Nombre De Cas Coronavirus Pays De La Loire

As nations in europe gradually serene lockdown incarcération after the sapin wave, test–trace–isolate strategies came to be critical to maintain auto incidence of covid disease 2019 (COVID-19) at meugler levels1,2. Reviewing your shortcomings can administer elements to consider in light de the seconde wave that is at this time underway in Europe. Here nous estimate auto rate ns detection ns symptomatic boîte of COVID-19 in la france after lockdown through auto use ns virological3 et participatory syndromic4 surveillance data coupled with mathematical transmission models calibrated to regional hospitalizations2. Our findings show that around 90,000 symptomatic infections, corresponding to 9 the end 10 cases, were not ascertained by the surveiller system in the life 7 weeks after lockdown native 11 May à 28 June 2020, although the audit positivity rate did not exceed thé 5% recommendation of the human being Health organization (WHO)5. The median detection rate raised from 7% (95% to trust interval, 6–8%) venir 38% (35–44%) end time, with super regional variations, owing à a strengthening de the system as well as a decrease in epidemic activity. According to participatory surveillance data, seulement un 31% of individuals through COVID-19-like symptoms consulted a doctor in the study period. This says that gros numbers de symptomatic caisse of COVID-19 did not seek medical advice in spite of recommendations, as confirmed par serological studies6,7. Encouraging awareness et same-day healthcare-seeking behaviour of suspected caisse of COVID-19 is critical to improve detection. However, auto capacity de the system remained insufficient also at the meugler epidemic activity completed after lockdown, and was predicted to deteriorate quickly with boosting incidence ns COVID-19 cases. Substantially more aggressive, targeted and efficient trial and error with easier access is required to loi as a tool venir control thé COVID-19 pandemic. The testing strategy will be critical venir enable partial lifting ns the current limite measures in Europe et to prevent a 3rd wave.

Vous lisez ce: Nombre de cas coronavirus pays de la loire

Surveillance and detection aim venir rapidly identify et isolate caisse to protect against onward undlion of SARS-CoV-2 in the community et to stop a an extensive resurgence of boîte of COVID-19. After année initial period—during which, because of a limite capacity, testing for SARS-CoV-2 infections greatly focused nous severely ill patients—a new testing policy was enforced in france to systematically screen parce que le potential infections v SARS-CoV-2 et enable lifting de the lockdown incarcération on 11 May 20208.

The particular characteristics du COVID-19, however, hinder the délimite of cases9,10,11. Gros proportions of asymptomatic transmittable individuals12, and the presence of mild jaune paucisymptomatic infections the easily go unobserved9,11, present serious difficulties to thé detection et control de SARS-CoV-29,10,13. Lacking a substantial portion of contagious individuals compromises the control effort, allowing the germes to silently spread10,11,12. Synthesizing evidence from virological3 et participatory syndromic surveillance4 v mathematical models2,14 the account pour behavioural data15,16,17,18, we assessed auto performance du the new testing plan in France and identified its henchmen limitations parce que le actionable improvements.

Management of the COVID-19 pandemic in france after lockdown in spring (May–June) 2020 involved thé generation ns a centralized database that collected tous data nous virological testing (SI-DEP3, the information system pour testing). All individuals with symptoms the were compatible with COVID-1919 were invited to consult their aperçu practitioner and obtain a prescription à la a virological test8. Lentilles de contact of confirmed cases were traced and tested. A le total of 20,777 virologically confirmed cases were notified from 13 May (week 20) à 28 June (week 26) in mainland France. These boîte included people with or without symptoms at thé time du testing who tested positive for SARS-CoV-2 jaune individuals that tested positive for SARS-CoV-2 for whom informations on clinical status at thé time de testing was missing (Extended les données Fig. 1). Accounting pour presymptomatic individuals amongst those presenting with non symptoms at auto time du testing et after imputation du missing data (Methods), année estimated 16,165 (95% confidence interval, 16,101–16,261) symptomatic cases were tested in auto study duration (Fig. 1a). Thé average delay from symptom onset to testing reduced from 12.5 days in week 20 to 2.8 days in week 26 (Fig. 1b et Extended data Fig. 1). Accounting parce que le this hold-up (Methods and Extended les données Fig. 2), nous estimated that 14,061 (13,972–14,156) virologically evidenced symptomatic caisse had année onset ns symptoms in thé study period, showing a decreasing trend over temps (2,493 in week 20, 1,647 in week 26). The juge positivity rate decreased in the sapin weeks and stabilized at about 1.2% (mean end weeks 24–26).

Fig. 1: Virological surveillance, participatory syndromic surveillance and behavioural data for model parameterization.


a, Estimated num of virologically confirmed symptomatic caisse in mainland France passant par week ns testing and week de onset (bar graphs), and test positivity rate (line graphs). Estimates are based conditions météorologiques the imputation ns individuals without symptoms that tested positif at auto time de testing into asymptomatic or presymptomatic; imputation of missing data on clinical condition at the time de testing right into asymptomatic, presymptomatic jaune symptomatic; et imputation du the daté of onset du symptoms pour presymptomatic et symptomatic caisse (Methods). Imputations to be performed n = 100 times. Uncertainties (black bars) correspondre to auto 95% confidence intervals. Juge positivity rates were computed for caisse with complete information. Data for weeks 20–26 were consolidated in week 30. b, Breakdown de virologically confirmed boîte with symptoms and complete information in auto SI-DEP database de week of testing follow to the declared onset of symptoms (left y axis; n = 5,514). The estimated temps from onset to testing is also shown (right y axis; median and 95% to trust interval acquired from n = 100 imputations du the onset date). c, Weekly incidence of suspected caisse of COVID-19 (median (dashed line), 95% confidence interval (shaded area) et 3-week moving average (solid line)), and percentage du individuals seeking healthcare (median et 95% confidence interval), approximated from the participatory surveillance system, (average weekly n = 7,481). d, The num of suspected cases of COVID-19 in the participatory cohort that sought healthcare, and among those individuals, the alors of individuals who received a prescription and performed a virological audit when given thé prescription. e, Estimated échanger in presence at workplace places over time and by an ar based conditions météorologiques Google assurance history data17. An ar acronyms are detailed in Table 1. f, Percentage of individuals preventing physical contact with respect to lockdown, approximated from a large survey conducted by Santé jc France18.

Source data

A digital participatory système was in addition considered pour COVID-19 syndromic surveillance in the general population20, consisting of those who did no consult a doctor. Dubbed, ce was adjusted from thé platform (which is specialized to thé surveillance de influenza-like illnesses4) à respond to thé COVID-19 health situation in beforehand 2020. It is based conditions météorologiques a set de volunteers who weekly self-declare your symptoms, along with sociodemographic information. Conditions météorologiques the basis du symptoms declared by année average ns 7,500 participants every week, auto estimated incidence de suspected cases of COVID-1919 diminished from around 1% to 0.8% over temps (Fig. 1c). Ns 524 doubt cases, 162 (31%) consulted a doctor in the study period. Amongst them, 89 (55%) received a prescription à la a test, resulting in auto screening du 50 individuals (56% de those given the prescription) (Fig. 1d).

We provided stochastic discrete age-stratified epidemic models2,14 based nous demography, lâge profile21 and social contact data15 ns the 12 regions ns mainland france to account pour age-specific contact activity et role in COVID-19 transmission. Disease évolution is specific à COVID-192,14 and parameterized using auto current knowledge à include presymptomatic transmission22, and asymptomatic12 and symptomatic epidemic with various degrees of severity9,11,23,24. The modèle was shown to prendre the undlion dynamics de the pandemic in Île-de-France in the tons wave and was used venir assess thé effect ns lockdown and exit strategies2,14. Full details are reported in the Methods.

Intervention measures were modelled as mechanistic modifications of the contact matrices, accounting pour a reduction in the num of contact engaged in specific settings, and were informed from empirical data. Lockdown les données were acquired from previously published studies2,14. Auto exit organiser was modelled considering region-specific data of lécole attendance based nous the les données from the Ministry du Education16, partial visibility at workplaces based on estimates from location history les données of mobile phones17 (Fig. 1e), a reduction in thé adoption du physical distancing end time and the increased risque aversion de older individuals based nous survey data18 (Fig. 1f), et the partial reopening du activities. A sensitivity analysis was performed nous the reopening of activities, oui data were lacking for année accurate parameterization of associated contacts. Testing and isolation de detected boîte were implemented de considering a 90% palliation in contacts for thé virologically confirmed caisse of COVID-192,14. Region-specific models to be fitted to regional hospital entrée data (Fig. 2) using a maximum likelihood approach. Further details are reported in thé Methods et Supplementary Information.


ac, Hospital admissions over time; data (points) et simulations (median et 95% to trust intervals) for Île-de-France (a), Pays du la leader (b) et Normandy (c). Hospital admission data up to week 27 (consolidated in week 28) to be used à infer parameter values. df, Projected num of new symptomatic caisse over temps (median et 95% trust interval) et estimated num of virologically shown symptomatic cases par week ns onset (points), à la the same regions (Île-de-France (d), Pays de la leader (e) et Normandy (f)) (left y axis). The estimated detection probability ns symptomatic des boites (%) is additionally shown (red points, median et 95% trust interval; right y axis). In all panels, 95% to trust intervals were obtained from n = 500 live independence stochastic runs. Plots à la the remaining areas are shown in Extended les données Fig. 3.

Source data

The projected number of boîte decreased over temps in toutes les personnes regions, in commitment with thé decreasing tendency reported in hospital admissions during auto study period (Fig. 2 et Extended data Fig. 3). Overall, 103,907 (95% trust interval, 90,216–116,377) new symptomatic infections were predicted in mainland la france in weeks 20–26 (from 35,704 (30,290–40,748) in week 20 venir 4,319 (3,773–4,760) in week 26). Île-de-France was the region with the largest predicted num of symptomatic boîte (from 12,427 (8,104–14,136) à 1,704 (1,258–2,004) native week 20 venir week 26), followed par Grand Est and Hauts-de-France (Table 1 and Extended les données Table 1).

Projections were substantially higher than the alors of virologically confirmed boîte (Figs. 2, 3). Thé estimated detection rate for symptomatic epidemic in mainland france in thé period ns weeks 20–26 to be 14% (12–16%), saying that around 9 out ns 10 new des boites with symptom were not identified de the surveillance system. A reduced detection price was found for asymptomatic infections (Extended data Fig. 5). Thé estimated detection rate boosted over temps (7% (6–8%) in week 20, 38% (35–44%) in week 26) (Table 1). By the end of June, 5 regions had a mean detection rate above 50%, and six regions had a detection price within thé confidence term of maquette projections (Fig. 3b–d). Tous regions other than Brittany shown average increasing trends in the estimated detection rate in june compared through May. Nous did not find any significant les associations between auto detection rate and the number of detected cases, or the audit positivity price (Extended data Fig. 4). However, the detection rate was negatively linked with model-predicted incidence (Spearman correlation, r = −0.75, P −15) (Fig. 3f). In addition, the data followed a power-law function, π = 66 × i−0.51, wherein π is the weekly detection rate of symptomatic cases (expressed as a percentage) et i thé projected weekly incidence (number of des boites per 100,000). This duty quantifies auto relationship between auto detection capacity of the test–trace–isolate system and the circulation de the germe in the population. Cette clearly shows that the detection capacity promptly decreases ont the incidence du COVID-19 increases.


a, Projected num of new symptomatic des boites over time (median and 95% trust interval) et estimated alors of virologically evidenced symptomatic cases by week de onset (points) in mainland la france (left y axis). Auto estimated detection rate of symptomatic des boites (%) is likewise shown (red points, median et 95% to trust interval; right y axis). b, estimated detection rate ns symptomatic des boites (%) et 95% confidence intervals end time à la mainland france (red dots and bars), et for toutes les personnes regions (grey lines, seul median values space shown parce que le visualization). c, Map ns the approximated detection rate (%) by region in week 26 (22–28 June 2020). d, approximated detection rate revenir region compared venir the national estimate. Areas are ranked by increasing mean detection rate. Caisse plots represent the median (line in thé middle de the box), interquartile range (box limits) and 2.5th and 97.5th percentiles (whiskers). e, guess percentage du the population infected (median et 95% to trust interval) compared with approximates from thé serological examine EpiCov26 performed conditions météorologiques a representative sample de the populations in mainland France. f, estimated detection rate of symptomatic des boites (%) de region et by week compared with thé projected incidence passant par region and by week. The curve spectacles the result of a least-square droit to the data with a power-law function, π = a × i−b, whereby π is thé detection price (expressed oui a percentage), je is thé weekly incidence (cases revenir 100,000), a = 66 (95% to trust interval, 52–85) et b = 0.51 (0.41–0.60). g, approximated incidence de symptomatic cases and 95% to trust intervals in mainland france in week 26 from different sources: virological surveillance data (SI-DEP), participatory surveillance les données (, with two estimates) et model projections. h, Projected incidence revenir region compared to the national estimate. Areas are ranked oui in d. Caisse plots are as defined in d. In all panels, medians et 95% trust intervals for model projection were acquired from n = 500 elevation stochastic runs.

Source data

Validation ns the maquette was performed in two ways. First, we compared our modèle projections du the percentage du the populations infected with thé results de three independent seroprevalence apprendre performed after the first wave in France7,25,26 (Methods). Modelling outcomes are in agreement with serological approximates at auto national and regional level (Fig. 3e and Extended les données Fig. 6). Second, nous compared thé projected incidence of symptomatic des boites of COVID-19 in week 26 (6.69 (5.84–7.37) boîte per 100,000) with the value derived from the num of virologically confirmed caisse (2.55 (2.48–2.61) cases per 100,000) et two estimates based nous data (Fig. 3g). The first estimate applies auto measured juge positivity rate to auto incidence de self-reported suspected boîte of COVID-19 (estimate 1, which surrendered 8.6 (95% trust interval, 6.2–11.5) cases per 100,000); the seconde additionally assumes that only 55% would be confirmed ont a doubt case by a physician et prescribed a audit (according à data; calculation 2, which surrendered 4.7 (3.4–6.3) boîte per 100,000). Our deviner are in line through plausible estimates from, et suggest that, nous average, at the very least 80% ns suspected des boites should it is in tested to reach thé predicted incidence.

Sensitivity analysis showed that thé findings to be robust to elements de the communication matrices that could not be informed passant par empirical les données (Supplementary Figs. 8, 9). Furthermore, a model selection evaluation showed that alters in communication patterns over time en raison de to restrictions et the activities du individuals de different lâge classes after lockdown (for example, partial attendance at school et remote working) are needed venir accurately capture the dévolution dynamics (Supplementary Table 2 et Supplementary Fig. 5).

Despite a juge positivity price in mainland france well below thé recommendations (5%) du the WHO5, a substantial proportion de symptomatic cases (9 out ns 10) continued to be undetected in the tons 7 weeks after lockdown.

Low detection rates in mid-May were in line with estimates à la the same period from a seroprevalence research in Switzerland27. Surveiller improved significantly over time, leading à half du the français regions reporting numbers of cases that were compatibles with maquette projections. Auto framework increasingly strengthened with enhancing resources end time, as shown by a more-rapid detection of des boites (78% palliation in thé average hold-up from symptom onset venir testing from pouvez to June). At thé same time, the système benefited indigenous a considerable and adversaires decrease in epidemic activity in tous regions.

Despite this positif trend, our findings to mark structural limitations and a an essential need for improvement. Some locations remained through limited diagnostique exhaustiveness. This is specifically concerning in those areas that were predicted to ont large numbers ns weekly epidemic (Île-de-France, in i m sorry only une out of three symptomatic des boites was detected passant par the end de June, and Grand Est, in which une out du five was detected). Almost all patients (92%) that were clinically diagnosed passant par sentinel général practitioners as suspected cases of COVID-19 were prescribed a test20. However, only 31% ns individuals through COVID-19-like symptom consulted a medical professional according à participatory regardez data. Overall, these compte suggest that a super number de symptomatic des boites of COVID-19 were not screened due to the fact that they did not seek clinical advice despite thé recommendations. This was confirmed de serological studies. In France, seul 48% du symptomatic participants v antibodies versus SARS-CoV-2 report consulting a aperçu practitioner7; in Spain, in between 16% et 20% ns individuals v antibodies versus SARS-CoV-2 reported a ahead virological screening6. Par combining approximates from virological et participatory surveillance data, conditions météorologiques extrapolated année incidence price from crowdsourced data that is compatibles with modèle projections, under thé hypothesis the the taille majority of suspected des boites would volonté tested (>80%). This finding further supports testing of toutes les personnes suspected cases of COVID-19. Large-scale la communication campaigns have to reinforce recommendations à raise awareness in the population and strongly encourage healthcare-seeking behaviour specifically in patient with mild symptoms. At the same time, investigations to recognize reasons pour not consulting a doctor could be quickly performed through thé participatory regardez system.

Red robinet might ont contributed to low testing rates. Prescription de a test was reputed compulsory in the new testing policy à prevent misuse of diagnostique resources8; however, this associated consultation, prescription and a activities appointment, which may ont discouraged mildly impacted individuals who do not require clinical assistance. To facilitate access, testing should not need a prescription, as later established par authorities28. Part local capacité à juger emerged over lété that boosted the alors of drive-through experimentation facilities, promoted massive screening in certain areas and offered en mouvement testing facilities à increase proximity to auto population29. The use of antigen test will further facilitate access. These capacité à juger are particularly relevant to against socioeconomic inequalities in access venir care in population that space vulnerable à COVID-1930,31. However, such strategies should not hinder a testing protocol that targets suspected index cases. Ours results seul that high testing efforts, measured by low test positivity rates, are not linked with high rates de detection. This was additionally observed in auto UK during the first wave, as soon as detection remained meugler despite large numbers ns tests and a low positivity rate32. Without solid case-based surveillance, the hasardeux is to disperse resources towards random people without symptom who space unlikely à be positive. This might saturate the test–trace–isolate system, oui observed during summer33, there is no slowing down thé circulation de SARS-CoV-2 the is required to safeguard thé hospital system.

Given presymptomatic transmission, annonce of contacts should be almost immediate à enable the effective interruption of dévolution chains22. à la testing à be an actionable tool venir control the dévolution of SARS-CoV-2, delays need to be suppressed and screening rates significantly increased but better targeted. Over May–June, thé average weekly num of essai was 250,000—remaining fine below the objective the was originally set passant par authorities (700,000 tests). The number of test increased end summer, cible proportionally to auto increased circulation du the virus. The capacity ns detection ns the test–trace–isolate système scaled oui the inverse of the carré root of the incidence, currently deteriorating quickly at meugler incidence levels. More aggressive testing that targets suspected index caisse should it is in performed at low viral circulation to avoid caisse resurgence. The system was predicted to be able à detect an ext than deux out de three des boites (rate >66%) only si the incidence was reduced than one symptomatic boîte per 100,000, a illustration that is 50 times smaller than approximated at the exit from lockdown. Ont detection du at the very least 50% of boîte is needed à control thé pandemic when avoiding intenté social distancing2, these results indicate that the system was insufficient venir perform comprehensive case-based surveillance, oui has to be recommended as soon as aiming to organiser out restrictions5. Current confinements applied in europe to curb the seconde wave sell a lundi opportunity to improve experimentation policies and support thé lifting of these procedures in auto upcoming weeks. Failing to à faire so may lead venir a rapid et uncontrolled increase in the num of caisse of COVID-192,34. Such danger is even stronger in auto winter season et with auto existing fatigué with regarder fixement to adhesion to auto restrictions18.

Models to be region-based et did not think about a faisabilité coupling between regional epidemics caused passant par mobility. This selection was supported par stringent movement confinements during lockdown30, et by the limited mobility boost in May–June, avant important inter-regional displacements took place at auto start of the summer holidays in July. Foreign importations ns the virus35 were neglected as France reopened its boundaries with eu member states nous 15 June, et the Schengen area stayed closed until July. Auto cohort is no representative du the general population; however, a vault study conditions météorologiques influenza-like illnesses has shown that auto adjusted incidence was in great agreement with sentinel estimates4. Underdetection peut faire also continuez because du the imperfect characteristics de the reverse-transcription calculatrice tests used à identify infections du SARS-CoV-236. Some des boites tested à la SARS-CoV-2 could oui had false-negative results, for example, due to the fact that they were tested too at an early stage after the infection, thus more increasing thé rate de underdetection. Previous work assessed thé rate du underdetection in 210 countries32, cible this study mostly focused on the early global dynamics. Our model gives up geographical extent pour higher les données quality in a particular country, providing a synthesis of les données sources the characterizes human being behaviour end time and space ensemble with virological and participatory surveillance les données to identify the weak web links in the pandemic response.

Our findings identify vital needs for the improvement de the test–trace–isolate réponse system to control auto COVID-19 pandemic. Substantially an ext aggressive and efficient trial and error that targets suspected caisse of COVID-19 needs to be accomplished to act as a way à control auto COVID-19 pandemic. Connected communication and logistical needs have to not be underestimated. These elements should be considered to enable thé lifting ns restrictive actions that are at this time used to curb the seconde wave de COVID-19 in Europe.

No statistical techniques were used à predetermine sample size. The experiments were not randomized and the investigators were no blinded to supplément during experiments and outcome assessment.

Virological surveiller data

The central database SI-DEP à la virological surveillance3 collects tous tests perform in France for any reason. In the period under study, des lignes directrices recommended individuals venir consult a general practitioner at the first sign de COVID-19-like symptoms and to obtain a prescription for a virological audit (a prescription was compulsory venir access thé test)8. In addition, routine testing was performed for patients admitted to auto hospital with any kind of diagnosis, medical care personnel et individuals at divers facilities (for example, in some treatment homes à la older people or long-term healthcare facilities). Data include comprehensive information à la the people tested in France, consisting of (1) the daté of thé test; (2) the result ns the test (positive jaune negative); (3) assurance (region); (4) the absence or presence du symptoms at auto time du testing; (5) self-declared delay between onset and test in presence of symptoms. Auto delay is provided with thé following breakdown: onset date occurring 0–1 day before date du test, 2–4 days before, 5–7 days before, 8–15 days before, or more than 15 days before. à la some tests, informations on point (4) et (5) is missing. Auto SI-DEP database noted complete information à la 23,210 (66%) out de 35,264 laboratory-confirmed boîte of COVID-19 tested in between week 20 (11–17 May) and week 30 (19–26 July), with an increasing trend of complete informations over time (from 49% in week 20 to 76% in week 30) (Extended data Fig. 1). Among confirmed cases with complete information, 12,716 (55%) showed non symptoms at the time of testing (Extended les données Fig. 1). The study referred to thé period from week 20 venir week 26. Les données up venir week 30 to be used to consolidate the les données in the study period accounting à la the delays.

Imputation of asymptomatic matches presymptomatic cases, start date et missing information

Individuals that tested positive nous a given cétait une date were taped in auto SI-DEP database as: cases with symptom at the time du testing, through a self-declared delay from onset de symptoms; caisse without symptom at thé time du testing; or caisse with no information on presence or absent of symptom at the time de testing. These three subsets of cases were analysed to account à la the presence du presymptomatic individuals amongst those with non symptoms at thé time du testing, imputation ns missing data and the estimation of dates of infection or symptom onset.

For laboratory-confirmed cases of COVID-19 who had actually symptoms at the time ns testing, nous estimated their date of onset utilizing the informations on the date of test et the temps interval du onset-to-test delay, which to be self-declared passant par the patient (Fig. 1b). In auto time duration between mainly 20 et 30, 20% of boîte had année onset-to-test delay ns ≤1 day, 63% had actually a delay de ≤4 days, 83% had a delay of ≤7 days et 88% had actually a delay ns ≤15 days (Extended data Fig. 1). Nous fitted a Gamma distribuer to the onset-to-test delay data with a maximum likelihood approach, utilizing three different periods of time (May, June et July), venir account for changes in the livré of self-declared delays over time (that is, plus long delays at thé beginning du the study period, much shorter delays at that end) (Extended data Fig. 2). Thé estimated average delay in May, June and July was 12.9 (95% to trust interval, 7.0–16.1), 5.1 (3.7–6.3) and 2.7 (2.0–3.1) days, respectively. July data were used to consolidate les données corresponding venir infections v onset in June et tested v delay. Given a confirmed des boites with symptom testing nous a details date, we assigned the onset date de sampling thé onset-to-testing hold-up from the fitted livré for the period, conditional to the fact that auto delay lies in auto corresponding temps interval declared de the patient. We assumed the onset did no occur avant the implementation de the denchères lockdown, conditions météorologiques 17 March 2020 (week 12); nous therefore truncated auto Gamma livré accordingly, when assigning the date of beginning for caisse with onset-to-test hold-up >15 days. Thé imputation procedure was brought out 100 times. Outcomes were aggregated par week of onset.

For laboratory-confirmed cases of COVID-19 with ne sont pas symptoms at auto time of testing, nous assumed that conditions météorologiques average 40% du them to be asymptomatic12 (see the ‘Transmission maquette summary’ section), whereas auto remaining 60% to be presymptomatic that tested beforehand thanks to contact tracing. Imputation to be done par sampling from a binomial distribution et repeated 100 times. Les données on contact tracing could not be used venir inform les données on infecter or symptom onset, because of intérieur regulatory framework on privacy preventing the matching of the two databases (virological tests and contact tracing). Given the court sensitivity ns PCR tests in thé early phase ns the incubation period, we considered the imputed presymptomatic boîte belonged to auto prodromic phase. Onset date for presymptomatic cases was estimated de sampling from an exponential livré with a mean du 1.5 days, matching to thé duration du the prodromic étape in our modèle (Supplementary Table 1). Parce que le imputed asymptomatic, conditions météorologiques assumed auto same hold-up from infection to testing ont in boîte with symptoms. Given the charpente of ours compartmental model and to match the definition de the time used parce que le symptomatic individuals (week du onset), conditions météorologiques considered a delay in auto detection of asymptomatic individuals starting from the end du the prodromic étape (corresponding to auto symptom start time à la symptomatic infections) à the date of testing. We assigned this date by sampling the delay from thé monthly gamma distribution. Imputation ns the dates was repetitive 100 times.

For laboratory-confirmed caisse of COVID-19 with no information on symptom at the time de testing, missing data were imputed de sampling native a multinomial distribution with probabilities same to the rate of occurrence ns the outcomes (asymptomatic, presymptomatic jaune symptomatic through five faisabilité time intervals for the onset-to-test delay) reported for caisse with complete information et assuming thé imputation of caisse without symptoms into asymptomatic et presymptomatic, ont described above. Imputation was performed passant par region et by week et repeated 100 times. Presymptomatic and symptomatic individuals were aggregated together par onset cétait une date (Fig. 1a) à estimate the rate du detection ns symptomatic cases.

Participatory surveillance data et analysis is a participatory virtual system pour the surveillance du COVID-19, easily accessible at Cette was adjusted from GrippeNet.fr4 to respond to thé COVID-19 health crisis in march 2020. is a participatory system for the surveillance ns influenza-like illnesses available in france since 2011 through a la coopération between Inserm, sorbonne Université et Santé publique France, supplementing sentinel surveillance4,37. The system is based nous a committed website à conduct syndromic surveillance through self-reported symptoms volunteered par participants residents in France. Data are collected conditions météorologiques a weekly basis; participants also carry out detailed profile information at enrolment38. In addition to tracking auto incidence du influenza-like illnesses4,37, was used venir estimate vaccine coverage in details subgroups39, individual la perception towards vaccination40 and healthcare-seeking behaviour41. It was likewise used venir assess behaviours and perceptions related to diseases other than influenza42, including COVID-1943.

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Participants are nous average older and include a bigger proportion ns women compared to the general population38,44. The participating populations is, however, representative in terms of health indicators such ont diabetes et asthma conditions. In spite of these discrepancies, trends du the approximated incidence de influenza-like illnesses native reports contrasted well v those du the denchères sentinel system4,37. All analyses were changed by age and sex ns participants.

To screen suspected des boites of COVID-19 in the aperçu population, nous used auto expanded des boites definition recommended par the High board of directors of auditeur Health for systematic testing et described in their 20 April 2020 notice19, which included either du the deux following definitions: (1) (sudden onset de symptoms or sudden onset of fever) and (fever jaune chills) et (cough jaune shortness du breath jaune (chest pain and age > 5 years old)) or (2) (sudden onset de symptoms) or (sudden onset du fever and fever); et one ns the three adhering to conditions: (i) (age > 5 years old) and ((feeling tired or exhausted) jaune (muscle/joint pain) or (headache) or (loss de smell there is no runny or blocked nose) or (loss ns taste)); or (ii) ((age ≥ 80 years old) or (age 3. These estimates were computed oui follows. Estimate 1 = ( approximated incidence ns suspected caisse in week 26) × (test positivity rate from SI-DEP in week 26); calculation 2 = ( approximated incidence du suspected des boites in week 26) × (estimated relationship screened and confirmed oui a suspected case of COVID-19 de a physician, and prescribed a test; approximates from × (test positivity rate from SI-DEP in week 26). The two estimates were used to validate model projections and identify thé specific surveiller mechanisms that necessary improvement.

Ethics statement to be reviewed and approved passant par the french Advisory Committee parce que le research on information treatment in thé health ar (that is, CCTIRS, authorization 11.565), et by the french National commission on Informatics et Liberty (that is, CNIL, authorization DR-2012–024)—the authorities ruling on tous matters related to ethics, data and privacy in auto country. Educated consent to be provided de each entrant at enrolment, according to regulations.

Transmission models summary

We supplied a stochastic discrete age-stratified transmission model for each an ar based nous demographic, contact15 and age profile data of french regions21. Models to be region-specific venir account for the geographically heterogeneous epidemic case in auto country and given thé mobility confinements limiting inter-regional déménageur fluxes. The study focused nous mainland la france where thé epidemic case was similaire across regions, et excluded Corsica, which reported very limited epidemic activity et overseas areas characterized passant par increasing transmission20.

Four age classes to be considered: <0–11), <11–19), <19–65) and 65+ years old, advert to as children, adolescents, adults et older individuals. Transmission dynamics follows a compartmental plan specific to COVID-19, in which individuals were split into susceptible, exposed, infectious and hospitalized (Supplementary Information and Supplementary Figs. 1, 2). We did not consider further le progrès from hospitalization (for example, dadmission to intensive care units, recovery or death2) as it was not needed parce que le the objective de the study. The infectious étape is separated into two steps: a prodromic étape (Ip) et a organiser during i beg your pardon individuals may remain either asymptomatic (Ia, v probability12 pa = 40%) or develop symptoms. In auto latter case, we distinguished between different degrees de severity du symptoms9,11,23,24, varying from paucisymptomatic (Ips), venir infectious individuals with soft (Ims) or severe (Iss) symptoms. Prodromic, asymptomatic and paucisymptomatic individuals have a reduced transmissibility rβ = 0.55, as estimated previously11, and in agreement with evidence from thé field45,46,47. A decreased susceptibility to be considered à la children et adolescents, in addition to a reduced relation amoureuse transmissibility du children, following accessible evidence from family members studies, contact-tracing analyses, serological investigations et modelling works48,49,50,51,52,53. A sensitivity analysis was performed nous the relative susceptibility and transmissibility of children, and on thé proportion du asymptomatic infections (Supplementary Figs. 10–13). Complete details space reported in the Supplementary Information.

The research was no extended venir the lété months, because of (1) thé challenge de mechanistically parameterizing the la communication matrices during summer; (2) thé increase of déménageur fluxes across regions weakening our assumption ns region-specific models; et (3) the interruption of surveillance during the lété break, i m sorry prevented the fausser of the key determinants behind caisse underascertainment.

Contact matrices

Age-stratified dévolution uses a social communication matrix the reports thé average communication rates between different age classes in France15. This ad to thé baseline condition, that is, antérieur à lockdown. The contact matrix includes the following layers: contact at home, school, workplace, transport, recreation activities et other activities, et discriminates in between physical and non-physical contacts. à account pour the change of communication patterns end time, communication matrices room mechanistically parameterized, passant par region and over time, through different data sources informing nous the percentage de students going to school16, thé percentage ns workers walking to auto workplace17, thé compliance à preventive measures18, through a higher compliance registered in larger individuals18. Informations on the progressé reopening de activities suggests that leisure et other tasks were only partially open in auto study period. Data, however, room not fine-grained enough à parameterize ours model, haricot de soja we i think a 50% opening of these activities et explore variations in auto sensitivity analysis.

School attendance

School reopening was parameterized de considering auto percentage de reported attendance at écoles (pre-school et primary school; middle and high school) provided par the Ministry de Education16 (Supplementary Fig. 3). The alors of contacts in the school matrix was modified venir account à la the attendance ns students in each lécole level provided par data. The is, attendance de 14.5%, referring, pour example, to the attendance registered in Île-de-France in pre-schools et primary schools, corresponds venir a reduction de 85.5% in the num of contact established at school by students belonging à that school level. Contacts for different modes of transport were modified accordingly.

Presence at work

To account à la the percentage ns individuals at work, provided recommendations on remote working et activities the were not yet reopened, nous used auto estimated variation ns presence at workplaces based on téléphone portable phone location data provided by Google Mobility Trends17. Contact at work et for different mode of transport were therefore modified according to this percentage, oui described for contact at school. Household contact were increased proportionally à each adult remaining at page daccueil based nous statistics to compare weekend matches weekday contacts15 and the proportion ns adults functioning during the weekend54, oui done previously2.

Adoption de physical distancing

Our previous work showed that physical lentilles de contact during lockdown were fully avoided2, in covenant with les données collected afterwards18. To account pour individual adoption de preventive action after lockdown, conditions météorologiques used the percentage de population preventing physical contacts estimated indigenous a large survey conducted by Santé publique france (CoviPrev18). Les données were fitted v a direct regression (Fig. 1) to provide thé weekly percentage of individuals staying clear of physical contacts. Conditions météorologiques therefore amendment our communication matrices over time, removing thé percentage of physical contact corresponding to auto survey estimates à la that week.

Increased risque aversion de older individuals

Data native the sois béni publique la france survey CoviPrev18 also montrer that enlarge individuals safeguarded themselves further relative to other lâge classes. On average, they respected physics distancing 28% much more than auto other âge classes (Supplementary Fig. 4). For this reason, nous considered a further reduction ns 30% in lentilles de contact for older individuals in thé exit phase, informed de survey data.

Inference framework

The parameters du the dévolution models à be estimated are specific à each pandemic phase.

Before lockdown, β, t0, where β is the undlion rate tout de suite contact and t0 is the date of auto start du the simulation, seeded through 10 infectious individuals.

During lockdown, αLD, tLD, where αLD is auto scaling factor ns the dévolution rate effronté contact et tLD is the daté when lockdown effects nous hospitalization data became visible.

After lockdown, αexit, πa(w), πs(w), where αexit is the scaling factor de the dévolution rate revenir contact, and πa(w) et πs(w) are auto proportion du asymptomatic et symptomatic caisse tested in la semaine w de the leave phase, respectively. Detected cases in the simulations had their contact reduced by 90% venir mimic isolation, oui done in previous studies2,14.

We provided simulations of the stochastic maquette to predict values for tous quantities de interest (500 simulations every time). We fitted the model to the du quotidien count ns hospitalizations Hobs(d) nous day ns throughout the period et the num of people testing positive by week du onset, split according venir disease standing (symptomatic or asymptomatic), denoted Tests,obs(w) et Testa,obs(w) in la semaine w de the exit phase. Nous used hospital entrée data up venir week 27 (29 June–5 July) venir account for the average delay from infection to hospitalization. Data in week 27 to be consolidated par waiting à la one extr week to account parce que le updates et missing les données (week 28, 6–12 July 2020).

We suspect a Poisson dispensés for hospitalizations et a binomial livré for the alors of people getting the test, therefore auto likelihood role is

$$L( mD ma mt ma|varTheta )=mathopprod limits_d=t_o^t_nP_ mP mo mi ms ms mo mn(H_ mo mb ms(d);H_ mp mr me md(d),eta ,t_0,alpha _ mL mD,t_ mL mD,alpha _ me mx mi mt,pi _ ma(w_d),pi _ ms(w_d)) imes prod _win me mx mi mtP_ mB mi mn mo mm mi ma ml( mT me ms mt_ ms, mo mb ms(w);i_ ms, mp mr me md(w),pi _ ms(w)) imes prod _win me mx mi mtP_ mB mi mn mo mm mi ma ml( mT me ms mt_ ma, mo mb ms(w);i_ ma, mp mr me md(w),pi _ ma(w))$$

where Θ = β, t0, αLD, tLD, αexit, πa(w), πs(w) indicates auto set of parameters venir be estimated, Hpred(d) is auto model-predicted number of hospital admissions on day d, is,pred(w) et ia,pred(w) are the model-predicted weekly incidences de symptomatic and asymptomatic cases, respectively, in la semaine w ns the exit phase, PPoisson is the probability fixed function of a pêcher distribution, PBinomial for a binomial distribution, is the time window considered pour the fit, and w is auto week in thé exit étape (weeks 20–26).

We reduced thé required computations with année optimization procedure in two steps, tons maximizing thé likelihood function in the pre-lockdown and lockdown phase to estimate the tons four parameters, and then maximizing thé likelihood in the exit phase de fixing the first four parameters that describe thé epidemic trajectory before the exit phase to their haute likelihood estimators (MLEs). This seconde step was additional simplified through année iterative procedure, et we seul through simulations that auto simplified optimization procedure is consistent and well-defined. The parameter an are was discover using wanderer software55. Fisher’s informations matrix was approximated at thé MLE value venir obtain thé corresponding to trust intervals. Simulations were then parameterized v 500 parameter sets derived from auto joint livré of transmission parameters at MLE (one stochastic simulation à la each parameter set). A Bayesian estimate ns the posterior parameter dispensés using Markov chain Monte carloss (MCMC) would certainly also oui been an alternative to hautement likelihood et confidence term estimation. In this case, however, MCMC would ont considerably slowed under parameter exploration, v negligible included value to auto fitting procedure.

We repeated maquette fitting starting from several beginning points et using various random alors streams. Values de fitted parameters et full details nous the different steps and the expérience performed are reported in the Supplementary information (Supplementary Figs. 6, 7 and Supplementary Table 3).

Simulation details

Simulations space initialized through 10 infected adults in the Ip compartment at time t0. Conditions météorologiques obtained 500 parameter to adjust from auto joint distribution of dévolution parameters at MLE et ran une stochastic simulation parce que le each parameter set. Therefore, errors in the detection rates computed in the output account pour the variability du the estimate de the parameters, in enhancement to the stochastic fluctuations ns the model. Conditions météorologiques find that auto errors in the estimations of thé detection rates acquired including auto variability de the parameters are slightly bigger than auto ones acquired with only stochastic fluctuation, suggesting that thé stochasticity du the modèle is the henchmen source ns error in the estimation of the detection rate.

Model selection analysis

To assess thé role ns the mechanistic modification of the la communication matrix informed de the different les données sources in the exit phase, conditions météorologiques compared our modèle to a simplified déditions assuming that communication patterns in auto exit phase do not change from pre-epidemic conditions, and that all changes in thé epidemic trajectory are explained exclusively by the transmissibility tout de suite contact. This is equivalent venir normalizing the la communication matrix to its largest eigenvalue et estimating thé reproductive proportion over time. Nous compared thé two models with auto Akaike informations criterion and found the accounting à la changes in contacts better describes thé epidemic trajectory (Supplementary Table 2 and Supplementary Fig. 5).

Comparison through serological estimates

We compared maquette projections with serological estimates from 3 independent studies7,25,26 (Fig. 3e et Extended les données Fig. 6).

Estimates by Carrat und al.7 offered ELISA-S tests and ELISA-NP tests. Auto sample was not representative du the population, et estimates to be weighted to account à la this bias. In the comparisons, we used thé results from a many imputation an approach performed by the authors and estimating a participant’s positivity with a likelihood of positivity based on observed juge results et covariates (see ref. 7 à la more details).

Estimates par Santé jc France25 are based nous at least one positive an outcome in one ns the complying with three tests: ELISA-S, ELISA-NP et a pseudo-neutralization audit that detects the presence de pseudo-neutralizing antibodies, representative du the presence du neutralizing antibodies as conferring protection against infection. Une analyse were performed nous residual sera obtained native clinical laboratories, et estimates to be weighted to account parce que le the lack du representativeness.

Estimates by EpiCoV26 (Enquête Épidémiologie et Conditions aux vie liées for Covid-19) provided ELISA-S tests et further validated these v a seroneutralizing antibody juge at higher specificity (see ref. 26 pour more details). This to be the seul seroprevalence survey that was conducted in a representative sample de the population. For this reason, nous used it ont the reference study.

For all studies, we décalage in Fig. 3e et Extended les données Fig. 6 thé estimates 14 days antérieur à the last blood collection to account for the temps needed to mount a detectable presence ns antibodies. à la the EpiCoV survey, conditions météorologiques used auto last cétait une date at i m sorry samples were sent back to the laboratory.

Modelling outcomes are in good agreement with auto serological approximates at the national level (Fig. 3e) et in the gros majority ns the regions (Extended les données Fig. 6). Projections tend venir be systematically smaller sized than serological approximates in two regions that were weakly affected by the epidemic (Pays de la Loire and Brittany), although they remained compatibles with observations.

Overall differences may be périmé to the restriction of the methods involved. First, the belles of tests, thé specificity levels, auto samples of the population tested, et the weighting et imputation approaches considered in every serological study can lead to differences across the three investigations. Nous note, parce que le example, that larger imbalances are observed in between EpiCov and Santé publique france results in those areas that proficient smaller epidemics. Nous used EpiCov oui the reference study as it was auto only one study the was conducted nous a representative sample du the population. Second, over there are limitations to auto dataset ns hospital admissions used to calibrate auto models: thé database infrastructure parce que le data circonscriptions became to work in mid-March et was fill in retrospectively. Présentation biases would inevitably alter auto inference of parameters in thé pre-lockdown phase. This may oui differed region passant par region; however, we have no way venir control pour this potential bias; possible errors would ont affected areas with small-size epidemics more than others. In support of this hypothesis, we remarque that a similar cible independent mathematical model fitted to regional hospitalization data24 in the sapin wave predicted small epidemics in Pays de la Loire and Brittany, similarly à our model.

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Reporting summary

Further informations on research design is easily accessible in the research Reporting an overview linked à this paper.