[PDF] Download PDF - BMC Medicine PDF s12916-020-01698-4.pdf

Impact of lockdown on COVID-19 epidemic in Île-de-France and possible exit strategies Laura Di Domenico1, Giulia Pullano1,2, Chiara E Sabbatini1,

Covid-19 Oxfam France, 10 avril 2020 The disproportionately high mortality rate in the poorest became headline news During the spring lockdown

[PDF] Download PDF - BMC Medicine

Impact of lockdown on COVID-19 epidemic in Île-de-France and possible exit strategies Laura Di Domenico1, Giulia Pullano1,2, Chiara E Sabbatini1,

Download PDF - BMC Public Health

The French general population's attitudes toward lockdown against COVID-19: a fragile consensus Patrick Peretti-Watel1,2* , Pierre Verger1,2, Odile Launay3

[PDF] French Polynesia

28 août 2020 · May 13, 2020: Lifting of lockdown measures in the islands (except for Tahiti France due to the COVID-19 pandemic The loan has 2 www loopsamoa com/ global-news/tahiti-stops-sale-alcohol-amid-covid-19-crisis-90933

[PDF] Impact of lockdown on COVID-19 epidemic in Île-de - medRxiv

16 juil 2020 · As France experiences the first wave of COVID-19 pandemic in lockdown, situation in France, (ii) evaluate the impact of the lockdown implemented https ://www covidnet fr/fr/news/2020/4/10/2020s15_COVID_19_FR/

[PDF] France - European Union Agency for Fundamental Rights

4 mai 2020 · On 28 April, the Prime Minister confirmed that the lockdown would be lifted from 11 10 France, National Academy of Medicine, “Covid-19: exit from www schengenvisainfo com/news/temporary-internal-border-controls-

[PDF] Experience delivers results – La Poste supports home - UpuInt

during COVID-19 lockdown La Poste (France) has rapidly implemented two new services to support the continued education of children during lockdown In partnership in the recent UPU news article featuring La Poste, and in an article on

RESEARCH ARTICLE Open Access

Impact of lockdown on COVID-19 epidemic

in Île-de-France and possible exit strategies

Laura Di Domenico

1 , Giulia Pullano 1,2 , Chiara E. Sabbatini 1 , Pierre-Yves Boëlle 1 and Vittoria Colizza 1*

Abstract

Background:More than half of the global population is under strict forms of social distancing. Estimating the

expected impact of lockdown and exit strategies is critical to inform decision makers on the management of the

COVID-19 health crisis.

Methods:We use a stochastic age-structured transmission model integrating data on age profile and social

contacts in Île-de-France to (i) assess the epidemic in the region, (ii) evaluate the impact of lockdown, and (iii)

propose possible exit strategies and estimate their effectiveness. The model is calibrated to hospital admission data

before lockdown. Interventions are modeled by reconstructing the associated changes in the contact matrices and

informed by mobility reductions during lockdown evaluated from mobile phone data. Different types and durations

of social distancing are simulated, including progressive and targeted strategies, with large-scale testing.

Results:We estimate the reproductive number at 3.18 [3.09, 3.24] (95% confidence interval) prior to lockdown and

at 0.68 [0.66, 0.69] during lockdown, thanks to an 81% reduction of the average number of contacts. Model

predictions capture the disease dynamics during lockdown, showing the epidemic curve reaching ICU system

capacity, largely strengthened during the emergency, and slowly decreasing. Results suggest that physical contacts

outside households were largely avoided during lockdown. Lifting the lockdown with no exit strategy would lead

to a second wave overwhelming the healthcare system, if conditions return to normal. Extensive case finding and

isolation are required for social distancing strategies to gradually relax lockdown constraints.

Conclusions:As France experiences the first wave of COVID-19 pandemic in lockdown, intensive forms of social

distancing are required in the upcoming months due to the currently low population immunity. Extensive case

finding and isolation would allow the partial release of the socio-economic pressure caused by extreme measures,

while avoiding healthcare demand exceeding capacity. Response planning needs to urgently prioritize the logistics

and capacity for these interventions.

Keywords:COVID-19, Mathematical modeling, Lockdown, Exit strategies, Non-pharmaceutical interventions, Social

distancing, Reproductive number

Background

More than half of the global population is under strict forms of social distancing [1,2], with more than 90 countries in lockdown to fight against COVID-19 pan- demic. France implemented the lockdown from March

17 to May 11, 2020 [3]. The aim of this measure is to

drastically increase the so-called social distance between individuals to break the chains of transmission and re- duce COVID-19 spread. It is an unprecedented measure that was previously implemented only in Italy, Spain, and Austria [2], following the example of China [4], to curb the dramatic increase of hospitalizations and ad- missions to ICU approaching saturation of the health- care system.

which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give

appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if

changes were made. The images or other third party material in this article are included in the article's Creative Commons

licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons

licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain

permission directly from the copyright holder. To view a copy of this licence, visithttp://creativecommons.org/licenses/by/4.0/.

The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the

data made available in this article, unless otherwise stated in a credit line to the data. * Correspondence:vittoria.colizza@inserm.fr 1 INSERM, Sorbonne Université, Pierre Louis Institute of Epidemiology and

Public Health, Paris, France

Full list of author information is available at the end of the article Di Domenicoet al. BMC Medicine (2020) 18:240 The implementation of extreme measures of social dis- tancing, including mobility restrictions, banning of mass gatherings, closure of schools and work activities, isola- tion, and quarantine, helped control the first wave of COVID-19 pandemic in China [4-8]. Such exceptional coverage and intensive degree of intervention coupled with strict enforcement may be key to the resulting out- come. How this will play out in Europe is still uncertain [9,10]. Most importantly, how to relax such stringent constraints on social life and economy while controlling the health crisis remains under investigation [11-13]. Here we use an age-structured mathematical model to (i) assess the current COVID-19 pandemic situation in France, (ii) evaluate the impact of the lockdown imple- mented nationwide on March 17, 2020, and (iii) estimate the effectiveness of possible exit strategies. The model is applied to the region of Île-de-France (heavily affected by the epidemic); it is data-driven and calibrated on hos- pital admission data for the region prior to lockdown. Different types and durations of social distancing inter- ventions are explored, including a progressive lifting of the lockdown targeted on specific classes of individuals (e.g., allowing a larger portion of the population to go to work, while protecting the elderly) and large-scale test- ing for case finding and isolation. The aim is to identify possible strategies to reduce the public health impact following the lifting of the lockdown. The original version of this study was made available as a preprint in mid-April, 1 month before the exit from lockdown. This revised version updates the comparison and validation of model projections, once data became available, while maintaining the context of the beginning of lockdown.

Methods

We consider a stochastic discrete age-structured epi- demic model based on demographic and age profile data [14] of the region of Île-de-France (Fig.1).

Fig. 1aNumber of hospitalizations per 100,000 inhabitants per region in France as of April 2, 2020 [15].bNumber of ICU beds in Île-de-France

and increase of capacity over time [16].cAge profile in Île-de-France region corresponding to younger children, teenagers, adults, seniors (0, 11;

11, 19; 19, 65; and 65+ years old, respectively).dContact matrices in the baseline scenario (no intervention) obtained from data [17] (left) and

estimated for lockdown (right) Di Domenicoet al. BMC Medicine (2020) 18:240 Page 2 of 13

Mixing

Four age classes are considered: 0-10; 11-18; 19-64; and

65+ years old, called in the following younger children (yc),

adolescents or teenagers (t), adults (a), and seniors (s), re- spectively. We use social contact matrices measured in France in 2012 through a social contact survey [17]. The matrices represent the mixing in the baseline scenario (no interventions) between individuals in these four age groups (Fig.1), depending on the type of activity and place where the contacts occur (household, school, workplace, trans- port,leisure,other).Intervention measures are modeled through modifications of the contact matrices (see below).

Compartmental model and transmission

Transmission dynamics follows a compartmental scheme specific for COVID-19 (Fig.2), where individuals are di- vided into susceptible, exposed, infectious, hospitalized, in ICU, recovered, and deceased. The infectious phase is di- vided into two steps: a prodromic phase (I p ) occurring be- fore the end of the incubation period, followed by a phase where individuals may remain either asymptomatic (I a )or develop symptoms. In the latter case, we distinguish be- tween different degrees of severity of symptoms, ranging from paucisymptomatic (I ps ), to infectious individuals with mild (I ms ) or severe (I ss ) symptoms, according to data from Italian COVID-19 epidemic [18] and estimates from individual-case data from China and other countries [19]. We explore two values of the probability of being asymp- tomatic, namelyp a = 20% and 50%, in line with available estimates [20-22]. Individuals in the prodromic phase and asymptomatic and paucisymptomatic individuals have a smaller transmission rate with respect to individuals with moderate or severe symptoms, as reported by contact tra- cing investigations [23] and estimated in Ref. [8]. Current evidence from household studies, contact tracing investi- gations, and modeling works suggest that children are as likely to be infected by COVID-19 as adults, but more likely to become either asymptomatic or paucisympto- matic [22,24-26]. This may explain the very small per- centage (<5%) of children in COVID-19 confirmed cases worldwide [27]. Here we assume that children in both classes (younger children and adolescents) are equally sus- ceptible as adults, following Ref. [24], and that they be- come either asymptomatic or paucisymptomatic only. Different relative susceptibility or infectivity of children compared to adults is tested for sensitivity analysis. The compartmental model includes hospitalization and admission to ICU for severe cases. ICU admission rates, hospital case fatality, and lengths of stay after admission are informed from French hospital data for patient trajec- tories in Île-de-France region (SIVIC database maintained by the Agence du Numérique en Santé and Santé Publi- que France [28,29]) (see also Additional file1). ICU beds' occupation is the indicator used to evaluate the capacity of the region to face the surge of patients requiring inten- sive care. Since we do not use hospital beds'occupation for this evaluation, we neglect the time spent in the hos- pital after exiting intensive care. Parameters, values, and sources used to define the compartmental model are listed in Table S1 of the Add- itional file1[8,18,19,21,28,30-35].

Change of behavior due to severe illness

We assume that infectious individuals with severe symp- toms reduce of 75% their number of contacts because of the illness they experience, as observed during 2009 H1N1 pandemic [36]. Higher reductions are tested as possible interventions of self-isolation (see below).

Social distancing interventions

We implement social distancing interventions by recon- structing the associated changes of the contact matrices, accounting for a reduction of the number of contacts engaged in specific settings. More precisely: Fig. 2Compartmental model. S, susceptible; E, exposed; I p , infectious in the prodromic phase (the length of time including E and I p stages is the incubation period); I a , asymptomatic infectious; I ps , paucysymptomatic infectious; I ms , symptomatic infectious with mild symptoms; I ss

symptomatic infectious with severe symptoms; ICU, severe case admitted to ICU; H, severe case admitted to the hospital but not in intensive

care; R, recovered; D, deceased Di Domenicoet al. BMC Medicine (2020) 18:240 Page 3 of 13 ǼSchool closure: the contact matrix for school is removed. We consider that 5% of adults may stay at home to take care of children while schools are closed (not applied with telework or lockdown). ǼTelework performed by a given % of individuals: contacts at work and on transports are reduced to account for the % of workers not going to work anymore. In France, telework is performed daily byquotesdbs_dbs17.pdfusesText_23

[PDF] [PDF] Download PDF - BMC Medicine

[PDF] Health inequalities & Covid-19: the situation in France