https://www.medrxiv.org/content/10.1101/2020.01.23.20018549v...
Novel coronavirus 2019-nCoV: early estimation of epidemiological parameters and epidemic predictions
> Key findings:
> โ We estimate the basic reproduction number of the infection (๐ ๐ 0) 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.
> โ We estimate that only 5.1% (95%CI, 4.8โ5.5) 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.
> โ 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 daysโ 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.
> โ 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.
> โ 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.
Source:
Can we naรฏvely extrapolate that we expect ~4,000 casualties from ~190,000 infections?
I'm not good at understanding numbers, but I'm sure someone here can chime in with a better way to read this.
And that's before we consider the spoiler of mutations, which is the real problem. In the long term, it is evolutionarily advantageous for the virus to become less lethal and eventually fade into the background as just another cold, if it isn't wiped out by aggressive quarantining. However, in the short term, many of the same things that will make it more transmissible, such as more effectively converting host systems into virus factories, or contrariwise, being more effectively hidden while still being contagious, will also make it more dangerous to the host and/or society.
But for all that, there is a sense in which the "naive extrapolation" is also the best thing we have right now based on available data. It is, at least, data-driven.
From the above URL, among 41 people who went to the hospital:
* All 41 patients had pneumonia with abnormal findings on chest CT
* acute respiratory distress syndrome (12 [29%])
* RNAaemia (six [15%])
* acute cardiac injury (five [12%])
* secondary infection (four [10%])
* 13 (32%) patients were admitted to an ICU
* six (15%) died
Question is what percentage of people exposed get bad enough to want to go to the hospital?
[1] https://www.scmp.com/news/china/society/article/3047720/chin...
[1] https://twitter.com/DrEricDing/status/1220919589623803905
In fact, multiple specialist academics said ~3 days ago that only 5% of cases are confirmed, now may be less due to reports of equipment shortages, the additional time the virus has had to disperse geographically, and Chinese New Year. Therefore, we can multiply any official figures by 20x. Official figures are nearing 5,000 cases, which extrapolating from those estimates means we're at around 100,000 infections as of today.
Source: I wrote and am maintaining an animated map of the domestic spread (the one on Wikipedia), over here: https://github.com/globalcitizen/2019-wuhan-coronavirus-data... .. powered by two scrapers, main one is from DXY, which is ahead of this 'real time' map by many hours, judging by total figures.
The same site was handling fire information for the Australian bushfires and had the same text - although that was more "realtime" as the data source was being updated much more frequently.
Fatal Errors
Unable to load https://gisanddata.maps.arcgis.com/sharing/rest/content/item... status: 502
d@https://js.arcgis.com/3.31/init.js:112:340 d@https://js.arcgis.com/3.31/init.js:140:425 f@https://js.arcgis.com/3.31/init.js:145:35
Maybe the load is a bit too heavy for the app at the moment
EDIT: after a few attempts I was able to load the map, it is nicely done!
[0] - https://twitter.com/Terrence_STR/status/1221100970521829377
1. example from another comment below: https://news.ycombinator.com/item?id=22168981
I died laughing at this! It of course is very serious and not funny, but I still laughed at this detail.
That said with a mortality rate of 2%, affecting a country like China with 1.4 billion people, even if half the people get infected, that's 14 million dead.
So overall serious on a large scale.
You say "even" as if it's a conservative estimate, but to me 50% of the country's population being infected by the same virus sounds extreme. Wouldn't this be on the extreme side?
They've reported that strong symptoms occur in only 25% of cases, so if you get it chances are you're just experiencing having a cold (which is caused by a coronavirus).
Early disease death rates are always overestimated because people tend to report only the worst cases of it (even more if the usual symptoms look like a cold). But there is no way to know by how much we are overestimating it.
I'm staying inside for now...
oh, and this, a BSL-4 lab: https://www.nature.com/articles/doi:10.1038/nature.2017.2148...
Rather, consider using a heatmap to show the proportional density of observations in a way that better highlights their geographic distribution.
The media sell stories. People click on those stories, the stories that get clicked on most get other stories written on the topic. This is how the industry works. It's actually quite a simple model.
Human beings are fucking terrible at risk assessment. What do people here think the risk is for them of dying of coronavirus compared to say dying in a traffic accident? Which is more likely to happen to you by a few magnitudes?
That said, I don't trust the numbers so at this point it's hard to say. My gut says there are way more infected people than reported, but deaths are probably less likely to be underreported at that scale. Which means mortality rate would actually be significantly lower than 2%. On flip side, the chance of getting infected would be much higher.
Tough for most to outrun/outsmart an epidemic.
Read/watch some of the Twitter whistleblowers.
Govs have no reason to tell everyone the true risks - and every reason to hide them.
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 awful lot of people.
But there are two few confirmed cases to understand the mortality rate. Might be there are additional factors unique to China that is causing a higher death rate.
I was reading somewhere that the doubling in cases in China was mainly due to increasing capacity for hospitals to diagnose the infection rather than an actual measurement of the spread of the disease.
People are being turned away from hospitals. Dead patients are not being post-humously tested for coronavirus. Most patients hospitalized arent yet recovered (or dead) yet etc...
The only rate we know so far is the dead/hospitalized rate, which is around 13% - which comperable to SARS.
Great presentation tool.
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?
Kobe's death, while tragic, is more along the lines of current event discussion.