China syndrome

The chart above shows cases (C), deaths  (D) and recoveries (R) in the Chinese province of Henan, 22 January – 7 March 2020 (source John Hopkins).

As you can see, the number of cases rises rapidly until early February, then the rise slows, and from mid February tails off completely. With a delay of about two weeks the number  of recoveries rises, then falls, following the same pattern. Thus there are now 1,272 diagnosed cases in Henan, with 1,243 recoveries, and only 7 unresolved cases, and 7 deaths.

With a population of 95m in Henan, that means that the fatality ratio of cases resolved is 1 in 58, but as a ratio of the general population it is only 1 in 4,333,000.

Does that mean there is no cause for worry?

Continue reading “China syndrome”

Central planning works?

Never thought Eumaeus would say that, but if the data is right (sourced from John Hopkins) then the chart above shows how, for three Chinese provinces (Guangdong, Henan, Hunan) the rise in new cases has been almost completely stopped.

Bruce Aylward (Assistant Director-General of the World Health Organization, and the leader of the WHO team that visited Wuhan in China) explains here how they did it.

His main target, and the message bears repeating endlessly, is the fallacy that only an authoritarian regime can accomplish what was done in China.

We know it’s a bad dangerous disease. How do we bring the mortality down? You concentrate your resources, you shorten the time frame to make sure you can identify very quickly, you look at the high risk ones and get them into the specialised centres in case they crash, these are the things that we should focus on.

[EDIT] For balance, here is Gordon Change explaining how China has “bought the WHO”, and questioning the statistics (see chart above) apparently showing that infections are “on the downslope”. “Whenever you have statistics supporting a leader’s policies, you’ve got to be very concerned”.

As we have reiterated, the conclusion depends on the accuracy of the data, and accuracy of data is hard to determine when all we have is the data. As Wittgenstein said, you can’t check what is said in the newspaper by buying a second copy.

 

Diamond Princess update

Data so far suggests that of the 3,711 people on the Diamond Princess cruise ship, 705 contracted the virus, i.e. 19%. Much higher than in any place in China, as we have stated passim, but then a cruise ship is a confined space with a lot of people in it. So far, 6 people appear to have died, which is 0.85% of those diagnosed. The mortality relative to the whole population is therefore 6 divided by 3,711, i.e. 0.16%.

These figures conflict somewhat with the crude distribution we published here, but we did say to treat all figures with caution. There is a conflict because, as is well known, mostly old people like us go on cruises, but we are seeing nothing like an 18% case fatality rate.

[EDIT] A useful paper on the Diamond Princess age distribution is here. Table copied below.

Age group Symptomatic cases Asymptomatic cases 1 Total Crude asymptomatic ratio 2 Persons aboard3
0-9 0 1 1 100% (95%CI: 2.5%, 100%) 16
10- 1 1 2 50.0% (95%CI: 1.3%, 98.7%) 23
20- 18 2 20 10.0% (95%CI: 1.2, 31.7%) 347
30- 18 5 23 21.7% (95%CI: 7.5%, 43.7%) 429
40- 18 7 25 28.0% (95%CI: 12%, 49.4%) 333
50- 27 22 49 44.9% (95%CI: 30.1%, 59.8%) 398
60- 73 56 129 43.4% (95%CI: 2.5, 100%) 924
70- 92 136 228 59.6% (95%CI: 53.0%, 66.1%) 1015
80- 27 25 52 48.1% (95%CI: 34.0%, 62.3%) 215
90- 2 0 2 0% (95%CI: 2.5%, 84.2%) 11

Resolution and recovery

As we suggested earlier, unresolved (existing) cases (E) tend to resolve into recovery rather than death. The table below shows this clearly.  Between 28 February and 1 March, there were 79 deaths in Hubei, but 5,133 recoveries. By the same token, the apparent fatality rate also falls.

Place Date C D R E D/(D+R)
Hubei 28-Feb-20 65,914 2,682 26,403 36,829 9.22%
Hubei 29-Feb-20 66,337 2,727 28,930 34,680 8.61%
Hubei 01-Mar-20 66,907 2,761 31,536 32,610 8.05%

Note again that the percentage of cases is small compared to the population. The population of Hubei is about the same as the UK. So about 1 in 1,000 have been diagnosed with the disease since it began.  Only because of draconian isolation measures, however, which is why the impact on the economy is so severe.

Conflicting data

The table below shows, for 28 February 2020, the diagnosed cases (C), deaths (D), and recoveries (R) from 25 parts of China. Data from John Hopkins University.

The usual caveats apply. 

The last three columns are a function of the primary data. Existing cases (E) is C-(D+R), i.e. diagnosed cases less resolved cases. D/(D+R) is one method for estimating the case fatality ratio. E/C is the unresolved cases divided by diagnosed cases. This ratio will fall to zero over time given that all cases will resolve into recoveries or deaths. The table is sorted by this number.

As is evident, the fatality ratio varies wildly, from Jiangxi, where 790 out of 935 cases have already been resolved, with only one death, to Hubei where little more than half the 65,914 cases have been resolved, with a 9.22% apparent fatality ratio.

Time series analysis (not shown here) suggests that unresolved cases (E) tend to resolve into recovery rather than death, hence there is a moderately strong correlation (0.6) between E and apparent fatality.

There is no explanation yet of why cases should have taken so long to resolve in Hubei, which the media call the epicentre of the outbreak. Note also, as before, that the cases in Hubei are a tiny fraction of its population.

In other news, manufacturing activity in China in February plunged faster than during the 2008 financial crisis.

Place Date C D R E D/(D+R) E/C
Qinghai 28-Feb-20 18 0 18 0 0.00% 0%
Gansu 28-Feb-20 91 2 82 7 2.38% 8%
Yunnan 28-Feb-20 174 2 156 16 1.27% 9%
Henan 28-Feb-20 1,272 20 1,112 140 1.77% 11%
Hebei 28-Feb-20 318 6 277 35 2.12% 11%
Jiangxi 28-Feb-20 935 1 790 144 0.13% 15%
Shanghai 28-Feb-20 337 3 279 55 1.06% 16%
Anhui 28-Feb-20 990 6 821 163 0.73% 16%
Hunan 28-Feb-20 1,017 4 830 183 0.48% 18%
Shanxi 28-Feb-20 133 0 109 24 0.00% 18%
Shaanxi 28-Feb-20 245 1 199 45 0.50% 18%
Jiangsu 28-Feb-20 631 0 515 116 0.00% 18%
Zhejiang 28-Feb-20 1,205 1 975 229 0.10% 19%
Fujian 28-Feb-20 296 1 235 60 0.42% 20%
Jilin 28-Feb-20 93 1 73 19 1.35% 20%
Guizhou 28-Feb-20 146 2 112 32 1.75% 22%
Liaoning 28-Feb-20 121 1 93 27 1.06% 22%
Tianjin 28-Feb-20 136 3 102 31 2.86% 23%
Xinjiang 28-Feb-20 76 3 52 21 5.45% 28%
Chongqing 28-Feb-20 576 6 402 168 1.47% 29%
Beijing 28-Feb-20 410 7 257 146 2.65% 36%
Sichuan 28-Feb-20 538 3 338 197 0.88% 37%
Shandong 28-Feb-20 756 6 405 345 1.46% 46%
Hubei 28-Feb-20 65,914 2,682 26,403 36,829 9.22% 56%

 

Winners and losers

Mostly losers, to be honest.

Harry Hindsight says to have loaded up on healthcare (NMC), makers of surgical masks and stuff (Bunzl) and pharma (Hikma), and dumped airlines (Easyjet, ICAG), but he has never been a friend in need.

 

Name Price 28/2 Price last week Total gain/loss Pct
Easyjet PLC 1,059.21 1,464.2 -405.00 -27.7%
Melrose Industries PLC 208.8 280.4 -71.60 -25.5%
International Consolidated Airlines Group SA 473.1 623.1 -150.00 -24.1%
Legal & General Group PLC 255 313.9 -58.90 -18.8%
Standard Life Aberdeen PLC 268.9 323.0 -54.10 -16.7%
Schroders PLC 2,834.00 3,376.0 -542.00 -16.1%
Persimmon PLC 2,756.00 3,282.0 -526.00 -16.0%
BHP Group PLC 1,399.40 1,661.4 -262.00 -15.8%
Prudential PLC 1,260.44 1,489.4 -229.00 -15.4%
Halma PLC 1,909.50 2,227.5 -318.00 -14.3%
Tesco PLC 219.5 255.7 -36.20 -14.2%
3i Group Plc 1,006.50 1,171.5 -165.00 -14.1%
Ashtead Group PLC 2,358.00 2,741.0 -383.00 -14.0%
Aviva PLC 347.7 404.1 -56.40 -14.0%
Meggitt PLC 531.4 615.8 -84.40 -13.7%
Smiths Group PLC 1,507.50 1,745.5 -238.00 -13.6%
Compass Group PLC 1,689.00 1,953.0 -264.00 -13.5%
Phoenix Group Holdings PLC 682.5 788.5 -106.00 -13.4%
AVEVA Group PLC 4,234.00 4,870.0 -636.00 -13.1%
BP PLC 394.65 453.6 -58.90 -13.0%
DCC PLC 5,502.00 6,252.0 -750.00 -12.0%
Royal Dutch Shell PLC 1,662.00 1,888.0 -226.00 -12.0%
WM Morrison Supermarkets PLC 164.55 186.0 -21.40 -11.5%
Reckitt Benckiser Group PLC 5,676.00 6,414.0 -738.00 -11.5%
Ferguson PLC 6,728.00 7,566.0 -838.00 -11.1%
Intertek Group PLC 5,202.00 5,844.0 -642.00 -11.0%
Experian PLC 2,549.00 2,863.0 -314.00 -11.0%
Diageo PLC 2,738.26 3,072.3 -334.00 -10.9%
Sage Group PLC 690.8 773.8 -83.00 -10.7%
J Sainsbury PLC 188.11 210.7 -22.59 -10.7%
Spirax-Sarco Engineering PLC 8,365.00 9,365.0 -1,000.00 -10.7%
Coca Cola HBC AG 2,507.00 2,798.0 -291.00 -10.4%
RSA Insurance Group PLC 510.6 569.0 -58.40 -10.3%
London Stock Exchange Group PLC 7,568.00 8,422.0 -854.00 -10.1%
Smith & Nephew PLC 1,735.00 1,924.0 -189.00 -9.8%
BAE Systems PLC 604.4 669.0 -64.60 -9.7%
Relx PLC 1,873.50 2,072.5 -199.00 -9.6%
SSE PLC 1,530.50 1,686.5 -156.00 -9.2%
Unilever PLC 4,188.00 4,593.0 -405.00 -8.8%
Imperial Brands PLC 1,580.65 1,728.7 -148.00 -8.6%
AstraZeneca PLC 6,934.00 7,544.0 -610.00 -8.1%
National Grid PLC 988 1,063.8 -75.80 -7.1%
GlaxoSmithKline PLC 1,563.00 1,658.2 -95.20 -5.7%
Rentokil Initial PLC 482.3 508.4 -26.10 -5.1%
Rolls-Royce Holdings PLC 628.8 662.4 -33.60 -5.1%
Hikma Pharmaceuticals PLC 1,832.50 1,927.5 -95.00 -4.9%
Bunzl plc 1,895.00 1,948.5 -53.50 -2.7%
NMC Health PLC 938.4 855.2 83.20 9.7%

Grim distribution

The table below is from Worldometers, based on a paper by the Chinese CCDC released on February 17 and published in the Chinese Journal of Epidemiology4

I would treat all such statistics with caution, but here they are anyway.

AGE DEATH RATE
80+ years old 14.8%
70-79 years old 8.0%
60-69 years old 3.6%
50-59 years old 1.3%
40-49 years old 0.4%
30-39 years old 0.2%
20-29 years old 0.2%
10-19 years old 0.2%
0-9 years old 0.0%

Death Rate is number of deaths divided by number of known cases of Coronavirus, but we still don’t know the ratio of known to unknown cases.

Grim reaper mathematics

The reporting of the corona virus outbreak makes almost no sense to Eumaeus – so he won’t comment.

But this site has some helpful statistics and explanations, and this paper published 7 February outlines the methodology for estimating case fatality rate, as well as (v important) the flaws inherent in the models used to estimate it.  See also this paper on a much earlier epidemic, which discusses many of the same problems.

One problem is to estimate the number of cases, particularly difficult when the disease may never show any symptoms. Dividing the number of deaths by the number of reported cases, i.e. those where the patient had symptoms, reported them and was correctly diagnosed, may grossly overestimate the fatality rate. General insurance actuaries may compare this to IBNR.

There is also the problem that if the disease is prolonged, there may be many cases where the outcome is not known, hence the correct method is to divide deaths by the number of cases reported days or weeks ago, where the ‘days or weeks’ is given by some estimate (again, another estimate) of the period from diagnosis to outcome.

[edit] See also the DXY site , a platform run by members of the Chinese medical community, aggregating media and government reports giving COVID-19 cumulative cases in near real-time.