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Apparently AI managed to flag
#2019nCoV as early as 31st December. Maybe it managed to find a signal in all the prominent news reports describing the outbreak... https://www.wired.com/story/ai-epidemiologist-wuhan-public-health-warnings/ …pic.twitter.com/AjyhZNzdDg
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There's been a lot of interest in this aspect of our analysis, so we built an online tool to allow people to explore different assumptions/scenarios further https://cmmid-lshtm.shinyapps.io/new_outbreak_probability/ … https://twitter.com/AdamJKucharski/status/1223270381097758720 …pic.twitter.com/FpbUelilGb
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Our analysis of early
#2019nCoV dynamics is now up as a pre-print. We combined data on cases within Wuhan and exported internationally to estimate transmission patterns and potential implications for outbreaks elsewhere. https://www.medrxiv.org/content/10.1101/2020.01.31.20019901v1 …pic.twitter.com/w6i3FWraeg
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Sensible
#nCoV advice in newspapers here in Singapore - there’s evidence masks can reduce onward transmission if you’re ill, but won’t necessarily prevent infection (and could possibly increase risk by giving false sense of security)pic.twitter.com/Y9Cv7bKARG
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A reminder about the challenges of estimating the case fatality rate of an infection in real-time - because we don’t yet know the final outcome of all the cases who are currently ill, estimates can be heavily biased (example shown below for Ebola) https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(14)61706-2/fulltext …pic.twitter.com/uboRfaFBhy
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Assuming SARS-like variation and Wuhan-like transmission, we estimated that once more than three infections have been introduced into a new location, there is an over 50% chance that an outbreak will occur 7/pic.twitter.com/YiAplVt43e
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We estimated a single introduction of 2019-nCoV with SARS-like or MERS-like individual-level variation in transmission and Wuhan-like reproduction number would have a 15–25% probability of causing a large outbreak 6/pic.twitter.com/gLtOKB7P7u
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Comparing model outputs to observed data, we noticed some countries had more cases with a link to Wuhan had more cases than the model predicted, notably France, USA, and Australia... 2/pic.twitter.com/k4U536gibe
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First UK cases of
#nCoV2019 confirmed https://www.bbc.co.uk/news/health-51325192 …. Based on flight connectivity, it was highly likely we were going to get an imported case (data below from yesterday, courtesy of https://www.mobs-lab.org/2019ncov.html ):pic.twitter.com/o3jOPDuOrA
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Timeline of human-to-human transmission of
#nCoV2020 in Germany https://www.nejm.org/doi/full/10.1056/NEJMc2001468?query=featured_home …pic.twitter.com/R6dxOVkhta
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This kind of analysis can reveal clues about whether there were peaks in transmission and whether or not outbreak control measures have been effective, we've previously looked at for Ebola (https://royalsocietypublishing.org/doi/full/10.1098/rstb.2016.0302 …) 4/pic.twitter.com/shzQ2cOxqZ
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We combined multiple datasets with a mathematical model to estimate how R0 has varied over time in Wuhan. We found R0 has likely fluctuated between 1.5-4, with strong indication that R0 was above 2 in early Jan. More info and context below... https://cmmid.github.io/ncov/wuhan_early_dynamics/index.html … 1/pic.twitter.com/rmLI5FuPfu
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Congratulations to
@AD_Henderson1 on his first PhD paper! It examines some unusual patterns of Zika antibody waning in adults, in contrast to dengue. Great to see more arbovirus insights emerging from our ongoing collaboration with FCCDC and ILM https://elifesciences.org/articles/48460 pic.twitter.com/UDeOBWgnuN
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Lot of new
#nCoV2019 plots appearing, but not always with underlying data. In case it’s useful, I’ve built an R package that can extract accurate underlying data from PDF plots: https://github.com/adamkucharski/scrapR/blob/master/README.md …pic.twitter.com/aO0gm0HJiZ
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Clinical features of 41 early
#nCoV2019 cases in Wuhan https://www.thelancet.com/pb-assets/Lancet/pdfs/S0140673620301835.pdf …pic.twitter.com/6ahPGITxf5
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New paper describing family cluster of
#nCoV2019 – including useful timeline for understanding potential characteristics of human-to-human transmission https://www.thelancet.com/pb-assets/Lancet/pdfs/S0140673620301549.pdf …pic.twitter.com/PgTI4Q7BEq
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Useful illustration of some of the uncertainty involved in estimating early epidemic growth for
#nCoV2019 https://twitter.com/C_Althaus/status/1220646280449462274 …pic.twitter.com/D2Qs1uC7gY
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Very nice study by
@BingyiY@SRileyIDD@datcummings and co on how antibody profiles to influenza change over time https://www.medrxiv.org/content/10.1101/2020.01.15.19015693v1 …pic.twitter.com/bupx04Sw52
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This paper by
@ChristoPhraser@SRileyIDD and co provides a very useful breakdown of why SARS could be controlled through isolation and contact tracing https://www.pnas.org/content/101/16/6146 …pic.twitter.com/ZyVRa7v2I6
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Recap of screening effectiveness studies for influenza A/H1N1p and SARS:pic.twitter.com/fMhPHS9p8G
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