- Aug 2024
- Feb 2024
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www.liberation.fr www.liberation.fr
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Der CO<sub>2</sub>-Gehalt der Atmosphäre wird 2024 weiter steigen, so dass die vom IPCC erarbeiteten Pfade, um das 1,5°-Ziel einzuhalten, nicht mehr eingehalten werden können. Das ergibt sich aus einer Studie des britischen Met Office, die sich auf die Daten des Mauna Loa-Observatoriums in Hawai stützt. (Die obere Grenze der Unsicherheitsbereiche dieser Pfade ist erreicht, selbst wenn der El-Niño-Einfluss abgezogen wird. Ein Einhalten der Pfade würde ein sofortiges Absinken des CO<sub>2</sub>-Gehalts erfordern.) https://www.liberation.fr/environnement/climat-les-concentrations-de-co2-cette-annee-menacent-la-limite-de-15c-daugmentation-globale-des-temperatures-20240119_6JIALPQDBNADFGNHS4MVDXR5QA/?redirected=1
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- Aug 2023
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dl.acm.org dl.acm.org
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It is not unrealistic to forsee the costs ofcomputation and memory plummeting by orders ofmagnitude, while the cost of human programmers increases.It will be cost effective to use large systems like ~. forevery kind of programming, as long as they can providesignificant increases in programmer power. Just ascompilers have found their way into every application overthe past twenty years, intelligent program-understandingsystems may become a part of every reasonablecomputational environment in the next twenty.
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- Apr 2022
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twitter.com twitter.com
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Adam Kucharski [@adamjkucharski]. (2021, September 8). Some tips on interpreting models (from an @SMC_London talk I gave a few months ago): Https://t.co/3NlRN6q6gb [Tweet]. Twitter. https://twitter.com/adamjkucharski/status/1435650792082575360
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- Mar 2022
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twitter.com twitter.com
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wsbgnl. (2022, January 6). Daily COVID-19 hospitalization in the US: observed and forecasted https://covid19forecasthub.org https://t.co/f1rqUhz1mE [Tweet]. @wsbgnl. https://twitter.com/wsbgnl/status/1479162051306033153
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- Oct 2021
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covid19scenariomodelinghub.org covid19scenariomodelinghub.org
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Home - COVID 19 scenario model hub. (n.d.). Retrieved July 5, 2021, from https://covid19scenariomodelinghub.org/
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- Jun 2021
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twitter.com twitter.com
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ReconfigBehSci. (2021, June 1). RT @nikosbosse: Predictions from the second week of the UK Covid-19 Crowd Forecasting Challenge are in. Https://t.co/GfzSBYRmgq On average… [Tweet]. @SciBeh. https://twitter.com/SciBeh/status/1399869840928612354
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- Apr 2021
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twitter.com twitter.com
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ReconfigBehSci. (2021, January 1). RT @bhrenton: In 91 days, we are 99.71% of the way to President Biden’s goal of 200 million shots in 100 days. We can expect to formally me… [Tweet]. @SciBeh. https://twitter.com/SciBeh/status/1385198744401072136
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english.stackexchange.com english.stackexchange.com
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Incidentally, I'd add that it can also be used in looking toward the future, in awareness that we lack such a crystal ball: We base our plans on our knowledge, and there'll be times where we know there is a gap in that knowledge, but we're also aware that there may be things we can't possibly foresee, because "we don't know what we don't know".
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www.carbonbrief.org www.carbonbrief.org
- Mar 2021
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forecasters.org forecasters.org
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Forecasting for COVID-19 has failed. (2020, June 14). International Institute of Forecasters. https://forecasters.org/blog/2020/06/14/forecasting-for-covid-19-has-failed/
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covid19-projections.com covid19-projections.com
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COVID-19 Infections Tracker. (n.d.). COVID-19 Projections Using Machine Learning. Retrieved June 20, 2020, from https://covid19-projections.com/infections-tracker/
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en.wikipedia.org en.wikipedia.org
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The original concept of Project Athena was that there would be course-specific software developed to use in conjunction with teaching. Today, computers are most frequently used for "horizontal" applications such as e-mail, word processing, communications, and graphics.
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- Feb 2021
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pontosandaristera.wordpress.com pontosandaristera.wordpress.com
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.: Πρώτα απ’ όλα, δε νομίζω ότι, παρά τα όσα λέει ο κόσμος, ότι θα υπάρξει μια επιστροφή του θρησκευτικού στις Δυτικές χώρες.
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- Jan 2021
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css-tricks.com css-tricks.com
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This raises a very important point: we can’t know every user’s reason for why they’re visiting our website, but we can use the tools made available to us to help guide them along their way. If that means storing an HTML document for use offline, we’re empowered to help make the experience as easy as possible.
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- Dec 2020
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blogs.bmj.com blogs.bmj.com
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Karl Friston and Anthony Costello: What we have learned from the second covid-19 surge? (2020, December 8). The BMJ. https://blogs.bmj.com/bmj/2020/12/08/karl-friston-and-anthony-costello-what-we-have-learned-from-the-second-covid-19-surge/
Tags
- Bayesian
- prediction
- case number
- inaccuracy
- second wave
- is:blog
- lang:en
- COVID-19
- criticism
- forecast
- epidemiology
- vaccine
- modeling
Annotators
URL
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- Oct 2020
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www.coe.int www.coe.int
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AI and control of Covid-19 coronavirus. (n.d.). Artificial Intelligence. Retrieved October 15, 2020, from https://www.coe.int/en/web/artificial-intelligence/ai-and-control-of-covid-19-coronavirus
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humanwhocodes.com humanwhocodes.com
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Checking in is akin to sharing your code with others, and once out in the world, it’s hard to predict what that code will do.
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github.com github.com
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Complexity, interconnectivity, novelty, & creation is beyond any single entity's ability to effectively forecast.
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covid-19.iza.org covid-19.iza.org
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IZA – Institute of Labor Economics. ‘COVID-19 and the Labor Market’. Accessed 6 October 2020. https://covid-19.iza.org/publications/dp13664/.
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- Sep 2020
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Chen, Q., & Porter, M. A. (2020). Epidemic Thresholds of Infectious Diseases on Tie-Decay Networks. ArXiv:2009.12932 [Physics]. http://arxiv.org/abs/2009.12932
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twitter.com twitter.com
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Joshua Salomon on Twitter. (n.d.). Twitter. Retrieved September 7, 2020, from https://twitter.com/SalomonJA/status/1302767010367983616
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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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www.theatlantic.com www.theatlantic.com
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Lowrey, A. (2020, March 31). We Need to Start Tossing Money Out of Helicopters. The Atlantic. https://www.theatlantic.com/ideas/archive/2020/03/we-need-start-tossing-money-out-helicopters/608968/
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www.nber.org www.nber.org
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Gregory, V., Menzio, G., & Wiczer, D. G. (2020). Pandemic Recession: L or V-Shaped? (Working Paper No. 27105; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27105
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www.nber.org www.nber.org
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Baker, S. R., Bloom, N., Davis, S. J., & Terry, S. J. (2020). COVID-Induced Economic Uncertainty (Working Paper No. 26983; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w26983
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www.nber.org www.nber.org
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Balla-Elliott, D., Cullen, Z. B., Glaeser, E. L., Luca, M., & Stanton, C. T. (2020). Business Reopening Decisions and Demand Forecasts During the COVID-19 Pandemic (Working Paper No. 27362; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27362
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covid-19.iza.org covid-19.iza.org
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The Lockdown Impact on Unemployment for Heterogeneous Workers. COVID-19 and the Labor Market. (n.d.). IZA – Institute of Labor Economics. Retrieved July 30, 2020, from https://covid-19.iza.org/publications/dp13439/
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covid-19.iza.org covid-19.iza.org
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Gender Inequality in COVID-19 Times: Evidence from UK Prolific Participants. COVID-19 and the Labor Market. (n.d.). IZA – Institute of Labor Economics. Retrieved July 29, 2020, from https://covid-19.iza.org/publications/dp13463/
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- Jul 2020
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www.nber.org www.nber.org
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Atkeson, A., Kopecky, K., & Zha, T. (2020). Estimating and Forecasting Disease Scenarios for COVID-19 with an SIR Model (Working Paper No. 27335; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27335
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www.nber.org www.nber.org
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Landier, A., & Thesmar, D. (2020). Earnings Expectations in the COVID Crisis (Working Paper No. 27160; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27160
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Ellison, G. (2020). Implications of Heterogeneous SIR Models for Analyses of COVID-19 (Working Paper No. 27373; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27373
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www.nber.org www.nber.org
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Liu, L., Moon, H. R., & Schorfheide, F. (2020). Panel Forecasts of Country-Level Covid-19 Infections (Working Paper No. 27248; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27248
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www.nber.org www.nber.org
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Chodorow-Reich, G., & Coglianese, J. (2020). Projecting Unemployment Durations: A Factor-Flows Simulation Approach With Application to the COVID-19 Recession (Working Paper No. 27566; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27566
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osf.io osf.io
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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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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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osf.io osf.io
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Payne, J. L., Morgan, A., & Piquero, A. R. (2020). COVID-19 and Social Distancing Measures in Queensland Australia Are Associated with Short-Term Decreases in Recorded Violent Crime [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/z4m8t
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osf.io osf.io
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Burgess, M. G., Langendorf, R. E., Ippolito, T., & Pielke, R. (2020). Optimistically biased economic growth forecasts and negatively skewed annual variation [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/vndqr
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Deutsche Post reports strong results, to pay staff bonus. (2020, July 8). CNBC. https://www.cnbc.com/2020/07/08/deutsche-post-reports-strong-results-to-pay-staff-bonus.html
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- Jun 2020
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behavioralscientist.org behavioralscientist.org
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Gauri, V. (2020 April 30). Behavioral Public Policy Faces a Crisis. Behavioral Scientist. https://behavioralscientist.org/behavioral-public-policy-faces-a-crisis/
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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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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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- May 2020
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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.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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Moore, D. A., & Wallsten, T. S. (2020). Rolling Forecasts. https://doi.org/10.31234/osf.io/ryvg3
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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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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
- open data
- prediction
- intervention
- policy
- transmission dynamics
- superspreading
- antibody testing
- COVID-19
- emergency
- epidemiology
- war time
- modeling
- containment measures
- China
- mathematics
- computational modeling
- public health
- is:article
- challenge
- quarentine
- lang:en
- forecast
- contact tracing
- complex network
- pharmaceutical
- isolation
- infection
Annotators
URL
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twitter.com twitter.com
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Katherine Milkman en Twitter: “Overconfidence is a pernicious bias, even in experts. It’s astounding how few experts’’ confidence intervals included the correct estimate of #COVID19 infections in the US by 3/29 when forecasting for just two weeks in the future. (of course, non-expert estimates are even worse) https://t.co/pa6oMDp2wV" / Twitter.” (n.d.). Twitter. Retrieved April 17, 2020, from https://twitter.com/katy_milkman/status/1244668082062348291
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- Apr 2020
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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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rocs.hu-berlin.de rocs.hu-berlin.de
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Forecasts by Country. (n.d.). Retrieved April 17, 2020, from http://rocs.hu-berlin.de/corona/docs/forecast/results_by_country/
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6-day forecasts of COVID-19 case counts by country based on a novel epidemiological model that integrates the effect of population behavior changes due to government measures and social distancing.The SIR-X model is described in detail here: Effective containment explains sub-exponential growth in confirmed cases of recent COVID-19 outbreak in Mainland China, B. F. Maier & D. Brockmann, medRxiv, https://doi.org/10.1101/2020.02.18.20024414, (2020)The containment measures implemented in response to the growing pandemic vary drastically by country. Classical epidemiological models fail to capture the impact of such efforts on the spread of the outbreak. Under unconstrained conditions, we would see exponential growth in the number of confirmed cases. However, several graphs below indicate that this is not the case. These insights can be used to evaluate the effectiveness of containment strategies in order to inform further courses of action and future policies.Click a country below to view the forecasts for that country. Move the pointer to display the number of confirmed cases by date.The open dots indicate the total number of confirmed cases over time. The blue bars represent the new confirmed cases per day. The solid line depict the model's fit and subsequent predictions of case count numbers for the next 6 days as well as the expected new cases per day. The grey and red shaded regions represent the 98% and 68% confidence intervals, respectively.
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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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- Nov 2017
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nwn.blogs.com nwn.blogs.com
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Virtual Worlds (Slowly!) Emerging from Disillusionment Trough In Gartner's 2012 Hype Cycle
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- Nov 2016
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journals.plos.org journals.plos.org
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My thoughts on Climatic Associations of British Species Distributions Show Good Transferability in Time but Low Predictive Accuracy for Range Change by Rapacciuolo et al. (2012).
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Whilst the consensus method we used provided the best predictions under AUC assessment – seemingly confirming its potential for reducing model-based uncertainty in SDM predictions [58], [59] – its accuracy to predict changes in occupancy was lower than most single models. As a result, we advocate great care when selecting the ensemble of models from which to derive consensus predictions; as previously discussed by Araújo et al. [21], models should be chosen based on aspects of their individual performance pertinent to the research question being addressed, and not on the assumption that more models are better.
It's interesting that the ensembles perform best overall but more poorly for predicting changes in occupancy. It seems possible that ensembling multiple methods is basically resulting in a more static prediction, i.e., something closer to a naive baseline.
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an average 87% of grid squares maintaining the same occupancy status; similarly, all climatic variables were also highly correlated between time periods (ρ>0.85, p<0.001 for all variables). As a result, models providing a good fit to early distribution records can be expected to return a reasonable fit to more recent records (and vice versa), regardless of whether relevant predictors of range shift have actually been captured. Previous studies have warned against taking strong model performance on calibration data to indicate high predictive accuracy to a different time period [20], [24]–[26]; our results indicate that strong model performance in a different time period, as measured by widespread metrics, may not indicate high predictive accuracy either.
This highlights the importance of comparing forecasts to baseline predictions to determine the skill of the forecast vs. the basic stability of the pattern.
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The correct classification rate of grid squares that remained occupied or remained unoccupied (CCRstable) was fairly high (mean±s.d. = 0.75±0.15), and did not covary with species’ observed proportional change in range size (Figure 3B). In contrast, the CCR of grid squares whose occupancy status changed between time periods (CCRchanged) was very low overall (0.51±0.14; guessing randomly would be expected to produce a mean of 0.5), with range expansions being slightly better predicted than range contractions (0.55±0.15 and 0.48±0.12, respectively; Figure 3C).
This is a really important result and my favorite figure in this ms. For cells that changed occupancy status (e.g., a cell that has occupied at t_1 and was unoccupied at t_2) most models had about a 50% chance of getting the change right (i.e., a coin flip).
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Quantifying the temporal transferability of SDMs by comparing the agreement between model predictions and observations for the predicted period using common metrics is not a sufficient test of whether models have actually captured relevant predictors of change. A single range-wide measure of prediction accuracy conflates accurately predicting species expansions and contractions to new areas with accurately predicting large parts of the distribution that have remained unchanged in time. Thus, to assess how well SDMs capture drivers of change in species distributions, we measured the agreement between observations and model predictions of each species’ (a) geographic range size in period t2, (b) overall change in geographic range size between time periods, and (c) grid square-level changes in occupancy status between time periods.
This is arguably the single most important point in this paper. It is equivalent to comparing forecasts to simple baseline forecasts as is typically done in weather forecasting. In weather forecasting it is typical to talk about the "skill" of the forecast, which is how much better it does than a simple baseline. In this case the the baseline is a species range that doesn't move at all. This would be equivalent to a "naive" forecast in traditional time-series analysis since we only have a single previous point in time and the baseline is simply the prediction based on this value not changing.
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