official records. Nature, 603(7902), 562–562. https://doi.org/10.1038/d41586-022-00708-0
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twitter.com twitter.com
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ReconfigBehSci. (2022, February 2). RT @AndreasShrugged: Meta-shmeta analysis. They claim they find that lockdowns reduced mortality in Europe and U.S. only by 0.2%. After bro… [Tweet]. @SciBeh. https://twitter.com/SciBeh/status/1489499513219911686
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- Feb 2022
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twitter.com twitter.com
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Adam Kucharski. (2022, January 18). Below analysis was two years ago (https://bbc.co.uk/news/health-51148303). As well as providing an early warning about the COVID threat, it’s a good illustration of what is often an under-appreciated point: If we want to make sense of epidemic data and dynamics in real-time, we need models… 1/ https://t.co/ZdpzOq3Bzp [Tweet]. @AdamJKucharski. https://twitter.com/AdamJKucharski/status/1483368504392880128
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twitter.com twitter.com
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Yaniv Erlich on Twitter. (n.d.). Twitter. Retrieved February 8, 2022, from https://twitter.com/erlichya/status/1482847821397176325
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www.nature.com www.nature.com
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Adam, D. (2022). Will Omicron end the pandemic? Here’s what experts say. Nature, 602(7895), 20–21. https://doi.org/10.1038/d41586-022-00210-7
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- Jan 2022
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virologydownunder.com virologydownunder.com
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Mackay, A. I. M. & PhD. (2022, January 29). Thank goodness we did all the work. Virology Down Under. https://virologydownunder.com/thank-goodness-we-did-all-the-work/
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www.nature.com www.nature.com
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Zimmerman, M. I., Porter, J. R., Ward, M. D., Singh, S., Vithani, N., Meller, A., Mallimadugula, U. L., Kuhn, C. E., Borowsky, J. H., Wiewiora, R. P., Hurley, M. F. D., Harbison, A. M., Fogarty, C. A., Coffland, J. E., Fadda, E., Voelz, V. A., Chodera, J. D., & Bowman, G. R. (2021). SARS-CoV-2 simulations go exascale to predict dramatic spike opening and cryptic pockets across the proteome. Nature Chemistry, 13(7), 651–659. https://doi.org/10.1038/s41557-021-00707-0
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psyarxiv.com psyarxiv.com
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Budak, C., Soroka, S., Singh, L., Bailey, M., Bode, L., Chawla, N., Davis-Kean, P., Choudhury, M. D., Veaux, R. D., Hahn, U., Jensen, B., Ladd, J., Mneimneh, Z., Pasek, J., Raghunathan, T., Ryan, R., Smith, N. A., Stohr, K., & Traugott, M. (2021). Modeling Considerations for Quantitative Social Science Research Using Social Media Data. PsyArXiv. https://doi.org/10.31234/osf.io/3e2ux
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twitter.com twitter.com
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Prof. Debby Bogaert 💙. (2021, December 20). @chrischirp NL is slightly behind the UK re #omicron and based lockdown on a.o. This model (source @MarionKoopmans). Despite uncertainty the ‘continue as is’ effect on ICU beds occupied (red) is chilling. Green model is with lockdown after and blue is before Christmas. Decisiveness matters! Https://t.co/2IODZGnNJ6 [Tweet]. @DebbyBogaert. https://twitter.com/DebbyBogaert/status/1472845880411758592
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www.theguardian.com www.theguardian.com
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Devlin, H., Davis, N., & correspondents, N. D. S. (2022, January 14). Expect another Omicron wave in early summer, Sage says. The Guardian. https://www.theguardian.com/world/2022/jan/14/expect-another-covid-omicron-wave-in-early-summer-sage-says
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twitter.com twitter.com
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Helen Branswell. (2022, January 11). 1. #Omicron’s takeover was stunningly rapid and is now nearly complete, at least in the U.S. The latest “Nowcast” from @CDCgov (which uses recent data to model what’s happen now) suggests most of what is circulating here now is omicron. Https://t.co/6w3e8Ut5NW [Tweet]. @HelenBranswell. https://twitter.com/HelenBranswell/status/1480970453313277954
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www.theguardian.com www.theguardian.com
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Sample, I., & editor, I. S. S. (2022, January 13). Covid isolation to be cut to five full days in England, says Sajid Javid. The Guardian. https://www.theguardian.com/world/2022/jan/13/covid-isolation-to-be-cut-to-five-full-days-in-england-says-sajid-javid
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www.washingtonpost.com www.washingtonpost.com
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U.S. breaks record with more than 145,000 covid-19 hospitalizations. (n.d.). Washington Post. Retrieved January 12, 2022, from https://www.washingtonpost.com/health/2022/01/10/covid-hospitalized-omicron/
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www.medrxiv.org www.medrxiv.org
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Keeling, M. J., Brooks-Pollock, E., Challen, R. J., Danon, L., Dyson, L., Gog, J. R., Guzman-Rincon, L., Hill, E. M., Pellis, L. M., Read, J. M., & Tildesley, M. (2021). Short-term Projections based on Early Omicron Variant Dynamics in England. (p. 2021.12.30.21268307). https://doi.org/10.1101/2021.12.30.21268307
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- Dec 2021
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www.imperial.ac.uk www.imperial.ac.uk
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Omicron largely evades immunity from past infection or two vaccine doses | Imperial News | Imperial College London. (n.d.). Imperial News. Retrieved December 21, 2021, from https://www.imperial.ac.uk/news/232698/modelling-suggests-rapid-spread-omicron-england/
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twitter.com twitter.com
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Acme Birch Beer. (2021, December 13). A long thread on @NYTimes/@washingtonpost/@TheAtlantic’s favorite prediction-making expert, Monica Gandhi. Feb 22, 2021: “I need to say variants, shmariants, okay? I’m sorry, I don’t know what kind of trouble that’s going to get me in...” https://t.co/CwQL6QBG78 [Tweet]. @KindAndUnblind. https://twitter.com/KindAndUnblind/status/1470389931679883268
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Covid: UK reports highest daily cases since the pandemic began. (2021, December 15). BBC News. https://www.bbc.com/news/uk-59673150
Tags
- socialising
- COVID-19
- mask wearing
- modeling
- lang:en
- statistics
- delta
- daily cases
- legislation
- government
- Omicron
- hospitalization
- restrictions
- covid pass
- data
- mental health
- mask mandate
- guidance
- education
- epidemic
- Christmas
- testing
- policy
- ventilation
- UK
- booster
- NHS
- variant
- vaccine
- is:news
Annotators
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www.abc.net.au www.abc.net.au
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NSW could have 25,000 COVID-19 cases per day by next month, modelling suggests. (2021, December 14). ABC News. https://www.abc.net.au/news/2021-12-15/nsw-covid-19-infections-reach-three-month-high-1360-new-cases/100700866
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www.medrxiv.org www.medrxiv.org
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Colosi, E., Bassignana, G., Contreras, D. A., Poirier, C., Boëlle, P.-Y., Cauchemez, S., Yazdanpanah, Y., Lina, B., Fontanet, A., Barrat, A., & Colizza, V. (2021). Screening and vaccination against COVID-19 to minimize school closure (p. 2021.08.15.21261243). https://doi.org/10.1101/2021.08.15.21261243
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Mahase, E. (2021). Covid-19: Do vaccines work against omicron—and other questions answered. BMJ, 375, n3062. https://doi.org/10.1136/bmj.n3062
Tags
- antibody
- antiviral
- EU
- modeling
- Africa
- lang:en
- delta
- restrictions
- reinfection
- PCR
- Europe
- booster
- risk
- variant
- treatment
- domestic measures
- COVID-19
- mask wearing
- severity
- South Africa
- epidemiology
- mutation
- response
- is:article
- detection
- Omicron
- hospitalization
- data
- work from home
- travel ban
- testing
- transmissibility
- research
- UK
- infection rate
- vaccine
Annotators
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twitter.com twitter.com
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Art Poon. (2021, November 28). Our first https://filogeneti.ca/CoVizu update with B.1.1.529. As expected, number of mutations is well over molecular clock prediction (~13 diffs). Relatively low numbers of identical genomes implies large number of unsampled infections. We update every two days from GISAID. https://t.co/m8w2CjL1c0 [Tweet]. @art_poon. https://twitter.com/art_poon/status/1465001066194481162
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- Nov 2021
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twitter.com twitter.com
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Jeffrey Barrett. (2021, October 19). Proportion of AY.4.2 (now on http://covid19.sanger.ac.uk) has been steadily increasing in England, which is a pattern that is quite different from other AY lineages. Several of them rose when there was still Alpha to displace, but none has had a consistent advantage vs other Delta. Https://t.co/mD5gQzKxgV [Tweet]. @jcbarret. https://twitter.com/jcbarret/status/1450408485829718039
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www.theguardian.com www.theguardian.com
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Spiegelhalter, D., & Masters, A. (2021, November 7). We can be confident there have been far more than 5 million global Covid deaths. The Guardian. https://www.theguardian.com/theobserver/commentisfree/2021/nov/07/we-can-be-confident-there-have-been-far-more-than-5-million-covid-deaths
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twitter.com twitter.com
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Henk-Jan Westeneng on Twitter. (n.d.). Twitter. Retrieved November 2, 2021, from https://twitter.com/HJWesteneng/status/1455304431038308352 i
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- Sep 2021
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www.imperial.ac.uk www.imperial.ac.uk
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Global study estimates 1.5 million children have lost a caregiver from COVID-19 | Imperial News | Imperial College London. (n.d.). Retrieved September 14, 2021, from https://www.imperial.ac.uk/news/226833/global-study-estimates-15-million-children/
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- Jul 2021
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twitter.com twitter.comTwitter1
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Leah Keating on Twitter: “This work with @DavidJPOS and @gleesonj is now on arXiv (https://t.co/hxjZnCmKcM): ‘A multi-type branching process method for modelling complex contagion on clustered networks’ Here is a quick overview of our paper: (1/6) https://t.co/3jQ2flhk71” / Twitter. (n.d.). Retrieved July 23, 2021, from https://twitter.com/leahakeating/status/1418150117106978816
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- Jun 2021
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www.economist.com www.economist.com
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See how age and illnesses change the risk of dying from covid-19 | The Economist. (n.d.). Retrieved June 29, 2021, from https://www.economist.com/graphic-detail/covid-pandemic-mortality-risk-estimator
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Burton, J. W., Cruz, N., & Hahn, U. (2021). Reconsidering evidence of moral contagion in online social networks. Nature Human Behaviour. https://doi.org/10.1038/s41562-021-01133-5
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- May 2021
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www.eventbrite.com www.eventbrite.com
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Data Collection and Integration to Enhance Public Health Registration, Thu, Jun 10, 2021 at 1:00 PM | Eventbrite. (n.d.). Retrieved May 28, 2021, from https://www.eventbrite.com/e/data-collection-and-integration-to-enhance-public-health-registration-156146370999
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Wang, C. J. (2021). Contact-tracing app curbed the spread of COVID in England and Wales. Nature, d41586-021-01354–01358. https://doi.org/10.1038/d41586-021-01354-8
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- Apr 2021
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www.theguardian.com www.theguardian.com
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UCL team’s claim that herd immunity set to be achieved in UK disputed | Coronavirus | The Guardian. (n.d.). Retrieved April 12, 2021, from https://www.theguardian.com/world/2021/apr/09/ucl-team-claim-covid-19-herd-immunity-achieved-uk-disputed-scientists
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- Mar 2021
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www.pnas.org www.pnas.org
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Hong, B., Bonczak, B. J., Gupta, A., Thorpe, L. E., & Kontokosta, C. E. (2021). Exposure density and neighborhood disparities in COVID-19 infection risk. Proceedings of the National Academy of Sciences, 118(13). https://doi.org/10.1073/pnas.2021258118
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www.youtube.com www.youtube.com
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JAMA Network. (2020, November 6). Herd Immunity as a Coronavirus Pandemic Strategy. https://www.youtube.com/watch?v=2tsUTAWBJ9M
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- Jan 2021
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www.nature.com www.nature.com
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(2021). How epidemiology has shaped the COVID pandemic. Nature, 589, 491-492. Doi: 10.1038/d41586-021-00183-z
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Corral. A., (2020). Scientific comment on "Tail risk of contagious diseases" Cornell University Physics and Society. Retrieved from: https://arxiv.org/abs/2007.06876
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- Oct 2020
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jamanetwork.com jamanetwork.com
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Woolf, S. H., Chapman, D. A., Sabo, R. T., Weinberger, D. M., Hill, L., & Taylor, D. D. H. (2020). Excess Deaths From COVID-19 and Other Causes, March-July 2020. JAMA, 324(15), 1562. https://doi.org/10.1001/jama.2020.19545
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www.mobs-lab.org www.mobs-lab.org2019nCOV1
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2019nCOV. (n.d.). MOBS Lab. Retrieved October 2, 2020, from https://www.mobs-lab.org/2019ncov.html
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- Sep 2020
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science.sciencemag.org science.sciencemag.org
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Ruktanonchai, N. W., Floyd, J. R., Lai, S., Ruktanonchai, C. W., Sadilek, A., Rente-Lourenco, P., Ben, X., Carioli, A., Gwinn, J., Steele, J. E., Prosper, O., Schneider, A., Oplinger, A., Eastham, P., & Tatem, A. J. (2020). Assessing the impact of coordinated COVID-19 exit strategies across Europe. Science, 369(6510), 1465–1470. https://doi.org/10.1126/science.abc5096
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twitter.com twitter.com
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Adam Kucharski on Twitter. (n.d.). Twitter. Retrieved September 22, 2020, from https://twitter.com/AdamJKucharski/status/1307231588732764161
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www.medrxiv.org www.medrxiv.org
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Friston, K., Costello, A., & Pillay, D. (2020). Dark matter, second waves and epidemiological modelling. MedRxiv, 2020.09.01.20185876. https://doi.org/10.1101/2020.09.01.20185876
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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
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COVID-19. (n.d.). Retrieved September 7, 2020, from https://covid19.healthdata.org/united-states-of-america?view=total-deaths&tab=trend
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www.covid-projections.com www.covid-projections.com
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COVID Projections Tracker. (n.d.). Retrieved September 7, 2020, from https://www.covid-projections.com/
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www.theguardian.com www.theguardian.com
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Spinney, L. (2020, May 31). Covid-19 expert Karl Friston: “Germany may have more immunological ‘dark matter.’” The Observer. https://www.theguardian.com/world/2020/may/31/covid-19-expert-karl-friston-germany-may-have-more-immunological-dark-matter
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- Aug 2020
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www.medrxiv.org www.medrxiv.org
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Ray, E. L., Wattanachit, N., Niemi, J., Kanji, A. H., House, K., Cramer, E. Y., Bracher, J., Zheng, A., Yamana, T. K., Xiong, X., Woody, S., Wang, Y., Wang, L., Walraven, R. L., Tomar, V., Sherratt, K., Sheldon, D., Reiner, R. C., Prakash, B. A., … Consortium, C.-19 F. H. (2020). Ensemble Forecasts of Coronavirus Disease 2019 (COVID-19) in the U.S. MedRxiv, 2020.08.19.20177493. https://doi.org/10.1101/2020.08.19.20177493
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covid19forecasthub.org covid19forecasthub.org
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Home—COVID 19 forecast hub. (n.d.). Retrieved August 28, 2020, from https://covid19forecasthub.org/
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osf.io osf.io
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Alipio, M. (2020). Do socio-economic indicators associate with COVID-2019 cases? Findings from a Philippine study [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/e2hfa
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www.nber.org www.nber.org
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Cronin, C. J., & Evans, W. N. (2020). Private Precaution and Public Restrictions: What Drives Social Distancing and Industry Foot Traffic in the COVID-19 Era? (Working Paper No. 27531; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27531
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www.nature.com www.nature.com
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Menni, C., Valdes, A. M., Freidin, M. B., Sudre, C. H., Nguyen, L. H., Drew, D. A., ... & Visconti, A. (2020). Real-time tracking of self-reported symptoms to predict potential COVID-19. Nature Medicine, 1-4.
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papers.ssrn.com papers.ssrn.com
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Sajadi, M. M., Habibzadeh, P., Vintzileos, A., Shokouhi, S., Miralles-Wilhelm, F., & Amoroso, A. (2020). Temperature, Humidity and Latitude Analysis to Predict Potential Spread and Seasonality for COVID-19 (SSRN Scholarly Paper ID 3550308). Social Science Research Network. https://doi.org/10.2139/ssrn.3550308
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goodjudgment.io goodjudgment.io
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COVID Recovery Dashboard. Retrieved from https://goodjudgment.io/covid-recovery/#1363 on 12/08/2020
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www.nber.org www.nber.org
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Harris, J. E. (2020). Reopening Under COVID-19: What to Watch For (Working Paper No. 27166; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27166
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Collins, G. S., & Wilkinson, J. (n.d.). Statistical issues in the development a COVID-19 prediction models. Journal of Medical Virology, n/a(n/a). https://doi.org/10.1002/jmv.26390
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Modelling the Distributional Impact of the COVID-19 Crisis. COVID-19 and the Labor Market. (n.d.). IZA – Institute of Labor Economics. Retrieved August 5, 2020, from https://covid-19.iza.org/publications/dp13235/
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covid-19.iza.org covid-19.iza.org
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Does the COVID-19 Pandemic Improve Global Air Quality? New Cross-National Evidence on Its Unintended Consequences. COVID-19 and the Labor Market. (n.d.). IZA – Institute of Labor Economics. Retrieved July 29, 2020, from https://covid-19.iza.org/publications/dp13480/
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Aleta, A., Arruda, G. F. de, & Moreno, Y. (2020). Data-driven contact structures: From homogeneous mixing to multilayer networks. PLOS Computational Biology, 16(7), e1008035. https://doi.org/10.1371/journal.pcbi.1008035
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- Jul 2020
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www.nber.org www.nber.org
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Fernández-Villaverde, J., & Jones, C. I. (2020). Estimating and Simulating a SIRD Model of COVID-19 for Many Countries, States, and Cities (Working Paper No. 27128; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27128
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MCLS Virtual Brown Bag June 12, 2020: Bayesian Modelling. (2020, June 15). https://www.youtube.com/watch?v=7LLZPNLhn5o
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Kubinec, R., & Carvalho, L. (2020). A Retrospective Bayesian Model for Measuring Covariate Effects on Observed COVID-19 Test and Case Counts [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/jp4wk
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science.sciencemag.org science.sciencemag.org
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Barton, C. M., Alberti, M., Ames, D., Atkinson, J.-A., Bales, J., Burke, E., Chen, M., Diallo, S. Y., Earn, D. J. D., Fath, B., Feng, Z., Gibbons, C., Hammond, R., Heffernan, J., Houser, H., Hovmand, P. S., Kopainsky, B., Mabry, P. L., Mair, C., … Tucker, G. (2020). Call for transparency of COVID-19 models. Science, 368(6490), 482.2-483. https://doi.org/10.1126/science.abb8637
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osf.io osf.io
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Payne, J. L., & Morgan, A. (2020). COVID-19 and Violent Crime: A comparison of recorded offence rates and dynamic forecasts (ARIMA) for March 2020 in Queensland, Australia [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/g4kh7
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osf.io osf.io
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Payne, J. L., & Morgan, A. (2020). Property Crime during the COVID-19 Pandemic: A comparison of recorded offence rates and dynamic forecasts (ARIMA) for March 2020 in Queensland, Australia [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/de9nc
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Gleeson, J. P., Onaga, T., Fennell, P., Cotter, J., Burke, R., & O’Sullivan, D. J. P. (2020). Branching process descriptions of information cascades on Twitter. ArXiv:2007.08916 [Physics]. http://arxiv.org/abs/2007.08916
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www.medrxiv.org www.medrxiv.org
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Fonseca, S. C., Rivas, I., Romaguera, D., Quijal-Zamorano, M., Czarlewski, W., Vidal, A., Fonseca, J. A., Ballester, J., Anto, J. M., Basagana, X., Cunha, L. M., & Bousquet, J. (2020). Association between consumption of vegetables and COVID-19 mortality at a country level in Europe. MedRxiv, 2020.07.17.20155846. https://doi.org/10.1101/2020.07.17.20155846
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osf.io osf.io
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Kulu, H., & Dorey, P. (2020). Infection Rates from Covid-19 in Great Britain by Geographical Units: A Model-based Estimation from Mortality Data [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/84f3e
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Fitzgerald, R. M. (2020). WAKING TO NORMAL: Examining Archival Appraisal in Data-driven Society [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/2befk
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www.youtube.com www.youtube.com
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Q&A: Modelling COVID-19. (n.d.). Retrieved June 25, 2020, from https://www.youtube.com/watch?v=HUKC8Wq2a0k
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Brooks, H. Z., Kanjanasaratool, U., Kureh, Y. H., & Porter, M. A. (2020). Disease Detectives: Using Mathematics to Forecast the Spread of Infectious Diseases [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/mvn9z
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science.sciencemag.org science.sciencemag.org
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Haushofer, J., & Metcalf, C. J. E. (2020). Which interventions work best in a pandemic? Science. https://doi.org/10.1126/science.abb6144
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www.sciencedirect.com www.sciencedirect.com
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Fontana, M., Iori, M., Montobbio, F., & Sinatra, R. (2020). New and atypical combinations: An assessment of novelty and interdisciplinarity. Research Policy, 49(7), 104063. https://doi.org/10.1016/j.respol.2020.104063
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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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jasp-stats.org jasp-stats.org
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Introducing JASP 0.13. (2020, July 2). JASP - Free and User-Friendly Statistical Software. https://jasp-stats.org/?p=6483
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arxiv.org arxiv.org
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Lovato, J., Allard, A., Harp, R., & Hébert-Dufresne, L. (2020). Distributed consent and its impact on privacy and observability in social networks. ArXiv:2006.16140 [Physics]. http://arxiv.org/abs/2006.16140
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- Jun 2020
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www.newscientist.com www.newscientist.comR number1
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Vaughan, A. (n.d.). R number. New Scientist. Retrieved June 29, 2020, from https://www.newscientist.com/term/r-number/
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psyarxiv.com psyarxiv.com
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Cheung, M. W.-L. (2020). Meta-Analytic Structural Equation Modeling [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/epsqt
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covid19.gleamproject.org covid19.gleamproject.org
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Gleam Project | COVID-19 Mobility USA
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DaveLagnado on Reddit
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www.thelancet.com www.thelancet.com
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Kucharski, A. J., Klepac, P., Conlan, A. J. K., Kissler, S. M., Tang, M. L., Fry, H., Gog, J. R., Edmunds, W. J., Emery, J. C., Medley, G., Munday, J. D., Russell, T. W., Leclerc, Q. J., Diamond, C., Procter, S. R., Gimma, A., Sun, F. Y., Gibbs, H. P., Rosello, A., … Simons, D. (2020). Effectiveness of isolation, testing, contact tracing, and physical distancing on reducing transmission of SARS-CoV-2 in different settings: A mathematical modelling study. The Lancet Infectious Diseases, 0(0). https://doi.org/10.1016/S1473-3099(20)30457-6
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rviews.rstudio.com rviews.rstudio.com
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Views, R. (2020, May 20). An R View into Epidemiology. /2020/05/20/some-r-resources-for-epidemiology/
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arxiv.org arxiv.org
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Gozzi, N., Tizzani, M., Starnini, M., Ciulla, F., Paolotti, D., Panisson, A., & Perra, N. (2020). Collective response to the media coverage of COVID-19 Pandemic on Reddit and Wikipedia. ArXiv:2006.06446 [Physics]. http://arxiv.org/abs/2006.06446
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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
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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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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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- May 2020
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www.thelancet.com www.thelancet.com
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Verity, R., Okell, L., Dorigatti, I., Winskill, P., Whittaker, C., Walker, P., Donnelly, C., Ferguson, N., & Ghani, A. (2020). COVID-19 and the difficulty of inferring epidemiological parameters from clinical data – Authors’ reply. The Lancet Infectious Diseases, 0(0). https://doi.org/10.1016/S1473-3099(20)30443-6
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www.jmir.org www.jmir.org
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Farooq, A., Laato, S., & Islam, A. K. M. N. (2020). Impact of Online Information on Self-Isolation Intention During the COVID-19 Pandemic: Cross-Sectional Study. Journal of Medical Internet Research, 22(5), e19128. https://doi.org/10.2196/19128
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covid19.gleamproject.org covid19.gleamproject.org
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COVID-19 Modeling: Italy
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science.sciencemag.org science.sciencemag.org
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Salje, H., Tran Kiem, C., Lefrancq, N., Courtejoie, N., Bosetti, P., Paireau, J., Andronico, A., Hozé, N., Richet, J., Dubost, C.-L., Le Strat, Y., Lessler, J., Levy-Bruhl, D., Fontanet, A., Opatowski, L., Boelle, P.-Y., & Cauchemez, S. (2020). Estimating the burden of SARS-CoV-2 in France. Science, eabc3517. https://doi.org/10.1126/science.abc3517
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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
- isolation
- COVID-19
- quarentine
- epidemiology
- modeling
- lang:en
- open data
- is:article
- mathematics
- prediction
- superspreading
- pharmaceutical
- China
- emergency
- computational modeling
- contact tracing
- intervention
- antibody testing
- policy
- transmission dynamics
- forecast
- war time
- infection
- challenge
- public health
- complex network
- containment measures
Annotators
URL
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ai.googleblog.com ai.googleblog.com
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Tsitsulin, A. & Perozzi B. Understanding the Shape of Large-Scale Data. (2020 May 05). Google AI Blog. http://ai.googleblog.com/2020/05/understanding-shape-of-large-scale-data.html
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psycnet.apa.org psycnet.apa.org
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Can we count on parents to help their children learn at home? (2020, May 8). Evidence for Action. https://blogs.unicef.org/evidence-for-action/can-we-count-on-parents-to-help-their-children-learn-at-home/
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www.economist.com www.economist.com
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Countries are using apps and data networks to keep tabs on the pandemic. (2020 March 26). The Economist. https://www.economist.com/briefing/2020/03/26/countries-are-using-apps-and-data-networks-to-keep-tabs-on-the-pandemic?fsrc=newsletter&utm_campaign=the-economist-today&utm_medium=newsletter&utm_source=salesforce-marketing-cloud&utm_term=2020-05-07&utm_content=article-link-1
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www.bmj.com www.bmj.com
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Response to “Modelling the pandemic”: Reconsidering the quality of evidence from epidemiological models. (2020). https://www.bmj.com/content/369/bmj.m1567/rr-0
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www.tandfonline.com www.tandfonline.com
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Fenton, N. E., Neil, M., Osman, M., & McLachlan, S. (2020). COVID-19 infection and death rates: The need to incorporate causal explanations for the data and avoid bias in testing. Journal of Risk Research, 0(0), 1–4. https://doi.org/10.1080/13669877.2020.1756381
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arxiv.org arxiv.org
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Müller, M., Derlet, P. M., Mudry, C., & Aeppli, G. (2020). Using random testing to manage a safe exit from the COVID-19 lockdown. ArXiv:2004.04614 [Cond-Mat, Physics:Physics, q-Bio]. http://arxiv.org/abs/2004.04614
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link.aps.org link.aps.org
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Krönke, J., Wunderling, N., Winkelmann, R., Staal, A., Stumpf, B., Tuinenburg, O. A., & Donges, J. F. (2020). Dynamics of tipping cascades on complex networks. Physical Review E, 101(4), 042311. https://doi.org/10.1103/PhysRevE.101.042311
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- Apr 2020
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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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Im, H., Ahn, C., Wang, P., & Chen, C. (2020, April 13). An Early Examination: Psychological, Health, and Economic Correlates and Determinants of Social Distancing Amidst COVID-19. https://doi.org/10.31234/osf.io/9ravu
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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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cmmid.github.io cmmid.github.io
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Russell, 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, March 22). Using a delay-adjusted case fatality ratio to estimate under-reporting. CMMID Repository. https://cmmid.github.io/topics/covid19/global_cfr_estimates.html
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arxiv.org arxiv.org
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El Shoghri, A., et al. (2020 April 03). How mobility patterns drive disease spread: A case study using public transit passenger card travel data. 2019 IEEE 20th International Symposium on "A World of Wireless, Mobile and Multimedia Networks". DOI:10.1109/WoWMoM.2019.8793018
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www.thelancet.com www.thelancet.com
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Viner, R. M., et al. (2020 April 06). School closure and management practices during coronavirus outbreaks including COVID-19: a rapid systematic review. The Lancet. DOI: https://doi.org/10.1016/S2352-4642(20)30095-X.
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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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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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doi.org doi.org
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Atchison, C. J., Bowman, L., Vrinten, C., Redd, R., Pristera, P., Eaton, J. W., & Ward, H. (2020). Perceptions and behavioural responses of the general public during the COVID-19 pandemic: A cross-sectional survey of UK Adults [Preprint]. Public and Global Health. https://doi.org/10.1101/2020.04.01.20050039
Tags
- COVID-19
- risk perception
- social distancing
- perception
- quarentine
- modeling
- survey
- is:preprint
- lang:en
- behavior
- response
- data collection
- government
- adult
- minority
- cross-sectional
- prevention
- economy
- policy
- transmission dynamics
- lockdown
- UK
- handwashing
- face mask
- self-isolation
- demographics
- statistics
Annotators
URL
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twitter.com twitter.com
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ReconfigBehSci en Twitter: “‘Proper science without the drag’ – Move to the medical model of journal review: ‘Yes/No’ decision. We suggest the temporary adoption of this model for crisis-relevant material by journals. [happening already, but potentially even better models: @Meta_psy and @F1000Research?]” / Twitter. (n.d.). Twitter. Retrieved April 15, 2020, from https://twitter.com/scibeh/status/1242094075312046082
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Local file Local file
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DP-3T/documents. (n.d.). GitHub. Retrieved April 8, 2020, from https://github.com/DP-3T/documents
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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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trello.com trello.com
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Collective Intelligence and COVID-19 | Trello. (n.d.). Retrieved April 20, 2020, from https://trello.com/b/STdgEhvX/collective-intelligence-and-covid-19
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- Jan 2020
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www.digitalocean.com www.digitalocean.com
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First sighting of Jupyter Notebook (that I recall).
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- Nov 2018
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Local file Local file
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For the second, we could try to detect inconsistencies, eitherby inspecting samples of the class hierarchy
Yes, that's what I do when doing quality work on the taxonomy (with the tool wdtaxonomy)
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Possible relations between Items
This only includes properties of data-type item?! It should be made more clear because the majority of Wikidata classes has other data types.
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A KG typically spans across several domains and is built on topof a conceptual schema, orontology, which defines what types of entities (classes) are allowed inthe graph, alongside the types ofpropertiesthey can have
Wikidata differs from typical KG as it is not build on top of classes (entity types). Any item (entity) can be connected by any property. Wikidata's only strict "classes" in the sense of KG classes are its data types (item, lemma, monolingual string...).
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- Aug 2017
- Jun 2015
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The comparison between the model and the experts is based on the species distribution models (SMDs), not on actual species occurrences, so the observed difference could be due to weakness in the SDM predictions rather than the model outperforming the experts. The explanation for this choice in Footnote 4 is reasonable, but I wonder if it could be addressed by rarifying the sampling appropriately.
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