Found this report circulating on twitter from a junior doctor in Australia.<p><a href="https://www.medrxiv.org/content/10.1101/2020.01.23.20018549v1.full.pdf" rel="nofollow">https://www.medrxiv.org/content/10.1101/2020.01.23.20018549v...</a><p>Novel coronavirus 2019-nCoV: early estimation of epidemiological parameters and epidemic predictions<p>> Key findings:<p>> ● We estimate the basic reproduction number of the infection (𝑅𝑅0) to be significantly greater than one. We estimate it to be between 3.6 and 4.0, indicating that 72-75% of transmissions must be prevented by control measures for infections to stop increasing.<p>> ● We estimate that only 5.1% (95%CI, 4.8–5.5) of infections in Wuhan are identified, indicating a large number of infections in the community, and also reflecting the difficulty in detecting cases of this new disease. Surveillance for this novel pathogen has been launched very quickly by public health authorities in China, allowing for rapid assessment of the speed of increase of cases in Wuhan and other areas.<p>> ● If no change in control or transmission happens, then we expect further outbreaks to occur in other Chinese cities, and that infections will continue to be exported to international destinations at an increasing rate. In 14 days’ time (4 February 2020), our model predicts the number of infected people in Wuhan to be greater than 190 thousand (prediction interval, 132,751 to 273,649). We predict the cities with the largest outbreaks elsewhere in China to be Shanghai, Beijing, Guangzhou, Chongqing and Chengdu. We also predict that by 4 Feb 2020, the countries or special administrative regions at greatest risk of importing infections through air travel are Thailand, Japan, Taiwan, Hong Kong, and South Korea.<p>> ● Our model suggests that travel restrictions from and to Wuhan city are unlikely to be effective in halting transmission across China; with a 99% effective reduction in travel, the size of the epidemic outside of Wuhan may only be reduced by 24.9% on 4 February.<p>> ● There are important caveats to the reliability of our model predictions, based on the assumptions underpinning the model as well as the data used to fit the model. These should be considered when interpreting our findings.<p>Source:<p><a href="https://twitter.com/char_durand/status/1221997021663387649" rel="nofollow">https://twitter.com/char_durand/status/1221997021663387649</a>
One minor note is that the "real time" nature of this map is a bit deceiving at the moment, at least to me. While the specific figures imply that it's pulling data at a near real-time basis, it's still dependent on countries posting the latest figures in batch, and then being updated as last indicated in the top right. As of now, it seems verbal announcements precede the digital announcements and as such I've found journalist-maintained maps to be a bit more up to date with the latest announcements, such as the one maintained by the NYTimes. Although if this continues to spread to different demographics, this map will probably have much higher utility.
I get:<p>Fatal Errors<p>Unable to load <a href="https://gisanddata.maps.arcgis.com/sharing/rest/content/items/bda7594740fd40299423467b48e9ecf6/data?f=json" rel="nofollow">https://gisanddata.maps.arcgis.com/sharing/rest/content/item...</a> status: 502<p>d@<a href="https://js.arcgis.com/3.31/init.js:112:340" rel="nofollow">https://js.arcgis.com/3.31/init.js:112:340</a> d@<a href="https://js.arcgis.com/3.31/init.js:140:425" rel="nofollow">https://js.arcgis.com/3.31/init.js:140:425</a> f@<a href="https://js.arcgis.com/3.31/init.js:145:35" rel="nofollow">https://js.arcgis.com/3.31/init.js:145:35</a><p>Maybe the load is a bit too heavy for the app at the moment<p>EDIT: after a few attempts I was able to load the map, it is nicely done!
If this whistleblower [0] is to be believed, then the Chinese may be downplaying this<p>[0] - <a href="https://twitter.com/Terrence_STR/status/1221100970521829377" rel="nofollow">https://twitter.com/Terrence_STR/status/1221100970521829377</a>
For those who want something a little more mobile-friendly, we just built a similar map over the weekend: <a href="https://coronavirus.app/" rel="nofollow">https://coronavirus.app/</a>
Someone who is more versed in the usual statistics, how serious is it? Without domain knowledge, I have no idea if the few dozen deaths in this period at these locations is significant or not.
Baidu's realtime map has a higher count this, though in Chinese. It seems to be more up to date, with detailed patient info sometimes included in the news tab.<p><a href="https://voice.baidu.com/act/newpneumonia/newpneumonia" rel="nofollow">https://voice.baidu.com/act/newpneumonia/newpneumonia</a>
I also use this that has a slightly more up to date numbers.
<a href="https://jobtube.cn/wv/" rel="nofollow">https://jobtube.cn/wv/</a>
Please don't use circle radius for indicating the number of observations. It is really easy to confuse the circle radius with the geographic extent of the observations, when in reality they are likely more tightly clustered.<p>Rather, consider using a heatmap to show the proportional density of observations in a way that better highlights their geographic distribution.
Nextstrain has a better visualization <a href="https://nextstrain.org/ncov" rel="nofollow">https://nextstrain.org/ncov</a>
The methodological approach and data sources are detailed in the associated post by JHU professor Lauren Gardner: <a href="https://systems.jhu.edu/research/public-health/ncov/" rel="nofollow">https://systems.jhu.edu/research/public-health/ncov/</a>
If these stats hold, the disease looks to have a mortality of around 2%.<p>Doesn't sound like a lot, but an R0 of >2 is concerning because of what it means for total infected. 2% of everyone within range of access to modern transportation is an <i>awful</i> lot of people.
Terrible geocoding -- the one case in Sydney, state of NSW, is drawn as a dot in what is guess is the centroid of the state, hundreds of km away from anywhere.
almost 4% mortality rate in Hubei is pretty scary, I imagine a lot has to do with China not reporting real infection numbers which are rumoured to be much higher
here is a chart with log scale option <a href="http://coronaviruschart.com/" rel="nofollow">http://coronaviruschart.com/</a>
@mods, there was the news the other day about Kobe's death. It got flagged, and for good reason.<p>Why is this coronavirus stuff not flagged also? There are currently 3 articles on the front page related to this. It just slipped or is there a reason for this?