Something for #degrowth?

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

Cauchemez, S., & Bosetti, P. (2021). A reconstruction of early cryptic COVID spread. Nature. https://doi.org/10.1038/d41586-021-02989-3

Leder, J., Schütz, A., & Pastukhov, A. (Sasha). (2021). Keeping the kids home: Increasing concern for others in times of crisis. PsyArXiv. https://doi.org/10.31234/osf.io/6s28u

Chadi, M.-A., & Mousannif, H. (2021). Reinforcement Learning Based Decision Support Tool For Epidemic Control [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/tcr8s

And people say math isn't important...

Holme, P. (2021). Fast and principled simulations of the SIR model on temporal networks. PLOS ONE, 16(2), e0246961. https://doi.org/10.1371/journal.pone.0246961

Nande A, Adlam B, Sheen J, Levy MZ, Hill AL (2021) Dynamics of COVID-19 under social distancing measures are driven by transmission network structure. PLoS Comput Biol 17(2): e1008684. https://doi.org/10.1371/journal.pcbi.1008684

Houghton, J. P. (2020). Interdependent Diffusion: The social contagion of interacting beliefs. ArXiv:2010.02188 [Physics]. http://arxiv.org/abs/2010.02188

Virus transmission from urinals. (2020). Physics of Fluids, 32(8), 081703. https://doi.org/10.1063/5.0021450

Albertini, M., Sage, L., & Scherer, S. (2020). Intergenerational contacts and Covid-19 spread: Omnipresent grannies or bowling together? [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/exym8

Lockdowns, Loneliness and Life Satisfaction. COVID-19 and the Labor Market. (n.d.). IZA – Institute of Labor Economics. Retrieved August 8, 2020, from https://covid-19.iza.org/publications/dp13140/

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/

Social Stability Challenged: Pandemics, Inequality and Policy Responses. COVID-19 and the Labor Market. (n.d.). IZA – Institute of Labor Economics. Retrieved August 5, 2020, from https://covid-19.iza.org/publications/dp13249/

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

Baqaee, D., Farhi, E., Mina, M. J., & Stock, J. H. (2020). Reopening Scenarios (Working Paper No. 27244; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27244

Holme, P. (2020). Fast and principled simulations of the SIR model on temporal networks. ArXiv:2007.14386 [Physics, q-Bio]. http://arxiv.org/abs/2007.14386

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

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

Di Lauro, F., Croix, J.-C., Berthouze, L., & Kiss, I. (2020). PDE-limits of stochastic SIS epidemics on networks. ArXiv:2007.01043 [Physics, q-Bio]. http://arxiv.org/abs/2007.01043

Tufekci, Z. (2020, April 2). Don’t Believe the COVID-19 Models. The Atlantic. https://www.theatlantic.com/technology/archive/2020/04/coronavirus-models-arent-supposed-be-right/609271/

Chawla, D. S. (2020). Critiqued coronavirus simulation gets thumbs up from code-checking efforts. Nature. https://doi.org/10.1038/d41586-020-01685-y

Horn, S. R., Weston, S. J., & Fisher, P. (2020). Identifying causal role of COVID-19 in immunopsychiatry models [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/w4d5u

Van Mieghem, P., & Wang, F. (2020). Time dependence of susceptible-infected-susceptible epidemics on networks with nodal self-infections. Physical Review E, 101(5), 052310. https://doi.org/10.1103/PhysRevE.101.052310

Kurzweil, R. (2020 May 19). AI-Powered Biotech Can Help Deploy a Vaccine In Record Time. Wired. https://www.wired.com/story/opinion-ai-powered-biotech-can-help-deploy-a-vaccine-in-record-time/

Golino, H., Christensen, A. P., Moulder, R. G., Kim, S., & Boker, S. M. (2020, April 14). Modeling latent topics in social media using Dynamic Exploratory Graph Analysis: The case of the right-wing and left-wing trolls in the 2016 US elections. https://doi.org/10.31234/osf.io/tfs7c

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/

COVID-19 Modeling: Italy

Cuskley, C., & Wallenberg, J. (2020, May 14). Noise resistance in communication: Quantifying uniformity and optimality. https://doi.org/10.31234/osf.io/wpvq4

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

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

Mathias Bynens en Twitter: “New accessibility feature in @ChromeDevTools: simulate vision deficiencies, including blurred vision & various types of color blindness. 🔥 Find out how people with vision deficiencies experience your web app, and resolve contrast issues you didn’t even know you had! https://t.co/QKLQmEhhMM” / Twitter. (n.d.). Twitter. Retrieved April 28, 2020, from https://twitter.com/mathias/status/1237393102635012101

Kojaku, S., Hébert-Dufresne, L., & Ahn, Y.-Y. (2020). The effectiveness of contact tracing in heterogeneous networks. ArXiv:2005.02362 [Physics, q-Bio]. http://arxiv.org/abs/2005.02362

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

What Happens Next? COVID-19 Futures, Explained With Playable Simulations. (n.d.). What Happens Next? COVID-19 Futures, Explained With Playable Simulations. Retrieved May 4, 2020, from https://ncase.me/covid-19/

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

Phillips, N. D., Hertwig, R., Kareev, Y., & Avrahami, J. (2014). Rivals in the dark: How competition influences search in decisions under uncertainty. Cognition, 133(1), 104–119. https://doi.org/10.1016/j.cognition.2014.06.006

Adam, D. (2020 April 02). Special report: The simulations driving the world's response to COVID-19. Nature. doi: 10.1038/d41586-020-01003-6.

Pre-service teachers can benefit from the use of simulations that reproduce classroom environments, student behaviors and profiles, and academic outcomes to guide their craft as educators. In this text, simSchool is briefly evaluated by student teachers to determine its usefulness. While the study had significant limitations of volunteer test subjects in a one-time usage of the tool, simSchool still was given some high marks for it's purpose and realistic depiction of student profiles and classroom environment. Finding suggest simulations like simSchool can continue to improve and with long-term use, would be effective at developing skills for educators. Rating: 8/10

In the text by Jennifer Herseim, virtual reality (VR) is identified as a tool to help with teacher training. Teachers can embark on a learning process in a secure environment with a diverse set of student avatars operated in part, by a real individual. Staff can explore their teaching methods and styles with recorded and measured skills and responses for future review and reflection. Rating 7/10

This is interesting!

So there's more to it than building things and surviving? A student who plays minecraft told me today that he thinks you actually do learn what you are simulating in the game. "Like when you build a garden, you learn how to farm," he told me. Hmmmm.

The Physics of Golf, by Martin Paul Gardiner