Lancet: "Severe breakthrough COVID-19 cases in the SARS-CoV-2 delta (B.1.617.2) variant era":
Really useful Twitter thread on rapid tests: https://twitter.com/VicLeungIDdoc/status/1471050063241629699?s=20
FDA's lists of rapid tests that fail for Omicron variant: https://www.medtechdive.com/news/fda-update-covid-tests-fail-detect-omicron-variant/611617/
Mina's description of how to interpret rapid tests after exposure https://twitter.com/EricTopol/status/1472959306475597826?s=20
FDA list of tests impacted by various variants: https://www.fda.gov/medical-devices/coronavirus-covid-19-and-medical-devices/sars-cov-2-viral-mutations-impact-covid-19-tests?utm_medium=email&utm_source=govdelivery
michael mina press conference rapid tests -- https://www.hsph.harvard.edu/news/features/coronavirus-covid-19-press-conference-with-michael-mina-04-01-21/
FDA testing basics -- nice overview chart: https://www.fda.gov/media/140161/download
Background on RT-LAMP https://www.nature.com/articles/s41598-021-95799-6
Major thread by M Mina on Omicron, rapid testing, sequence of symptoms / infectivity, and possible worry about bronchial nature of Omicron --> ineffective nasal swabbing: https://twitter.com/michaelmina_lab/status/1472024457640394756?s=20
Data on effectiveness of Binax for Omicron: https://twitter.com/michaelmina_lab/status/1474156551925604353?s=20 -- indicates just as effective
Advice on gathering for holidays as of 23 DEC 2021: https://twitter.com/michaelmina_lab/status/1474150628859559951?s=20
Study evaluating infection risk as a function of mask combinations: https://twitter.com/AbraarKaran/status/1474215552776171520?s=20
Northeastern article suggesting that viruses always become less deadly: https://news.northeastern.edu/2021/12/13/virus-evolution/
McGill article on the myth of viruses always evolving to become less deadly over time: https://www.mcgill.ca/oss/article/covid-19/do-bad-viruses-always-become-good-guys-end
Gavi.org article on same: https://www.gavi.org/vaccineswork/will-covid-19-evolve-be-more-or-less-deadly
Wastewater COVID tracking https://www.mwra.com/biobot/biobotdata.htm
No evidence that it will become milder https://www.theguardian.com/world/2021/dec/03/what-does-the-future-hold-for-coronavirus-explainer
lit review: https://www.hindawi.com/journals/complexity/2021/3816221/
emily oster / great barrington declaration funding, long thread: https://twitter.com/maya_chavez_/status/1392585619088416768?s=20
time to infectious dose, masks https://www.cidrap.umn.edu/news-perspective/2021/10/commentary-what-can-masks-do-part-1-science-behind-covid-19-protection
how long does immunity last: https://www.healthline.com/health-news/how-long-does-immunity-last-after-covid-19-what-we-know
lasting immunity https://jamanetwork.com/journals/jama/fullarticle/2782139
why it's important to still wear masks: https://www.nationwidechildrens.org/family-resources-education/700childrens/2021/02/why-masks-are-important-after-covid-19
"Can rewiring strategy control the epidemic spreading?": https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7126863/
alternative small world network definition idea, more tractable analytically: https://d-nb.info/1194507883/34
epidemic dynamics on an adaptive network https://pubmed.ncbi.nlm.nih.gov/16803215/#:~:text=Here%20we%20study%20epidemic%20dynamics,hysteresis%2C%20and%20first%20order%20transitions. -- nice follow-up articles listed -- and, pdf is here: https://journals.aps.org/prl/pdf/10.1103/PhysRevLett.96.208701 -- neat!
complex network approach to supply chains https://www.researchgate.net/publication/263268796_A_complex_network_approach_to_supply_chain_network_theory/link/57c8efa508ae9d640480e633/download
Note: they're optimizing for efficiency ... but interesting to wonder if there is a tradeoff in efficiency vs resilience ...
resilience, p 458
"Modeling topologically resilient supply chain networks" , via https://appliednetsci.springeropen.com/articles/10.1007/s41109-018-0070-7
Interesting -- above paper is really about trying to generate network models based on observed real-world networks via some learning algorithm ...
"Network science approach to modelling the topology and robustness of supply chain networks: a review and perspective" https://appliednetsci.springeropen.com/articles/10.1007/s41109-017-0053-0 -- ah, looks like a great review & perspective
Ah, interesting -- figure out what properties of a real-world network are sort of 'trivial', shared by all networks of that type, and which are unusual:
Robustness vs resilience
Barabasi's network science book online: http://networksciencebook.com/chapter/1
"A supply chain view of the resilience enterprise" https://www.proquest.com/openview/ab02ef85c43466ea1085994bc7340615/1?pq-origsite=gscholar&cbl=26142
"Node-Level Resilience Loss in Dynamic Complex Networks" https://www.researchgate.net/figure/Network-Rewiring-Unwiring-to-Change-Resilience-i-a-parent-networks-node-11-has_fig6_339536011
"In an increasingly connected world, the resilience of networked dynamical systems is important in the felds of ecology, economics, critical infrastructures, and organizational behaviour. Whilst we understand small-scale resilience well, our understanding of large-scale networked resilience is limited. Recent research in predicting the efective network-level resilience pattern has advanced our understanding of the coupling relationship between topology and dynamics. However, a method to estimate the resilience of an individual node within an arbitrarily large complex network governed by non-linear dynamics is still lacking. Here, we develop a sequential mean-feld approach and show that after 1-3 steps of estimation, the node-level resilience function can be represented with up to 98% accuracy. This new understanding compresses the higher dimensional relationship into a onedimensional dynamic for tractable understanding, mapping the relationship between local dynamics and the statistical properties of network topology. By applying this framework to case studies in ecology and biology, we are able to not only understand the general resilience pattern of the network, but also identify the nodes at the greatest risk of failure and predict the impact of perturbations. These fndings not only shed new light on the causes of resilience loss from cascade efects in networked systems, but the identifcation capability could also be used to prioritize protection, quantify risk, and inform the design of new system architectures."
PDF here: PDF
Pinning control and sycnhronization in networks http://www.ee.cityu.edu.hk/~gchen/pdf/C-IJCAS14.PDF
-- yes, this is the concept I was looking for! 'pinning control'
"Feedback pinning control of collective behaviors aroused by epidemic spread on complex networks" https://arxiv.org/abs/1806.06651
"Spreading dynamics and synchronization behavior of periodic diseases on complex networks" https://www.sciencedirect.com/science/article/abs/pii/S0378437116306720
"Cluster collective behaviors via feedback pinning control induced by epidemic spread in a patchy population with dispersal" http://www.aimspress.com/article/id/5381
"Boat to bowl: resilience through network rewiring of a community-supported fishery amid the COVID-19 pandemic" https://iopscience.iop.org/article/10.1088/1748-9326/abe4f6 -- looks like it combines network analysis and empirical study of a real-world network -- fascinating!
looks like 'resilience and rewiring' are good key words to search for here https://www.google.com/search?q=network+resilience+rewiring+function&oq=network+resilience+rewiring+function&aqs=chrome..69i57j33.5997j0j4&sourceid=chrome&ie=UTF-8
Sensitivity vs specificity explained https://www.health.ny.gov/diseases/chronic/discreen.htm#:~:text=Sensitivity%20refers%20to%20a%20test's,have%20a%20disease%20as%20negative.
Further, more elaborate explanation https://www.siemens-healthineers.com/en-us/laboratory-diagnostics/assays-by-diseases-conditions/infectious-disease-assays/specificity-matters
comparison of accuracy of various tests https://www.evaluate.com/vantage/articles/news/policy-and-regulation/balancing-accuracy-and-cost-antigen-testing
Review of home health test kits: https://www.nytimes.com/wirecutter/reviews/at-home-covid-test-kits/
'On/go' same as 'CareStart' -- see bottom of page 2, here: https://www.fda.gov/media/151245/download
Performace of AccessBio CareStart rapid antigen test https://academic.oup.com/ofid/article/8/7/ofab243/6285218
Sensitivity and specificity recommendations from WHO https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(21)00048-7/fulltext?dgcid=hubspot_email_newsletter_lancetcovid21&utm_campaign=lancetcovid21&utm_medium=email&_hsmi=113126365&_hsenc=p2ANqtz-8MOnvtgIsqEXnK5pQVK__QVHzojxg7ePl7UapQXtpERs0vnMZchWKMbWkPHfdD3TE4Bx0Q_udrECE6jzzozWtZyAs7Zikvdh0J634uAIud43fWrXE&utm_content=113128108&utm_source=hs_email
Systemmatic review vs viral load https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1003735#sec008
relationship between viral load and infectiousness https://elifesciences.org/articles/69302
"Energy landscapes for machine learning" https://pubs.rsc.org/en/content/articlehtml/2017/cp/c7cp01108c
"Ecological Network Optimization in Urban Central District Based on Complex Network Theory: A Case Study with the Urban Central District of Harbin" https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7913767/
"Generating realistic scaled complex networks" https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6225971/
"Simulated Annealing from scratch in Python" https://machinelearningmastery.com/simulated-annealing-from-scratch-in-python/
Original paper on optimization by simulated annealing by Kirkpatrick https://www.science.org/doi/10.1126/science.220.4598.671
"Using Simulated Annealing to Improve the Information Dissemination Network Structure of a Foreign Animal Disease Outbreak Response" https://www.degruyter.com/document/doi/10.1515/jhsem-2017-0008/html -- PDF: https://krex.k-state.edu/dspace/bitstream/handle/2097/32613/JamesPleuss2016.pdf?sequence=3&isAllowed=y
"Optimization tool to improve the management of the leakages and recovered energy in irrigation water systems" https://www.sciencedirect.com/science/article/pii/S037837742100500X
"Simulated Annealing for Optimal Resource Allocation in Wireless Networks with Imperfect Communications" https://newslab.ece.ohio-state.edu/funded_projects/resources/simulated%20annealing.pdf -- this might be a model paper!
"Identifying epidemic spreading dynamics of COVID-19 by pseudocoevolutionary simulated annealing optimizers" [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7429370/(https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7429370/) -- ah, this is even more spot-on ...
"Dynamics of epidemic diseases on a growing adaptive network" https://www.nature.com/articles/srep42352
"scale-free networks are rare" https://www.nature.com/articles/s41467-019-08746-5
"Fluctuating epidemics on adaptive networks" https://arxiv.org/abs/0801.0606 -- worth study!
In the current work, an ideal setting was proposed where non-infected nodes were assumed to behave rationally and have perfect knowledge of the disease status of their current neighbors and potential new neighbors. It would be of interest to consider a situation in which not all contagious individuals appear ill or know that they are contagious, as might be the case for a sexually transmitted disease possessing asymptomatic individuals.. This effect might be modeled by simultaneous spreading through the network of both the disease and information about the disease. If the current model is extended with information and community structure, social dynamics could be extrapolated to improve contact tracing and epidemic control in organized populations with local structure.
Japanese study of Omicron infectious period, reported in Forbes https://www.forbes.com/sites/victoriaforster/2022/01/10/peak-infectiousness-with-omicron-is-3-6-days-after-symptom-onset-says-new-study/?sh=4e36587b1e32
Original study: "Active epidemiological investigation on SARS-CoV-2 infection caused by Omicron variant (Pango lineage B.1.1.529) in Japan: preliminary report on infectious period" https://www.niid.go.jp/niid/en/2019-ncov-e/10884-covid19-66-en.html
Related Twitter thread: https://twitter.com/LongDesertTrain/status/1480179160052125699
Peak vs high viral load https://twitter.com/LongDesertTrain/status/1480179164611284992?s=20
What I don't understand: 'peak infectiousness' is the focus. That last link is trying to suggest that there is still a high viral load on days 7-9. But on days -1 to 2 (-1 means 1 day before onset of symptoms) the viral load still seems 'high' ...
Referenced previous study on infectious period for pre-omicron variants, here: https://www.medrxiv.org/content/10.1101/2020.09.04.20188516v2 -- original PDF is here: https://www.medrxiv.org/content/10.1101/2020.09.04.20188516v2.full.pdf <-- seems like a really good way to get at the 'definitions' used in figuring out the timing
Food systems, COVID -- https://agrifoodecon.springeropen.com/articles/10.1186/s40100-020-00167-z
"Drive-specific adaptation in disordered mechanical networks of bistable springs" https://arxiv.org/abs/1908.09332
'A new theory of life' https://www.scientificamerican.com/article/a-new-physics-theory-of-life/
Jarzynski -- nonequil stat mech lecture https://www.youtube.com/watch?v=LX
Jeremy England lecture at MIT https://www.youtube.com/watch?v=10cVVHKCRWw
B Machta on information geometery https://www.youtube.com/watch?v=c0O2XxHUG-A
"heterogeneity, sloppiness, variability in biology" https://youtu.be/0Q_D3VTzIFg?t=993
"sloppy models, differential geometry, and why science works" https://www.youtube.com/watch?v=owUf4yhfcIk
"Minimal Fatal Shocks In Multistable Complex Networks" https://www.nature.com/articles/s41598-020-68805-6
--> very interesting paper with respect to the concepts we're interested in
mutliple-state biochemical networks https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2292366/
"Understanding and using sensitivity, specificity and predictive values": https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2636062/
"Natural immunity against covid reinfection risk" https://www.nsf.gov/discoveries/disc_summ.jsp?cntn_id=303705&org=NSF&from=news
BA.2 variant of Omicron https://fortune.com/2022/01/21/what-is-stealth-omicron-new-covid-variant-substrain-denmark/