Hyvin tehty. 
Vastaanotto melko viattomaan ehdotukseen oli luvalla sanoen… aika raju. Amerikkalaiset käyttäisivät sanaa “harsh”. Tulee taas mieleen, olisiko reaktio ollut sama, jos sama ehdotus olisi esitetty kasvokkain vaikkapa Inderesin neukkarissa.
Nyt sitten odottelemaan Inderesin forumin kaatumista ja tietojen katoamista järjestelmistä. 
Pyörittelin tuota: Tämän hetkisin toimenpitein Italia näyttää saavan epidemian taittumaan. Tämän on sanottu edellyttävän tartuttavuusluvun saamista alle kahden. Se, miten suuri laumasuoja (engl herd immunity) vaaditaan taltuttamaan epidemia, riippuu tuosta luvusta.
1.8 edellyttää että n. 45% populaatiosta on joko infektion tai rokotuksen jäljiltä immuuneja. (Jos tartuttavuus on hyvin korkea kuten tuhkarokossa, täytyy yli 90% olla rokotettuna.)
45% italialaisista on 27 miljoonaa ja jos kuukaudessa olisi tullut 10%, päästään tasaisen vauhdin taulukolla (yksinkertaistus) 4,5 kuukauteen. Eli 3,5 kuukautta jäljellä pitäen jarrutus samalla tasolla.
Tätä pitää katsoa kriittisesti. Luku 45% kuulostaa tutulta. Sehän on veikkaus epidemian leviämisen laajuudesta esimerkiksi Suomeen. On riski, että tässä ollaan itseasiassa kehäpäätelmissä ja/tai on tehty oletuksia liiaksi painottuen muiden virusten epidemiologiaan.
Italiassa olisi em. 10% mukaan yhtä koronaan kuollutta kohti 430 tartunnan saanutta.
Etelä-Koreassa on kuollut 175. Tämän mukaan tapauksia olisi kokonaisuudessaan E-K:ssa 75 000. Intensiivisellä ja kehittyneellä altistuneiden ja oireettomien testaamisella ovat löytäneet 10 000 eli liki kertaluokan ero tässä.
Jos otetaan optimistisin luku 26% saadaan Italiassa kestoksi noin 2 kk ja jäljellä 1 kk. Toisaalta samalla tuo E-K:n tulos muuttuu yhä vaikeammin selitettäväksi.
Kertauksena aiempaan: Tieto, joka puuttuu ja antaa ennustettavuuden kuvioon saadaan vasta-ainetesteillä. (Pidän Kiinalta aika heikkona suorituksena, jos he eivät tätä ole jo tehneet.)
Ehkä rajuus tuli siitä, että viesti oli henkilön ensimmäinen viesti ja rekisteröitynyt foorumille ainoastaan tätä tarkoitusta varten. Eli ihan oikeasti maistuu sensuuri vähän suussa…
Noita kiinabotteja on paikat täynnä tekemässä samaa joka paikassa mistä Google löytää “ei-hyväksyttyjä” termejä ko. virukselle.
oli kyllä hyvä tuo linkattu/poistettu youtube video joka löytyy hakusanalla “China Hiding MASSIVE Death Toll from Coronavirus”
kyllähän siellä sanotaan ääneen mitä palstallakin ja muuallakin on Kiinan lukujen luotettabuudesta sanottu, toki en osaa sanoa kuinka luotettavia lähteitä siihen kaivettu, mutta jotain kaivettu
pahoittelut jos rikkoo jotain
Sen linkkaaminen on ihan fine, eri asia on huudella perään “CCP virusta” ynnä muuta.
Ei kait kiinalaisiin numeroihin koskaan pidä suhtautua niin kuin me numeroihin yleensä suhtaudumme.
Joku hyvin tokaisi twitterissä, taisi olla itse John Hempton, että katsokaa mitä kiinalaiset tekevät (massiiviset lock downit helmikuussa), ei mitä viranomaiset sanovat (heidän numerot näyttää). 
Mielenkiintoista nähdä, tuleeko koronaviruksesta Kiinan “Chernobyl”.
Hyvä. Videot puhukoon puolestaan…Sidenote: Tutustukaa noihin muihinkin sarjan videoihin, kovvaa kammaa. Pahoittelut, meni vähän huutelun puolelle.
“Esimerkiksi Svenska Dagbladet -lehdessä (siirryt toiseen palveluun) ihastellaan Suomen Euroopan parhaiksi kuvailtuja ja hyvin ylläpidettyjä varmuusvarastoja”
Silkkikaapuun verhottu koura ulottuu kauas - oliko koodari Yu mistä kotoisin, että osaa kaivaa Ninja-koodaustaidot esiin kun Kiinan DOS-hyökkäys tulee? 
Taisin jo alkuviikosta kehottaa kiinnittämään huomiota nipponin maalle, no nyt
No kappas! Näyttää ihan oikeasti siltä, että rapsa on vedetty pois julkisesti saatavilta. Onneksi olin kaivanut sen oleellisen jo siihen viestiin.
PDF:ää en valitettavasti ladannut vaan esikatselin sitä web-vieverillä.
Google-haun välimuisti kuitenkin vielä antaa tekstisisältöä (ilman graafeja yms.)
Klikkaa alta auki:
Tämä on html-versio tiedostosta https://www.imperial.ac.uk/media/imperial-college/medicine/sph/ide/gida-fellowships/Imperial-College-COVID19-Europe-estimates-and-NPI-impact-30-03-2020.pdf. Google muuntaa indeksoidut sivut automaattisesti html-muotoon.
Vinkki: löydät hakutermisi sivulta nopeasti painamalla Ctrl+F tai ⌘-F (Mac) ja käyttämällä näkyviin tulevaa hakupalkkia.
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Estimating the number of infections and the impact of non-
pharmaceutical interventions on COVID-19 in 11 European countries
Seth Flaxman*, Swapnil Mishra*, Axel Gandy*, H Juliette T Unwin, Helen Coupland, Thomas A Mellan, Harrison
Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc
Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper,
Zulma Cucunubá, Gina Cuomo-Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland,
Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Will Green, Timothy Hallett, Arran
Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati-Gilani, Pierre
Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang,
Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani,
Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, Neil M. Ferguson1 and Samir Bhatt*1
Department of Infectious Disease Epidemiology, Imperial College London
Department of Mathematics, Imperial College London
WHO Collaborating Centre for Infectious Disease Modelling
MRC Centre for Global Infectious Disease Analysis
Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London
Department of Statistics, University of Oxford
*Contributed equally 1Correspondence: neil.ferguson@imperial.ac.uk, s.bhatt@imperial.ac.uk
Summary
Following the emergence of a novel coronavirus (SARS-CoV-2) and its spread outside of China, Europe
is now experiencing large epidemics. In response, many European countries have implemented
unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and
universities, banning of mass gatherings and/or public events, and most recently, widescale social
distancing including local and national lockdowns.
In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact
of these interventions across 11 European countries. Our methods assume that changes in the
reproductive number – a measure of transmission - are an immediate response to these interventions
being implemented rather than broader gradual changes in behaviour. Our model estimates these
changes by calculating backwards from the deaths observed over time to estimate transmission that
occurred several weeks prior, allowing for the time lag between infection and death.
One of the key assumptions of the model is that each intervention has the same effect on the
reproduction number across countries and over time. This allows us to leverage a greater amount of
data across Europe to estimate these effects. It also means that our results are driven strongly by the
data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain.
We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact
of interventions implemented several weeks earlier. In Italy, we estimate that the effective
reproduction number, Rt , dropped to close to 1 around the time of lockdown (11th March), although
with a high level of uncertainty.
Overall, we estimate that countries have managed to reduce their reproduction number. Our
estimates have wide credible intervals and contain 1 for countries that have implemented all
interventions considered in our analysis. This means that the reproduction number may be above or
below this value. With current interventions remaining in place to at least the end of March, we
estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March
[95% credible interval 21,000-120,000]. Many more deaths will be averted through ensuring that
interventions remain in place until transmission drops to low levels. We estimate that, across all 11
countries between 7 and 43 million individuals have been infected with SARS-CoV-2 up to 28th March,
representing between 1.88% and 11.43% of the population. The proportion of the population infected
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to date – the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany
and Norway, reflecting the relative stages of the epidemics.
Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be
observed in trends in mortality, for most of the countries considered here it remains too early to be
certain that recent interventions have been effective. If interventions in countries at earlier stages of
their epidemic, such as Germany or the UK, are more or less effective than they were in the countries
with advanced epidemics, on which our estimates are largely based, or if interventions have improved
or worsened over time, then our estimates of the reproduction number and deaths averted would
change accordingly. It is therefore critical that the current interventions remain in place and trends in
cases and deaths are closely monitored in the coming days and weeks to provide reassurance that
transmission of SARS-Cov-2 is slowing.
SUGGESTED CITATION
Seth Flaxman, Swapnil Mishra, Axel Gandy et al . Estimating the number of infections and the impact of non-
pharmaceutical interventions on COVID-19 in 11 European countries. Imperial College London (2020), doi:
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1 Introduction
Following the emergence of a novel coronavirus (SARS-CoV-2) in Wuhan, China in December 2019 and
its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have
emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the
capacity of health systems to treat as many severe cases as possible, European countries, like those in
other continents, have implemented or are in the process of implementing measures to control their
epidemics. These large-scale non-pharmaceutical interventions vary between countries but include
social distancing (such as banning large gatherings and advising individuals not to socialize outside
their households), border closures, school closures, measures to isolate symptomatic individuals and
their contacts, and large-scale lockdowns of populations with all but essential internal travel banned.
Understanding firstly, whether these interventions are having the desired impact of controlling the
epidemic and secondly, which interventions are necessary to maintain control, is critical given their
large economic and social costs.
The key aim of these interventions is to reduce the effective reproduction number, , of the infection,
a fundamental epidemiological quantity representing the average number of infections, at time t, per
infected case over the course of their infection. If is maintained at less than 1, the incidence of new
infections decreases, ultimately resulting in control of the epidemic. If is greater than 1, then
infections will increase (dependent on how much greater than 1 the reproduction number is) until the
epidemic peaks and eventually declines due to acquisition of herd immunity.
In China, strict movement restrictions and other measures including case isolation and quarantine
began to be introduced from 23rd January, which achieved a downward trend in the number of
confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by
March 19th. Studies have estimated how changed during this time in different areas of China from
around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease
in the number of daily contacts per person.1,2 Control measures such as social distancing, intensive
testing, and contact tracing in other countries such as Singapore and South Korea have successfully
reduced case incidence in recent weeks, although there is a risk the virus will spread again once control
measures are relaxed.3,4
The epidemic began slightly later in Europe, from January or later in different regions.5 Countries have
implemented different combinations of control measures and the level of adherence to government
recommendations on social distancing is likely to vary between countries, in part due to different
levels of enforcement.
Estimating reproduction numbers for SARS-CoV-2 presents challenges due to the high proportion of
infections not detected by health systems1,6,7 and regular changes in testing policies, resulting in
different proportions of infections being detected over time and between countries. Most countries
so far only have the capacity to test a small proportion of suspected cases and tests are reserved for
severely ill patients or for high-risk groups (e.g. contacts of cases). Looking at case data, therefore,
gives a systematically biased view of trends.
An alternative way to estimate the course of the epidemic is to back-calculate infections from
observed deaths. Reported deaths are likely to be more reliable, although the early focus of most
surveillance systems on cases with reported travel histories to China may mean that some early deaths
will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time
lag in observing the effect of interventions on deaths since there is a 2-3-week period between
infection, onset of symptoms and outcome.
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In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in
11 European countries, inferring plausible upper and lower bounds (Bayesian credible intervals) of the
total populations infected (attack rates), case detection probabilities, and the reproduction number
over time ( Rt ). We fit the model jointly to COVID-19 data from all these countries to assess whether
there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong
assumption that particular interventions are achieving a similar impact in different countries and that
the efficacy of those interventions remains constant over time. The model is informed more strongly
by countries with larger numbers of deaths and which implemented interventions earlier, therefore
estimates of recent Rt in countries with more recent interventions are contingent on similar
intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with
greater precision.
Model and data details are presented in the appendix, validation and sensitivity are also presented in
the appendix, and general limitations presented below in the conclusions.
2 Results
The timing of interventions should be taken in the context of when an individual country’s epidemic
started to grow along with the speed with which control measures were implemented. Italy was the
first to begin intervention measures, and other countries followed soon afterwards (Figure 1). Most
interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a
2-3-week window over which to estimate the effect of interventions. Currently, most countries in our
study have implemented all major non-pharmaceutical interventions.
For each country, we model the number of infections, the number of deaths, and , the effective
reproduction number over time, with changing only when an intervention is introduced (Figure 2-
12). is the average number of secondary infections per infected individual, assuming that the
interventions that are in place at time t stay in place throughout their entire infectious period. Every
country has its own individual starting reproduction number before interventions take place.
Specific interventions are assumed to have the same relative impact on in each country when they
were introduced there and are informed by mortality data across all countries.
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Figure 1: Intervention timings for the 11 European countries included in the analysis. For further
details see Appendix 8.6.
2.1 Estimated true numbers of infections and current attack rates
In all countries, we estimate there are orders of magnitude fewer infections detected (Figure 2) than
true infections, mostly likely due to mild and asymptomatic infections as well as limited testing
capacity. In Italy, our results suggest that, cumulatively, 5.9 [1.9-15.2] million people have been
infected as of March 28th, giving an attack rate of 9.8% [3.2%-25%] of the population (Table 1). Spain
has recently seen a large increase in the number of deaths, and given its smaller population, our model
estimates that a higher proportion of the population, 15.0% (7.0 [1.8-19] million people) have been
infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000
[240,000-1,500,000] people infected.
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Table 1: Posterior model estimates of percentage of total population infected as of 28th March 2020.
2.2 Reproduction numbers and impact of interventions
Averaged across all countries, we estimate initial reproduction numbers of around 3.87 [3.01-4.66],
which is in line with other estimates.1,8 These estimates are informed by our choice of serial interval
distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in
lower starting reproduction numbers (Appendix 8.4.2, Appendix 8.4.6). The initial reproduction
numbers are also uncertain due to (a) importation being the dominant source of new infections early
in the epidemic, rather than local transmission (b) possible under-ascertainment in deaths particularly
before testing became widespread.
We estimate large changes in in response to the combined non-pharmaceutical interventions. Our
results, which are driven largely by countries with advanced epidemics and larger numbers of deaths
(e.g. Italy, Spain), suggest that these interventions have together had a substantial impact on
transmission, as measured by changes in the estimated reproduction number Rt . Across all countries
we find current estimates of Rt to range from a posterior mean of 0.97 [0.14-2.14] for Norway to a
posterior mean of 2.64 [1.40-4.18] for Sweden, with an average of 1.43 across the 11 country posterior
means, a 64% reduction compared to the pre-intervention values. We note that these estimates are
contingent on intervention impact being the same in different countries and at different times. In all
countries but Sweden, under the same assumptions, we estimate that the current reproduction
number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher,
not because the mortality trends are significantly different from any other country, but as an artefact
of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so
far. Overall, we cannot yet conclude whether current interventions are sufficient to drive below 1
(posterior probability of being less than 1.0 is 44% on average across the countries). We are also
unable to conclude whether interventions may be different between countries or over time.
There remains a high level of uncertainty in these estimates. It is too early to detect substantial
intervention impact in many countries at earlier stages of their epidemic (e.g. Germany, UK, Norway).
Many interventions have occurred only recently, and their effects have not yet been fully observed
due to the time lag between infection and death. This uncertainty will reduce as more data become
available. For all countries, our model fits observed deaths data well (Bayesian goodness of fit tests).
We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding
the latest 3 days of data and comparing model predictions to observed deaths (Appendix 8.3).
The close spacing of interventions in time made it statistically impossible to determine which had the
greatest effect (Figure 1, Figure 4). However, when doing a sensitivity analysis (Appendix 8.4.3) with
uninformative prior distributions (where interventions can increase deaths) we find similar impact of
Country
% of total population infected (mean [95% credible interval])
Austria
1.1% [0.36%-3.1%]
Belgium
3.7% [1.3%-9.7%]
Denmark
1.1% [0.40%-3.1%]
France
3.0% [1.1%-7.4%]
Germany
0.72% [0.28%-1.8%]
Italy
9.8% [3.2%-26%]
Norway
0.41% [0.09%-1.2%]
Spain
15% [3.7%-41%]
Sweden
3.1% [0.85%-8.4%]
Switzerland
3.2% [1.3%-7.6%]
United Kingdom
2.7% [1.2%-5.4%]
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interventions, which shows that our choice of prior distribution is not driving the effects we see in the
main analysis.
(A) Austria
(B) Belgium
(C) Denmark
(D) France
0
10000
20000
30000
40000
D
a
ily n
u
m
b
e
r o
f in
fe
c
tio
n
s
0
5
10
15
20
time
d
e
a
th
s
0
2
4
6
R
t
Interventions
Complete lockdown
Public events banned
School closure
Self isolation
Social distancing
50%
95%
0
50000
100000
150000
D
a
ily n
u
m
b
e
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f in
fe
c
tio
n
s
0
20
40
60
time
d
e
a
th
s
0
2
4
6
R
t
Interventions
Complete lockdown
Public events banned
School closure
Self isolation
Social distancing
50%
95%
0
5000
10000
15000
20000
25000
D
a
ily n
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m
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f in
fe
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n
s
0
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time
d
e
a
th
s
0
2
4
R
t
Interventions
Complete lockdown
Public events banned
School closure
Self isolation
Social distancing
50%
95%
0e+00
2e+05
4e+05
6e+05
D
a
ily n
u
m
b
e
r o
f in
fe
c
tio
n
s
0
100
200
300
400
time
d
e
a
th
s
0
2
4
R
t
Interventions
Complete lockdown
Public events banned
School closure
Self isolation
Social distancing
50%
95%
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(E) Germany
(F) Italy
(G) Norway
(H) Spain
0
50000
100000
150000
200000
D
a
ily n
u
m
b
e
r o
f in
fe
c
tio
n
s
0
25
50
75
time
d
e
a
th
s
0
2
4
6
R
t
Interventions
Complete lockdown
Public events banned
School closure
Self isolation
Social distancing
50%
95%
0
500000
1000000
1500000
D
a
ily n
u
m
b
e
r o
f in
fe
c
tio
n
s
0
500
1000
1500
time
d
e
a
th
s
0
1
2
3
4
R
t
Interventions
Complete lockdown
Public events banned
School closure
Self isolation
Social distancing
50%
95%
0
2000
4000
6000
D
a
ily n
u
m
b
e
r o
f in
fe
c
tio
n
s
0
1
2
3
4
5
time
d
e
a
th
s
0
2
4
R
t
Interventions
Complete lockdown
Public events banned
School closure
Self isolation
Social distancing
50%
95%
0e+00
1e+06
2e+06
3e+06
D
a
ily n
u
m
b
e
r o
f in
fe
c
tio
n
s
0
300
600
900
time
d
e
a
th
s
0
2
4
6
R
t
Interventions
Complete lockdown
Public events banned
School closure
Self isolation
Social distancing
50%
95%
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Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown
bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval (CI),
light blue 95% CI. The number of daily infections estimated by our model drops immediately after an
intervention, as we assume that all infected people become immediately less infectious through the
intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again.
Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI
as in left plot. Right: time-varying reproduction number , dark green 50% CI, light green 95% CI.
Icons are interventions shown at the time they occurred.
(I) Sweden
(J) Switzerland
(K) United Kingdom
0
50000
100000
150000
D
a
ily n
u
m
b
e
r o
f in
fe
c
tio
n
s
0
10
20
time
d
e
a
th
s
0
2
4
6
R
t
Interventions
Complete lockdown
Public events banned
School closure
Self isolation
Social distancing
50%
95%
0
25000
50000
75000
D
a
ily n
u
m
b
e
r o
f in
fe
c
tio
n
s
0
20
40
60
time
d
e
a
th
s
0
2
4
R
t
Interventions
Complete lockdown
Public events banned
School closure
Self isolation
Social distancing
50%
95%
0e+00
1e+05
2e+05
3e+05
4e+05
5e+05
D
a
ily n
u
m
b
e
r o
f in
fe
c
tio
n
s
0
50
100
150
200
time
d
e
a
th
s
0
2
4
R
t
Interventions
Complete lockdown
Public events banned
School closure
Self isolation
Social distancing
50%
95%
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Table 2: Total forecasted deaths since the beginning of the epidemic up to 31 March in our model
and in a counterfactual model (assuming no intervention had taken place). Estimated averted deaths
over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals.
Country
Observed
Deaths to 28th
March
Model estimated
deaths to 28th
March
Model
estimated
deaths to 31
March
Model
estimated
deaths to 31
March
Model deaths
averted to 31
March
(observed)
(our model)
(our model)
(counterfactual
model
assuming no
interventions
have occurred)
(difference
between
counterfactual
and actual)
Austria
68
88 [57 - 130]
140 [88 - 210] 280 [140 -
560]
140 [34 -
380]
Belgium
289
310 [230 - 420] 510 [370 -
730]
1,100 [590 -
2,100]
560 [160 -
1,500]
Denmark
52
61 [38 - 92]
93 [58 - 140] 160 [84 - 310] 69 [15 - 200]
France
1,995
1,900 [1,500 -
2,500]
3,100 [2,300 -
4,200]
5,600 [3,600 -
8,500]
2,500 [1,000
Germany
325
320 [240 - 410] 570 [400 -
810]
1,100 [570 -
2,400]
550 [91 -
1,800]
Italy
9,136
10,000 [8,200 -
13,000]
14,000
[11,000 -
19,000]
52,000
[27,000 -
98,000]
38,000
[13,000 -
84,000]
Norway
16
17 [7 - 33]
26 [11 - 51]
36 [14 - 81]
9.9 [0.82 -
38]
Spain
4,858
4,700 [3,700 -
6,100]
7,700 [5,500 -
11,000]
24,000
[13,000 -
44,000]
16,000
[5,400 -
35,000]
Sweden
92
89 [61 - 120]
160 [110 -
240]
240 [140 -
440]
82 [12 - 250]
Switzerland
197
190 [140 - 250] 310 [220 -
440]
650 [330 -
1,500]
340 [71 -
1,100]
United
Kingdom
759
810 [610 -
1,100]
1,500 [1,000 -
2,100]
1,800 [1,200 -
2,900]
370 [73 -
1,000]
All
17,787
19,000 [16,000 -
22,000]
28,000
[23,000 -
36,000]
87,000
[53,000 -
140,000]
59,000
[21,000 -
120,000]
Page 11
30 March 2020
Imperial College COVID-19 Response Team
Page 11 of 35
2.3 Estimated impact of interventions on deaths
Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31
March under our fitted model and under the counterfactual model, which predicts what would have
happened if no interventions were implemented (and = 0 i.e. the initial reproduction number
estimated before interventions). Again, the assumption in these predictions is that intervention
impact is the same across countries and time. The model without interventions was unable to capture
recent trends in deaths in several countries, where the rate of increase had clearly slowed (Figure 3).
Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely
applicable information criterion assessments – WAIC).
By comparing the deaths predicted under the model with no interventions to the deaths predicted in
our intervention model, we calculated the total deaths averted up to the end of March. We find that,
across 11 countries, since the beginning of the epidemic, 59,000 [21,000-120,000] deaths have been
averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 [13,000-
84,000] and 16,000 [5,400-35,000] deaths have been averted, respectively. Even in the UK, which is
much earlier in its epidemic, we predict 370 [73-1,000] deaths have been averted.
These numbers give only the deaths averted that would have occurred up to 31 March. If we were to
include the deaths of currently infected individuals in both models, which might happen after 31
March, then the deaths averted would be substantially higher.
(a) Italy
(b) Spain
Edit. Inderes maksimi merkkimäärä 30.000, joten siivottu 2/3 dokkarista pois.
Edit 2. Kiitos @OldFeki - pdf alla
Huom. Kirjoittelin jo aikaisemmin, että Italiassa koronatilastoihin otetaan vain sairaalaan saapuneet. Kotona kuolleet tai hoitokodissa tai muualla ei rekisteröidä.
Esim. siellä jossain Italian kylässä oli kokonaisuudessan koronaan kuolleita 3-4x(?) virallisiin tilastoihin.
Ranskassa oli samansuuntaista politiikkaa, ja fiksasivat sitä nyt.
Jos siis Italiassa virallisesti nyt 14.000 kuollutta, niin todellinen lukema lienee 30.000-60.000 kuollutta.
Ja tuo epävarmuus vaikuttaa toki sitten merkittävästi sekä kuolleisuuden että epidemian levinneisyyden arviointiin.
Sori, kaikella kunnioituksella ja rakkaudella mut oli pakko laittaa tää tähän ihan puhtaasti huumorimielessä vaikka asia on jo loppuun käsitelty 
Kohta 2:27-2:40.
Jos on niin, että Italiassa kuolleita on moninkertaisesti enemmän kuin viralliset luvut, niin on todennäköistä, että Kiinassa sama tilanne. Siten arvio kymmenistä tuhansista uhreista Kiinassa lienee oikean suuntainen.
Toivottavasti THL ei ihan suoraan ja kyseenalaistamatta perusta arvioitaan Kiinan virallisille luvuille. Valitettavasti niin se ilmeisesti tekee.
In the hardest-hit area around the city of Bergamo, some 4,500 people died of coronavirus in March, according to data analysis firm InTwig, while only 2,060 were included in the data provided by the Civil Protection Agency.
In Bergamo, fatalities more than quadrupled, while they increased between two- and three-fold in several other Lombardy cities. In some small towns at the heart of the outbreak they were up 10-fold this year compared with 2019.
Jäin miettimään eilisiä USA:n työttömyystilastoja. Työttömäksi jääminen on USA:ssa henkilökohtainen taloudellinen katastrofi tällaisina aikoina, kun korvaavaa työtä ei löydy helpolla. Mutta vielä pahemmaksi tilanteen tekee se, että ne 10 miljoonaa viimeisen parin viikon aikana työttömäksi jäänyttä tipahtivat samalla terveysvakuutusten piiristä.
Jonkinlainen vakuutus on lähes kaikilla työntekijöillä USA:ssa ja monet vakuutukset kattavat perheenjäsenetkin. Huonoimmatkin vakuutukset kattavat ‘normaalin’ sairastumisen hoitokulut. Mutta työttömäksi jääneillä… siinä jos vielä Corona iskee niin ei paljon kamalammaksi tilanne voi tulla.
Oliko jo täällä? Aika mielenkiintoinen juttu. Ainahan sanotaan, että google tietää meistä kaiken. Olisiko nyt hyödyksi. Tuolta katsottuna näyttää, että esimerkiksi Etelä-Korea on oikeasti varsin “auki”. Italia ja Espanja aivan lock downissa. Britit ja saksalaiset kuuliaisia. Ruotsalaiset huolettomina. Data 2-3 päivän viiveellä.
Lähdekritiikkiä/analyysiä mobiilidatan tuntijoilta?
Äkkisältään tässä ihmetytti Suomen “vain” 25% pudotus työpaikoilla kaikkien etätöiden, suljettujen ravintoloiden, hotellien, koulujen, kivijalkaliikkeiden ym. seurauksena.
Muistelen lukeneeni juttua, että koronan hoito olisi kuitenkin ilmaista USAssa?
