Pullano, G., Di Domenico, L., Sabbatini, C. E., Valdano, E., Turbelin, C., Debin, M., Guerrisi, C., Kengne-Kuetche, C., Souty, C., Hanslik, T., Blanchon, T., Boëlle, P.-Y., Figoni, J., Vaux, S., Campèse, C., Bernard-Stoecklin, S., & Colizza, V. (2021). Underdetection of cases of COVID-19 in France threatens epidemic control. Nature, 590(7844), 134–139. https://doi.org/10.1038/s41586-020-03095-6

Xu, Z., & Guo, H. (2018). Using Text Mining to Compare Online Pro- and Anti-Vaccine Headlines: Word Usage, Sentiments, and Online Popularity. Communication Studies, 69(1), 103–122. https://doi.org/10.1080/10510974.2017.1414068

McMurtry, C. M. (2020). Managing immunization stress-related response: A contributor to sustaining trust in vaccines. Canada Communicable Disease Report, 46(6), 210–218. https://doi.org/10.4745/ccdr.v46i06a10

This is quite popular recent two years - hybrid method, multi-modality

dataset

Why is this multiplied by 3? Related to statistical error of subsampling - rule of three)

Jaeger, B., Oud, B., Williams, T., Krumhuber, E., Fehr, E., & Engelmann, J. B. (2020, October 20). Trustworthiness detection from faces: Does reliance on facial impressions pay off?. https://doi.org/10.31234/osf.io/ayqeh

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

Abbott, K. R., & Sherratt, T. N. (2013). Optimal sampling and signal detection: Unifying models of attention and speed–accuracy trade-offs. Behavioral Ecology, 24(3), 605–616. https://doi.org/10.1093/beheco/art001

r/BehSciResearch—Review on combatting the COVID misinformation flood. (n.d.). Reddit. Retrieved October 12, 2020, from https://www.reddit.com/r/BehSciResearch/comments/j9mrlp/review_on_combatting_the_covid_misinformation/

Nouri, A. B., Ali. (n.d.). COVID Misinformation Is Killing People. Scientific American. Retrieved October 12, 2020, from https://www.scientificamerican.com/article/covid-misinformation-is-killing-people1/

New tool for the early detection of public health threats from Twitter data: Epitweetr. (2020, October 1). European Centre for Disease Prevention and Control. https://www.ecdc.europa.eu/en/news-events/new-tool-early-detection-public-health-threats-twitter-data-epitweetr

Scientists study whether immune response wards off or worsens Covid. (2020, October 4). The Guardian. http://www.theguardian.com/science/2020/oct/04/scientists-study-whether-immune-response-wards-off-or-worsens-covid

Kaplan, Edward H, Dennis Wang, Mike Wang, Amyn A Malik, Alessandro Zulli, and Jordan H Peccia. ‘Aligning SARS-CoV-2 Indicators via an Epidemic Model: Application to Hospital Admissions and RNA Detection in Sewage Sludge’. Preprint. Infectious Diseases (except HIV/AIDS), 29 June 2020. https://doi.org/10.1101/2020.06.27.20141739.

Daley, J. (n.d.). Millions of Rapid COVID-19 Antigen Tests May Help Fill the Testing Gap. Scientific American. Retrieved September 30, 2020, from https://www.scientificamerican.com/article/millions-of-rapid-covid-19-antigen-tests-may-help-fill-the-testing-gap/

ddPCR

López, J. A. M., Arregui-Garcĺa, B., Bentkowski, P., Bioglio, L., Pinotti, F., Boëlle, P.-Y., Barrat, A., Colizza, V., & Poletto, C. (2020). Anatomy of digital contact tracing: Role of age, transmission setting, adoption and case detection. MedRxiv, 2020.07.22.20158352. https://doi.org/10.1101/2020.07.22.20158352

Chin, A. W. H., Chu, J. T. S., Perera, M. R. A., Hui, K. P. Y., Yen, H.-L., Chan, M. C. W., Peiris, M., & Poon, L. L. M. (2020). Stability of SARS-CoV-2 in different environmental conditions. The Lancet Microbe, 1(1), e10. https://doi.org/10.1016/S2666-5247(20)30003-3

Tybur, J. M., Lieberman, D., Fan, L., Kupfer, T., & de Vries, R. E. (2020). Behavioral immune tradeoffs: Interpersonal value relaxes social pathogen avoidance [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/ec8uw

Santosh, R., Guntuku, S. C., Schwartz, H., Eichstaedt, J., & Ungar, L. (2020). Detecting Symptoms using Context-based Twitter Embeddings during COVID-19. https://openreview.net/forum?id=DFJhXXPZrM7

Herper, M. (2020, July 1). Covid-19 vaccine from Pfizer and BioNTech shows positive results. CNBC. https://www.cnbc.com/2020/07/01/coronavirus-vaccine-from-pfizer-and-biontech-shows-positive-results-report-says.html

Shan, B., Broza, Y. Y., Li, W., Wang, Y., Wu, S., Liu, Z., Wang, J., Gui, S., Wang, L., Zhang, Z., Liu, W., Zhou, S., Jin, W., Zhang, Q., Hu, D., Lin, L., Zhang, Q., Li, W., Wang, J., … Haick, H. (2020). Multiplexed Nanomaterial-Based Sensor Array for Detection of COVID-19 in Exhaled Breath. ACS Nano. https://doi.org/10.1021/acsnano.0c05657

Peeters, A., Mullins, G., Becker, D., Orellana, L., & Livingston, P. (2020). COVID-19’s impact on Australia’s health research workforce. The Lancet, 396(10249), 461. https://doi.org/10.1016/S0140-6736(20)31533-6

Mohammadi, A., Esmaeilzadeh, E., Li, Y., Bosch, R. J., & Li, J. Z. (2020). SARS-CoV-2 detection in different respiratory sites: A systematic review and meta-analysis. EBioMedicine, 0(0). https://doi.org/10.1016/j.ebiom.2020.102903

Cevik, M., Tate, M., Lloyd, O., Maraolo, A. E., Schafers, J., & Ho, A. (2020). SARS-CoV-2, SARS-CoV-1 and MERS-CoV viral load dynamics, duration of viral shedding and infectiousness: A living systematic review and meta-analysis. MedRxiv, 2020.07.25.20162107. https://doi.org/10.1101/2020.07.25.20162107

Identifying social media manipulation with OSoMe tools. (2020, August 11). https://www.youtube.com/watch?v=1BMv0PrdVGs&feature=youtu.be

Carl Bergstrom: “People are using data to bullshit.” (2020, August 1). The Guardian. http://www.theguardian.com/science/2020/aug/01/carl-bergstrom-people-are-using-data-to-bullshit

Tasnim, S., Hossain, M. M., & Mazumder, H. (2020). Impact of rumors or misinformation on coronavirus disease (COVID-19) in social media [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/uf3zn

Golding, N., Russell, T. W., Abbott, S., Hellewell, J., Pearson, C. A. B., Zandvoort, K. van, Jarvis, C. I., Gibbs, H., Liu, Y., Eggo, R. M., Edmunds, J. W., & Kucharski, A. J. (2020). Reconstructing the global dynamics of under-ascertained COVID-19 cases and infections. MedRxiv, 2020.07.07.20148460. https://doi.org/10.1101/2020.07.07.20148460

LoD is the lowest analyte concentration likely to be reliably distinguished from the LoB and at which detection is feasible. LoD is determined by utilising both the measured LoB and test replicates of a sample known to contain a low concentration of analyte.

LoB is the highest apparent analyte concentration expected to be found when replicates of a blank sample containing no analyte are tested.

LoB = meanblank + 1.645(SDblank)

Zhong, H., Wang, Y., Shi, Z., Zhang, L., Ren, H., He, W., Zhang, Z., Zhu, A., Zhao, J., Xiao, F., Yang, F., Liang, T., Ye, F., Zhong, B., Ruan, S., Gan, M., Zhu, J., Li, F., Li, F., … Zhao, J. (2020). Characterization of Microbial Co-infections in the Respiratory Tract of hospitalized COVID-19 patients. MedRxiv, 2020.07.02.20143032. https://doi.org/10.1101/2020.07.02.20143032

New Scientist on Twitter: “Thread on #covid19 trends in the US: Coronavirus infections have surged since the start of June from around 20,000 new cases a day to over 60,000. (1/4) https://t.co/wVFwHWczYR” / Twitter. (n.d.). Twitter. Retrieved July 19, 2020, from https://twitter.com/newscientist/status/1283387188391149571

Lang, T. (2020). Plug COVID-19 research gaps in detection, prevention and care. Nature, 583(7816), 333–333. https://doi.org/10.1038/d41586-020-02004-1

Mallapaty, S. (2020). The mathematical strategy that could transform coronavirus testing. Nature. https://doi.org/10.1038/d41586-020-02053-6

Song, S. (2020). China Experience in Controlling COVID-19. https://doi.org/10.31235/osf.io/gfnep

Hossain, M. M., McKyer, E. L. J., & Ma, P. (2020). Applications of artificial intelligence technologies on mental health research during COVID-19 [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/w6c9b

Davis, J. T., Chinazzi, M., Perra, N., Mu, K., Piontti, A. P. y, Ajelli, M., Dean, N. E., Gioannini, C., Litvinova, M., Merler, S., Rossi, L., Sun, K., Xiong, X., Halloran, M. E., Longini, I. M., Viboud, C., & Vespignani, A. (2020). Estimating the establishment of local transmission and the cryptic phase of the COVID-19 pandemic in the USA. MedRxiv, 2020.07.06.20140285. https://doi.org/10.1101/2020.07.06.20140285

Silverman, J. D., Hupert, N., & Washburne, A. D. (2020). Using influenza surveillance networks to estimate state-specific prevalence of SARS-CoV-2 in the United States. Science Translational Medicine. https://doi.org/10.1126/scitranslmed.abc1126

Fleming, N. (2020). Coronavirus misinformation, and how scientists can help to fight it. Nature. https://doi.org/10.1038/d41586-020-01834-3

The Lancet. (2020). Sustaining containment of COVID-19 in China. The Lancet, 395(10232), 1230. https://doi.org/10.1016/S0140-6736(20)30864-3

Hironori Funabiki on Twitter

Dewa, L. H., Lawrence‐Jones, A., Crandell, C., Jaques, J., Pickles, K., Lavelle, M., Pappa, S., & Aylin, P. (n.d.). Reflections, impact and recommendations of a co-produced qualitative study with young people who have experience of mental health difficulties. Health Expectations, n/a(n/a). https://doi.org/10.1111/hex.13088

Cai, L., Chen, Z., Luo, C., Gui, J., Ni, J., Li, D., & Chen, H. (2020). Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs. ArXiv:2005.07427 [Cs, Stat]. http://arxiv.org/abs/2005.07427

Ortiz, E., García-Pérez, G., & Serrano, M. Á. (2020). Geometric detection of hierarchical backbones in real networks. ArXiv:2006.03207 [Physics]. http://arxiv.org/abs/2006.03207

Meyer, A. C. M., Robinson. (2020, May 21). ‘How Could the CDC Make That Mistake?’ The Atlantic. https://www.theatlantic.com/health/archive/2020/05/cdc-and-states-are-misreporting-covid-19-test-data-pennsylvania-georgia-texas/611935/

Yasseri, T. (n.d.). Dominic Cummings: How the internet knows when you’ve updated your blog. The Conversation. Retrieved June 1, 2020, from http://theconversation.com/dominic-cummings-how-the-internet-knows-when-youve-updated-your-blog-139517

Aslak, U., & Alessandretti, L. (2020). Infostop: Scalable stop-location detection in multi-user mobility data. ArXiv:2003.14370 [Physics]. http://arxiv.org/abs/2003.14370

Kim, H. (2020, March 20). South Korea learned its successful Covid-19 strategy from a previous coronavirus outbreak: MERS. Bulletin of the Atomic Scientists. https://thebulletin.org/2020/03/south-korea-learned-its-successful-covid-19-strategy-from-a-previous-coronavirus-outbreak-mers/

Michalak, N. M., Sng, O., Wang, I., & Ackerman, J. (2020, May 14). Sounds of sickness: Can people identify infectious disease using sounds of coughs and sneezes?. https://doi.org/10.1098/rspb.2020.0944

Riolo, M. A., & Newman, M. E. J. (2020). Consistency of community structure in complex networks. Physical Review E, 101(5), 052306. https://doi.org/10.1103/PhysRevE.101.052306

Kaplan, E. H., & Forman, H. P. (2020). Logistics of Aggressive Community Screening for Coronavirus 2019. JAMA Health Forum, 1(5), e200565–e200565. https://doi.org/10.1001/jamahealthforum.2020.0565

Chu, H. Y., Englund, J. A., Starita, L. M., Famulare, M., Brandstetter, E., Nickerson, D. A., Rieder, M. J., Adler, A., Lacombe, K., Kim, A. E., Graham, C., Logue, J., Wolf, C. R., Heimonen, J., McCulloch, D. J., Han, P. D., Sibley, T. R., Lee, J., Ilcisin, M., … Bedford, T. (2020). Early Detection of Covid-19 through a Citywide Pandemic Surveillance Platform. New England Journal of Medicine, NEJMc2008646. https://doi.org/10.1056/NEJMc2008646

Shental, N., Levy, S., Skorniakov, S., Wuvshet, V., Shemer-Avni, Y., Porgador, A., & Hertz, T. (2020). Efficient high throughput SARS-CoV-2 testing to detect asymptomatic carriers. MedRxiv, 2020.04.14.20064618. https://doi.org/10.1101/2020.04.14.20064618

Wurtzer, S., Marechal, V., Mouchel, J.-M., & Moulin, L. (2020). Time course quantitative detection of SARS-CoV-2 in Parisian wastewaters correlates with COVID-19 confirmed cases. MedRxiv, 2020.04.12.20062679. https://doi.org/10.1101/2020.04.12.20062679

Lohse, S., Pfuhl, T., Berkó-Göttel, B., Rissland, J., Geißler, T., Gärtner, B., Becker, S. L., Schneitler, S., & Smola, S. (2020). Pooling of samples for testing for SARS-CoV-2 in asymptomatic people. The Lancet Infectious Diseases, S1473309920303625. https://doi.org/10.1016/S1473-3099(20)30362-5

Adams, E. R., Anand, R., Andersson, M. I., Auckland, K., Baillie, J. K., Barnes, E., Bell, J., Berry, T., Bibi, S., Carroll, M., Chinnakannan, S., Clutterbuck, E., Cornall, R. J., Crook, D. W., Silva, T. D., Dejnirattisai, W., Dingle, K. E., Dold, C., Eyre, D. W., … Sanchez, V. (2020). Evaluation of antibody testing for SARS-Cov-2 using ELISA and lateral flow immunoassays. MedRxiv, 2020.04.15.20066407. https://doi.org/10.1101/2020.04.15.20066407

Wolf, M. G. (2020, April 26). Survey Uses May Influence Survey Responses. https://doi.org/10.31234/osf.io/c4hd6

Third Report. (2020, April 17). COVID-19 Mobility Monitoring Project. https://covid19mm.github.io//in-progress/2020/04/17/third-report.html

Nanni, M., Andrienko, G., Boldrini, C., Bonchi, F., Cattuto, C., Chiaromonte, F., Comandé, G., Conti, M., Coté, M., Dignum, F., Dignum, V., Domingo-Ferrer, J., Giannotti, F., Guidotti, R., Helbing, D., Kertesz, J., Lehmann, S., Lepri, B., Lukowicz, P., … Vespignani, A. (2020). Give more data, awareness and control to individual citizens, and they will help COVID-19 containment. ArXiv:2004.05222 [Cs]. http://arxiv.org/abs/2004.05222

DFG, German Research Foundation—Call for Multidisciplinary Research into Epidemics and Pandemics in Response to the Outbreak of SARS-CoV-2. (n.d.). Retrieved April 15, 2020, from https://www.dfg.de/en/research_funding/announcements_proposals/2020/info_wissenschaft_20_20/

Paying attention to the qualifications of the author(s)/composer(s) is another crucial role in crap detection at it will help discern whether or not to take the piece seriously or to use it for further research.

In the Rheinghold text , he explains the importance of pay attention the website layout as well as content. However, in doing so, you must tune your crap detection and remember that not everything with a fancy layout is reliable, and vice versa.

Rather than simply stating information, the author (Berger) explains his source and the way in which he broke his research down into smaller categories. This citation is also apart of crap detection with a reliable source.

The implementing of this specific link gives important attribution and increases source reliability. The text makes a statement and is able to back it up with an external, secure source.

Though Rheinghold stresses the importance of crap detection and researching your sources, he accepts the fact that there a limits that we reach in terms of discernment of validity. This is shown as the ISIS busters reach ethical and practical limits of search. It is important in the way that one mustn't get overwhelmed with finding the true source origin because you can only go so far.

Rheinghold stresses the importance of looking at the base of things, rather than simply the makeup and what you see initially, it is important to dig deeper and look at sources from a questionable yet structured angle.

The article ends in 'edu' which, as Rheinghold states, increases estimation of its credibility.

Considering that the origin of this post comes from a non-secure site, that appears a tad amateur - also brings forth speculation. It is a blog site, and considering this - I somehow take what is posted 'with a grain of salt'.

I begin critiquing this article based on Rheinghold's initial conversation with his daughter. In the text Rheinghold suggests using a free internet service - Whois , in order to search for validity in research. After plugging this domain name into the site, I find that the name of the registered owner is 'Educase'. Educase is a nonprofit core data service for research and analysis.

I will be connecting this text through Howard Rheinghold's "Crap Detection 101" from chapter 2 of his book Net Smart - How to Thrive Online. This allows for further critic of this article in terms of this theme.

视频烟雾检测研究进展

Does anyone have an authoritative link for this concept of zones and how they work? It'd be much appreciated.

Early event detection problems can go here. Two example cases just came to my mind are: 1- in emergency response: detecting a disaster quickly is important. 2- in computational journalism: many locals suddenly start talking about an event means something newsworthy is going on.

This will be an ongoing challenge for species distribution modeling, because most of the data appropriate for these purposes is not collected in such a way as to allow the straightforward application of standard detection probability/occupancy models. This could potentially be addressed by developing models for detection probability based on species and habitat type. These models could be built on smaller/different datasets that include the required data for estimating detectability.

Presentation summarizing an approach to duplicate web page detection that was developed by a researcher whilst at Google in the early 2000s