Full lockdown policies in Western Europe countries have no evident

impacts on the COVID-19 epidemic.

Thomas Meunier1,2∗

1 Woods Hole Oceanographic Institution, Falmouth, Massachusetts2 Ensenada Center for Scientific Research and Higher Education, Ensenada, BC

April 24, 2020

Abstract

This phenomenological study assesses the impactsof full lockdown strategies applied in Italy, France,Spain and United Kingdom, on the slowdown of the2020 COVID-19 outbreak. Comparing the trajectoryof the epidemic before and after the lockdown, wefind no evidence of any discontinuity in the growthrate, doubling time, and reproduction number trends.Extrapolating pre-lockdown growth rate trends, weprovide estimates of the death toll in the absence ofany lockdown policies, and show that these strategiesmight not have saved any life in western Europe. Wealso show that neighboring countries applying lessrestrictive social distancing measures (as opposed topolice-enforced home containment) experience a verysimilar time evolution of the epidemic.

Introduction

The recent COVID-19 outbreak in Europe haschallenged the governments responsiveness in frontof an unpredictable and unprecedented situation.Since most countries were unprepared to face suchan unexpected epidemic, lack of testing capacitiesyielded most policies to shift towards social distanc-ing measures rather than modern laboratory-basedquarantine [10]. A broad range of public actions were

∗Correspondence to T. Meunier, tmeunier@whoi.edu

taken in response to the epidemic, from no action atall (Sweden) to full lockdown (Italy, France, Spainand United Kingdom), including police-enforcedhome containment. Other countries, such as theNetherlands and Germany, opted for a measuredresponse, encouraging social distancing withoutlocking their population down.While new medical treatments proposed to cureCOVID-19 cases are required to be validated throughcontrolled double blind studies, the benefits and risksof social distancing strategies are not subject to anycomparative tests. However, full lockdown measures,such as those decided in Italy, France, Spain andUnited Kingdom have not been experienced in West-ern Europe countries for centuries, and their effectsin contemporary population’s mental and physicalhealth is largely unknown. The COVID-19 epidemicepisode was shown to, by itself, affect mental health,including anxiety syndromes and depression [21]and the consequences of isolation could enhancethese conditions. In the absence of any controlgroup, the impacts on western Europe’s populationwill not be measurable until months. Nevertheless,increased mortality due to difficulties of accessto basic health care, increased mental conditionslinked to isolation, as well as social consequences ofeconomic recession, despite being unquantifiable sofar, is to be expected. Such measures are thus onlyappropriate if their impacts on limiting the epidemicspreading save more lives than their inherent death

1

All rights reserved. No reuse allowed without permission. was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint (whichthis version posted May 1, 2020. .https://doi.org/10.1101/2020.04.24.20078717doi: medRxiv preprint

toll. Attempting a real-time assessment of fulllockdown policies efficiency thus seems crucial tohelp public action decisions in the forthcoming weeks.

Recent modeling results suggestes that China’sfull lockdown policy was successful in containing theepidemic [9]. In an attempt to predict the efficiencyof similar policies in Western Europe countries (Italy,France, Spain and United Kingdom), Picchioti elal. (2020) [16] implemented a SEIR model, testingdifferent lockdown parameterizations, and suggestedthat early public containment measures could beefficient. However, as acknowledged by the authors,real-time parameterization of a model for an un-known disease is a difficult and uncertain task, andthe effects of lockdown may vary from one countryto another. Although modeling studies offer valuableinsights and possible scenarii for forthcoming events,and might provide a deep understanding of theepidemic’s dynamics a posteriori, they require vali-dation, which can only be provided by thorough dataanalysis. In that regard, the observational efforts ofTobias (2020) [20] represent an interesting approach.The latter recently claimed that the full lockdownpolicies in Spain and Italy have had positive resultsin slowing the epidemic. However, their methods,based on fitting linear trends to the logarithm ofthe daily new cases and daily death numbers, andcomparing them before, and after the lockdownpolicies, might not be appropriate. As will be shownbelow, to assess the trajectory of the epidemic, oneshould look for trends in the time derivative of thelogarithm of daily numbers, rather than trends inthe logarithm of the daily numbers itself.

Here, we show that the available data exhibit noevidence for any effects of the full lockdown policiesapplied in Italy, Spain, France and United Kingdomin the time evolution of the COVID-19 epidemic. Us-ing a phenomenological approach, we compare theevolution of the epidemic before and after the fulllockdown measures are expected to produce visibleresults. Our approach have similarities with Tobias(2020)’s [20]: it is focused on incident rather thancumulative data, and it compares pre-lockdown andpost-lockdown trends. However, here, no positive

changes are noticed in the trend of the daily deathgrowth rate, doubling time, or reproduction number,weeks after lockdown policies should have impacts.

1 Methods

Although Epidemic outbreaks are complex dynamicalsystems, the daily new cases number most generallyfollows a similar time evolution: after an exponen-tial growth, infections slow and eventually decay ex-ponentially as, whether group immunity is reached,or seasonal factors or public actions slow the virusreproduction. This behavior has been observed forseasonal influenza [14], H1N1 [7] as well as for re-cent coronavirus epidemics such as SARS [1, 5, 8] orMERS [3]. It is usually well described by exponen-tial functions such as the logistic distribution or theGauss function. The Gauss function is defined herefor time evolution of new cases as:

c(t) = e(t−Tp)2

τ2 (1)

where c(t) is the daily number of new cases, t is time,Tp is the time of the peak (maximum infection), and τis a time scale defining the duration of the epidemic.In the assumption of a steady relationship betweenthe number of cases and the number of fatalities (thefatality rate µ is time-independent), the daily deathnumber d(t) is linked to the daily new cases numberc(t) through the time-lagged proportionality relation:

d(t) = µc(t− Td), (2)

where Td is the average time between infection anddeath. It thus follows a similar Gaussian-like law.However, the latter never is a purely Gaussian func-tion, and is often skewed or exhibits more complexpatterns [8], so that the definition of Eq (1) is notexactly correct and needs to be generalized.It is natural and convenient to express time evolutionof c(t) as a power function with a time varying expo-nent. As for any strictly positive function, c(t) canbe written in the form of a generalized exponentialfunction:

c(t) = c(0)eγ(t)t (3)

2

All rights reserved. No reuse allowed without permission. was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint (whichthis version posted May 1, 2020. .https://doi.org/10.1101/2020.04.24.20078717doi: medRxiv preprint

where the time varying function γ(t) will be referredto as the growth rate of c(t). Expressing time evolu-tion of the daily new cases number in the form of Eq(3) can be thought of as a generalization of Richards’sphenomenological model [18].

If γ is a constant, c(t) is a pure exponential. Ifγ(t) is a linearly decaying function of the form γ(t) =β + αt, with α < 0, c(t) is a Gauss function, and Tpand τ can be expressed in terms of the slope andintercept of γ(t):

τ =

√−1

α, (4)

Tp = −−β2α

(5)

In that case, the short time asymptotic limit is closeto a pure exponential growth, since t << Tp. It thenslows down as t approaches Tp, and starts to decayas t passes Tp. Eq (3) however allows any form ofγ(t) and is not restricted to Gaussian or exponentialbehaviors. Values of γ(t) can be retrieved from anytime series of the daily new cases number, or equiva-lently the daily death number :

γ(t) = ln(d(t+ δt)

d(t))/δt =

ln(d(t+ δt)) − ln(d(t))

δt(6)

γ(t) can thus be defined as the time derivative of thenatural logarithm of the daily death number. Thismethod is commonly used in the study of transientperturbations growth in fluid mechanic’s generalizedstability theory [6, 12]. In this work, we will beprimarily studying the time variations of γ, andsearch for visible trends in the latter.

Since the time necessary for the number of fatalitiesto double (hereafter doubling time) is a commonlyused diagnostic of an epidemic evolution, it is alsocomputed in this work. The total number of deaths attime t is the time integral of the daily death number:

D(t) =

∫ t

0

d(s)ds (7)

Since D(t) is also a strictly positive function, it canbe expressed in a similar form as Eq (3):

D(t) = D(0)eΓ(t)t (8)

and Γ(t) can be retrieved as in Eq (6):

Γ(t) =ln(D(t+ δt)) − ln(D(t))

δt(9)

The doubling time (T2) is related to Γ following:

T2 =ln(2)

Γ(10)

so that we can obtain instantaneous estimatesof T2(t) without waiting for the number of totalfatalities to actually double.

To assess the efficiency of lockdown policies, wefirst compute the growth rate γ(t) from the dailydeaths observations and apply linear regression toestimate its trend before the lockdown should haveany visible effects (t < Tld+Td, where Tld is the startdate of the lockdown measures). We then predictvalues for each variable of interest after the lockdownshould have visible effects by extrapolating the linearevolution of γ(t) after this date. This allows us tocompare observed values of growth rate, daily deaths,doubling time, and total fatalities number, withthe values expected from the pre-lockdown trend(what would have happened if nothing had changed).

To assess the evolution of the epidemic with a moreclassical approach, we also compute an instantaneousreproduction number as well as an estimate of the re-production number, based on the daily deaths dataand Eq (2), which links the daily deaths number, thefatality rate, and the daily new cases number. Thereproduction number is the number of secondary in-fections provoked by a typical case [1]. In practice,the reproduction number shows large variability de-pending on a number of factors such as the age orthe region [15]. However, a mean estimate is usefulto assess the epidemic stage. Here, we approximatethe instantaneous reproduction number Ri as the ra-tio of the number of new cases and the total numberof contagious cases at time t:

Ri(t) =c(t)∫ t

t−Tc(c(s) − d(s))ds, (11)

where Tc is the time during which an infected personremains contagious, and s is an integration variable.

3

All rights reserved. No reuse allowed without permission. was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint (whichthis version posted May 1, 2020. .https://doi.org/10.1101/2020.04.24.20078717doi: medRxiv preprint

It is important to note that we make the assump-tions that new cases start to be contagious right af-ter infection (zero generation time), and that all caseswithin the contagion period Td are equally likely toproduce secondary infections. While our simplifyinghypothesis of zero generation time might yield to anunderestimate of the reproduction number, it doesnot affect its general trend, which is what this studyis focused on. The reproduction number R, which isthe number of persons that will be infected by eachcontagious person during the time Tc is approximatedas:

R(t) =

∫ t+Tc

t

Ri(s)ds (12)

The epidemic is in a growing phase if R > 1 anddecays otherwise.

2 Data

Because of the important proportion of asymp-tomatic cases of COVID-19 [2, 17, 13] and ofthe testing policies of most countries, which arerestrained to severe and potentially deadly cases, thedaily number of new confirmed cases is not a reliablevariable to assess the evolution of the epidemic. Wethus only used the daily deaths number to estimatethe growth rates and their trends. The daily numberof new cases is inferred from the daily death numberand a fixed fatality rate using Eq (2).The data used in this study are produced by theEuropean Center for Disease Prevention and Control(ECDC). Because of a lack of daily deaths reportsin nursing homes in France until 02 April 2020,that were suddenly corrected in the dataset, weonly use hospital deaths data for this country, forconsistency of the time series. The daily and totaldeaths numbers in France are thus greatly underes-timated in our study, but one should keep in mindthat we are focused on time evolution and trendsrather than absolute values, so that time-consistencyis the single most important requirement for the data.

To compute the growth rate of the daily deathsnumber γ(t) and of the total fatalities number Γ(t)as well as the doubling time T2(t), the daily deathsdata are first low-pass filtered using a 3-days runningmean on the logarithm ln(d(t)).

The values used here for the time between infec-tion and death Td ranges between 14 and 20 days,with a median reference value of 17 days. It cor-responds to the averaged value of hospitalization todeath reported by Russell et al. (2020) [19] (13 days)plus a period of 1 to 7 days between infection andhospitalization. Two different values of the time dur-ing which an infected person remains contagious (Tc)were tested to infer the reproduction number: 14, and21 days. The former is the duration of the quarantineapplied to any confirmed cases in most countries, andthe latter is a longer estimate used for comparisonsince the 14 days value is uncertain [19]. For com-puting estimates of the daily case number, we used afatality rate of 1.7 %, which is a median value betweenRussell et al. (2020)’s [19] estimates of the InfectionFatality Ratio and Case Fatality Ratio onboard theDiamond Princess passenger ship. The latter alsoclosely matches South Korea’s fatality rate (1.6%)[4], which is one of the most reliable national esti-mate so far, given the wide-range testing policy andthe advanced stage of the epidemic in this country.

3 Results

Time evolution of the reproduction numbers Riand R is shown in figure 1 for France, Italy, Spain,and United Kingdom. 4 different estimates areproposed in each figure: the estimates defined inEq (12), computed for values of contagion durationof Tc = 14 and 21 days, as well as values of theinstantaneous reproduction number multiplied bythe contagion duration (RiTc). For all four variables,time evolution exhibits a similar pattern: a steadydecreasing trend from 3.5 to 6 secondary infectionsper case in the beginning of the epidemic to lessthan unity 20 to 40 days before the reference date(April 24). In all four countries, no discontinuity inthe general decaying trend is observed around thefull lockdown’s start date. Even though this date

4

All rights reserved. No reuse allowed without permission. was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint (whichthis version posted May 1, 2020. .https://doi.org/10.1101/2020.04.24.20078717doi: medRxiv preprint

coincides approximately with the 1-crossing of Rin France, Italy, and Spain, the latter is only thefollow-up of a longer term decay. Note that, despitethe simplifying assumptions used here, our estimatesare of the same order of magnitude as Liu et al.(2020)[11]’s interval [1.4-6.49] for the outbreak inWuhan.

Analysis of the evolution of the growth rate γ(t)confirms this long-term trend in the trajectory ofthe epidemic before any full lockdown policies wereeffective (figure 2). A general decaying trend of γ(t)is evident from the beginning of the epidemic in all4 countries, although some variability exists aroundthe linear trend, with a nearly periodic oscillationof 5 to 8 days. Linear regression satisfyingly modelsthe time evolution of γ, with coefficients of determi-nation r2 (fraction of the variance explained by themodel) of 0.67, 0.72, 0.73, and 0.61 for France, Italy,Spain, and United Kingdom, respectively. Linearityof γ(t) suggests that time evolution of the epidemicis consistent with a Gauss function. Comparingthe linear decaying trend before, and after the timeby which full lockdown policies should have visibleimpacts, we find that the slope of γ(t) decreases inFrance, Italy, and Spain after the full lockdown, andremains constant in United Kingdom. The decay ofthe epidemic has thus slowed since the lockdown iseffective. Comparison of the general decay trendsin France, Italy, Spain and United Kingdom withthat of the Netherlands provides a further insighton the effects of full lockdown: Netherlands decaytrend is slightly slower than France and Spainbefore lockdown, and is nearly similar to Italy andUnited Kingdom’s. In all four countries, the decaytrend after the effective lockdown is slower thanNetherlands trend.

Since a raw, visual analysis of the effects of fulllockdown on the doubling time could mislead to theimpression that its increase is accelerated after thelockdown is effective, we should carefully inspect theresults of figure 3. We computed an estimate of thedoubling time, assuming the pre-lockdown trend inγ(t) remains constant after the lockdown is effective(we assume that γ(t) keeps on linearly decaying after

lockdown with the same slope as before lockdown).Comparing these estimates (dashed green line) withthe observed values (blue squares) shows that theinitial pre-lockdown trend yields a steeper rise in thedoubling time than what is observed after lockdownpolicies should have visible impacts. The visualimpression of an accelerating T2 growth in the datais thus not to be attributed to the lockdown effects,but rather to the inherent growth of the T2 functionwhen Γ(t) reaches small values. Figure 3 thus alsounderlines the lack of evidence of any effects of thefull lockdown.

Similarly to the doubling time, we estimatedthe evolution of the daily deaths number in thehypothesis of a continuation of the pre-lockdowntrend in γ(t) after the lockdown policies should havevisible impacts. Our results show that, even thoughthe dates of the daily deaths peaks in France, Italy,and Spain roughly correspond to the dates wherelockdown effects should be visible, the peak datesexpected from the pre-lockdown trends are actuallythe same. Moreover, daily deaths observations afterthis date show a slower decay than what would beexpected from the pre-lockdown tendencies. Forecastof the future evolution of the daily deaths numberusing the same method with the linear trend of thefull time series and the latest observations is alsoshown as the blue line (for indicative purpose only).

Time evolution of the total death toll, bothobserved, and predicted from pre-lockdown trendsis shown in figure 5. One would expect total deadnumbers to rapidly saturate at a value close fromthat corresponding to the crossing of the curveand the date of expected visible lockdown effects.However, the total dead number kept on growingafter this date, closely following the values expectedfrom pre-lockdown trends, and even reaching valuesbeyond the death toll expected from the latter.Again, the forecast dead number obtained fromextrapolating γ(t)’s linear trend in the future ispresented for indicative purpose. It is howeverinteresting to notice the consistency in the order ofmagnitude of the final total deaths forecast at a 15to 30 days interval (at the time of the lockdowns and

5

All rights reserved. No reuse allowed without permission. was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint (whichthis version posted May 1, 2020. .https://doi.org/10.1101/2020.04.24.20078717doi: medRxiv preprint

on April 24.).

While comparing pre and post lockdown timeevolutions of the epidemic brings a useful insighton the impacts of home containment, it is alsoof interest to compare this evolution with that ofcountries applying different policies. Figure 6 showsthe evolution of the daily deaths growth rates γ(t),their linear trends, the reproduction numbers, andthe doubling times for 10 countries. We selectedcountries that had over 1000 fatalities by April15, 2020, and chose to exclude data from China,given the growing doubts on their accuracy. Thetime reference was chosen to be the day by whichthe total death toll exceeded 100 in each country.Evolution of γ(t) shows a similar general decay trendin all countries. It is interesting to note that, whilethe linear trends of the growth rates have similarslopes in nearly all countries, they show a widerange of intercepts (value of γ at t = 0), showingthat although the slowdown of the epidemic followsa similar trajectory, each country started at verydifferent levels of growth rate. This general decaytrend is accompanied by a regular decay in thereproduction number in all countries, with similarslopes and, again, a wide range of initial reproduc-tion numbers. As expected from a decreasing growthrate and reproduction number, the doubling timeis increasing in all countries from the beginningof the time series. Figure 6 thus shows that timeevolution of the epidemic is homogeneous in WesternEurope, and that the main differences reside in theinitial conditions at the beginning of the epidemic.In particular, the figure shows that countries withsocial distancing policies, but no home containment,such as the Netherlands and Germany experience avery similar decay of the epidemic in terms of growthrate, reproduction number, and doubling time, tocountries with police-enforced home containment.On the other hand, results for Sweden suggest thattaking no action at all may yield a more variabledecay of the epidemic.

4 Summary and discussion

This observational study, using a generalized phe-nomenological method based on official daily deathsrecords only, shows that full lockdown policies ofFrance, Italy, Spain and United Kingdom haven’thad the expected effects in the evolution of theCOVID-19 epidemic. Our results show a generaldecay trend in the growth rates and reproductionnumbers two to three weeks before the full lockdownpolicies would be expected to have visible effects.Comparison of pre and post lockdown observationsreveals a counter-intuitive slowdown in the decayof the epidemic after lockdown. Estimates of dailyand total deaths numbers using pre-lockdown trendssuggest that no lives were saved by this strategy, incomparison with pre-lockdown, less restrictive, socialdistancing policies. Comparison of the epidemic’sevolution between the fully locked down countriesand neighboring countries applying social distancingmeasures only, confirms the absence of any effectsof home containment. Evolution of the epidemic inSweden however indicates that, in the absence ofany social distancing measures, the epidemic’s decaymay be subject to larger fluctuations. This workthus suggests that social distancing measures, suchas those applied in the Netherlands and Germany, orin Italy, France, Spain, and United Kingdom beforethe full lockdown strategies, have approximately thesame effects as police-enforced home containmentpolicies.

So far, the reasons for the relatively regulardecay of the epidemic remain largely unknown.While social distancing efforts may contribute to it,environmental conditions could as well have playeda role (possible seasonality of the virus). The groupimmunity hypothesis, though being unlikely if thereference fatality rates are correct, deserves a shortdiscussion: computing the number of daily new casesfrom the number of daily deaths following Eq (2),and using a fatality rate of 1.7%, we forecast a ratioof infected population at the end of the epidemic of1.4%, 3.0%, 3.2%, and 2.1% in France, Italy, Spain,and United Kingdom, respectively. The latter isobviously far from being able to yield any group

6

All rights reserved. No reuse allowed without permission. was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint (whichthis version posted May 1, 2020. .https://doi.org/10.1101/2020.04.24.20078717doi: medRxiv preprint

immunity. Under the rough assumption that 50to 70% of the population needs to be infected toensure group immunity, it is possible to computean hypothetical fatality rate using Eq (2). We findthat, if group immunity was responsible for thedecay of the epidemic, the fatality rates would beof about 0.05%, 0.10%, 0.11%, and 0.07% for the50% hypothesis in France, Italy, Spain, and UnitedKingdom, respectively, and of 0.03%, 0.07%, 0.08%,and 0.05% for the 70% hypothesis. Obviously,this is only a gedanken experiment, which is farbeyond the scope of this paper, and only serologicaltests and further data analysis, once the epidemicis completely instinct, will allow to discriminatebetween the possible reasons for its decay.

As a concluding remark, it should be pointed outthat, since the full lockdown strategies are shownto have no impact on the epidemic’s slowdown, oneshould consider their potentially high inherent deathtoll as a net loss of human lives.

References

[1] R. M. Anderson, C. Fraser, A. C. Ghani, C. A.Donnelly, S. Riley, N. M. Ferguson, G. M. Le-ung, T. H. Lam, and A. J Hedley. Epidemiol-ogy, transmission dynamics and control of sars:the 2002–2003 epidemic. Philosophical Transac-tions of the Royal Society of London. Series B:Biological Sciences, 359(1447):1091–1105, 2004.

[2] Y. Bai, L. Yao, T. Wei, F. Tian, D. Y. Jin,L. Chen, and M. Wang. Presumed asymptomaticcarrier transmission of covid-19. Jama, 2020.

[3] X. Chen, A. A. Chughtai, A. Dyda, and C. R.MacIntyre. Comparative epidemiology of mid-dle east respiratory syndrome coronavirus (mers-cov) in saudi arabia and south korea. Emergingmicrobes & infections, 6(1):1–6, 2017.

[4] J. B. Dowd, L. Andriano, D. M. Brazel, V. Ro-tondi, P. Block, X. Ding, Y. Liu, and M. C. Mills.Demographic science aids in understanding the

spread and fatality rates of covid-19. Proceedingsof the National Academy of Sciences, 2020.

[5] C. Dye and N. Gay. Modeling the sars epidemic.Science, 300(5627):1884–1885, 2003.

[6] B. F Farrell and P. J. Ioannou. General-ized stability theory. part i: Autonomous op-erators. Journal of the atmospheric sciences,53(14):2025–2040, 1996.

[7] Y. H. Hsieh, D. N. Fisman, and J. Wu. On epi-demic modeling in real time: An application tothe 2009 novel a (h1n1) influenza outbreak incanada. BMC research notes, 3(1):283, 2010.

[8] L. S. Hung. The sars epidemic in hong kong:what lessons have we learned? Journal of theRoyal Society of Medicine, 96(8):374–378, 2003.

[9] C. C. Ku, T. C. Ng, and H. H. Lin. Epidemiologi-cal benchmarks of the covid-19 outbreak controlin china after wuhan’s lockdown: a modellingstudy with an empirical approach. Available atSSRN 3544127, 2020.

[10] J. C. Lagier, P. Colson, H. T. Dupont, J. Sa-lomon, B. Doudier, C. Aubry, F. Gouriet,S. Baron, P. Dudouet, R. Flores, and others.Testing the repatriated for sars-cov2: Shouldlaboratory-based quarantine replace traditionalquarantine? Travel Medicine and Infectious Dis-ease, page 101624, 2020.

[11] Y Liu, A. A. Gayle, A. Wilder-Smith, andJ. Rocklov. The reproductive number of covid-19is higher compared to sars coronavirus. Journalof travel medicine, 2020.

[12] T. Meunier, C. Menesguen, X. Carton, S. LeGentil, and R. Schopp. Optimal perturbationsof an oceanic vortex lens. Fluids, 3(3):63, 2018.

[13] K. Mizumoto, K. Kagaya, A. Zarebski, andG. Chowell. Estimating the asymptomatic pro-portion of coronavirus disease 2019 (covid-19)cases on board the diamond princess cruiseship, yokohama, japan, 2020. Eurosurveillance,25(10):2000180, 2020.

7

All rights reserved. No reuse allowed without permission. was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint (whichthis version posted May 1, 2020. .https://doi.org/10.1101/2020.04.24.20078717doi: medRxiv preprint

[14] D. J. Muscatello, A. T. Newall, D. E. Dwyer,and C. R. MacIntyre. Mortality attributable toseasonal and pandemic influenza, australia, 2003to 2009, using a novel time series smoothing ap-proach. PloS one, 8(6), 2013.

[15] H. Nishiura, G. Chowell, M. Safan, andC. Castillo-Chavez. Pros and cons of estimatingthe reproduction number from early epidemicgrowth rate of influenza a (h1n1) 2009. The-oretical Biology and Medical Modelling, 7(1):1,2010.

[16] N. Picchiotti, M. Salvioli, E. Zanardini, andF. Missale. Covid-19 italian and europe epi-demic evolution: A seir model with lockdown-dependent transmission rate based on chinesedata. Available at SSRN 3562452, 2020.

[17] D. Raoult, A. Zumla, F. Locatelli, G. Ippolito,and G. Kroemer. Coronavirus infections: Epi-demiological, clinical and immunological fea-tures and hypotheses. Cell Stress, 4(4):66, 2020.

[18] F. J. Richards. A flexible growth function forempirical use. Journal of experimental Botany,10(2):290–301, 1959.

[19] T. W. Russell, J. Hellewell, C. I. Jarvis, K. Van-Zandvoort, S. Abbott, R. Ratnayake, S. Flasche,R. M. Eggo, A. J. Kucharski, and CMMID nCovworking group and others. Estimating the infec-tion and case fatality ratio for covid-19 usingage-adjusted data from the outbreak on the di-amond princess cruise ship. medRxiv, 2020.

[20] A. Tobıas. Evaluation of the lockdowns for thesars-cov-2 epidemic in italy and spain after onemonth follow up. Science of The Total Environ-ment, page 138539, 2020.

[21] X. Yin, J. Wang, J. Feng, Z. Chen, N. Jiang,J. Wu, S. Yan, H. Li, C. Lv, Z. Lu, and others.The impact of the corona virus disease 2019 out-break on chinese residents’ mental health. Avail-able at SSRN 3556680, 2020.

8

All rights reserved. No reuse allowed without permission. was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint (whichthis version posted May 1, 2020. .https://doi.org/10.1101/2020.04.24.20078717doi: medRxiv preprint

Figure 1: Time evolution of the reproduction number for France, Italy, Spain, and Great Britain. The blue squarerepresent the daily reproduction number multiplied by an estimate of the time during which a case is contagious(RiTc). The green dots represent the reproduction number (R) computed as a time integral of the daily reproductionnumber (Ri) and thus takes into account time variations of the latter. The dashed vertical orange line is the startdate of the lockdown policies. Two values of Tc were used for each variable: 14 and 21 days. Time is referenced toApril 24, 2020.

9

All rights reserved. No reuse allowed without permission. was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint (whichthis version posted May 1, 2020. .https://doi.org/10.1101/2020.04.24.20078717doi: medRxiv preprint

Figure 2: Time evolution of the growth rate of the daily deaths number (γ(t)) for France, Italy, Spain, and GreatBritain. The vertical orange dashed line shows the start date of the full lockdown policies. The orange shaded arearepresents the time at which the lockdown should show some effects in the epidemic spreading (14 to 20 days), andthe thick line is the reference date (17 days). The blue squares represent the observations, and the thick gray linerepresents the linear trend of the observations. The dashed green and red lines represent the linear trends before andafter the lockdown should affect the observations. Time is referenced to April 24, 2020.

10

All rights reserved. No reuse allowed without permission. was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint (whichthis version posted May 1, 2020. .https://doi.org/10.1101/2020.04.24.20078717doi: medRxiv preprint

Figure 3: Same as figure 2 for the doubling time. The thick dotted green line represents the expected evolution ifthe pre-lockdown linear trend in γ(t) is extrapolated beyond the day lockdown policies are expected to be effective(What would happen without any lockdown, assuming that the growth rate’s evolution remains the same). Time isreferenced to April 24, 2020.

11

All rights reserved. No reuse allowed without permission. was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint (whichthis version posted May 1, 2020. .https://doi.org/10.1101/2020.04.24.20078717doi: medRxiv preprint

Figure 4: Same as figure 3 for the daily deaths number. The green dotted lines represents the expected evolutionbased on the pre-lockdown trend of γ(t), while the plain blue line represents the expected future evolution based onthe full time series trend of γ(t). The light blue shading represents an error margin computed by doubling or dividingby two the slope of the linear fit to γ(t). Time is referenced to April 24, 2020.

12

All rights reserved. No reuse allowed without permission. was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint (whichthis version posted May 1, 2020. .https://doi.org/10.1101/2020.04.24.20078717doi: medRxiv preprint

Figure 5: Same as figure 4 for the total fatalities number.

13

All rights reserved. No reuse allowed without permission. was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint (whichthis version posted May 1, 2020. .https://doi.org/10.1101/2020.04.24.20078717doi: medRxiv preprint

Figure 6: Comparison of the time evolution of the epidemic for 10 countries. The time reference is chosen to bethe day each country reaches a total dead number of 100. The top left hand side panel shows the time evolutionof γ(t); the top right hand side panel shows the linear fits to γ(t); the bottom left hand side panel shows values ofthe reproduction number R(t); the bottom right-hand side panel shows the instantaneous doubling time. Time isreferenced to April 24, 2020.

14

All rights reserved. No reuse allowed without permission. was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint (whichthis version posted May 1, 2020. .https://doi.org/10.1101/2020.04.24.20078717doi: medRxiv preprint

All rights reserved. No reuse allowed without permission. was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint (whichthis version posted May 1, 2020. .https://doi.org/10.1101/2020.04.24.20078717doi: medRxiv preprint

All rights reserved. No reuse allowed without permission. was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint (whichthis version posted May 1, 2020. .https://doi.org/10.1101/2020.04.24.20078717doi: medRxiv preprint

All rights reserved. No reuse allowed without permission. was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint (whichthis version posted May 1, 2020. .https://doi.org/10.1101/2020.04.24.20078717doi: medRxiv preprint

All rights reserved. No reuse allowed without permission. was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint (whichthis version posted May 1, 2020. .https://doi.org/10.1101/2020.04.24.20078717doi: medRxiv preprint

All rights reserved. No reuse allowed without permission. was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint (whichthis version posted May 1, 2020. .https://doi.org/10.1101/2020.04.24.20078717doi: medRxiv preprint

All rights reserved. No reuse allowed without permission. was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint (whichthis version posted May 1, 2020. .https://doi.org/10.1101/2020.04.24.20078717doi: medRxiv preprint