Lapport de la modlisation durant la pandmie de

Lapport de la modlisation durant la pandmie de grippe H1N1 Simon Cauchemez MRC Centre for Outbreak Analysis and Modelling Imperial College London Mon cursus 1998-2001: ENSAE, Ecole Nationale de Statistique et de lAdministration Economique 2001-2002: DEA de Biomathmatiques 2002-2005: Thse lINSERM U707 2005-2006: Postdoc lImperial College London 2007- : RCUK fellowship lImperial College London

Pandmie de grippe H1N1 en 2009 15 Avril 2009: Centers for Disease Control and Prevention (CDC) dtectent un nouveau virus grippal aux Etats-Unis; Cas ayant voyag rcemment au Mexique; Donnes qui arrivent initialement du Mexique suggrent un scenario de pandmie relativement svre. Comment les outils danalyse et de modlisation ont t utiliss pour valuer la situation & informer les prises de dcisions? Usage de lanalyse et de la modlisation Quantification de la transmission information utilise lors des changements de phase de lOMS. Evaluation de la svrit. Interprtation des tendances, now-casting . Prdiction de la trajectoire de la pandmie.

Evaluation de limpact des interventions. Interaction entre modlisateurs et sant publique (1) OMS personnel de lImperial intgr lOMS (Maria Van Kerkhove). Cration dun rseau informel de modlisateurs 40 participants de 20+ pays, produisant des revues sur diffrents sujets. US CDC liens avec modlisateurs acadmiques ds la premire semaine, personnel prsent durant les 2 premiers mois. Interactions informelles diffrents niveaux de gouvernement (Etats fdraux, Maison Blanche). EU ECDC rseau de modlisateurs. Interaction entre modlisateurs et sant publique (2)

GB modlisateurs sigeaient dans le groupe dexperts scientifiques sur la pandmie, sous-groupe ddi, analyses hebdomadaires temps-rel. COBR/CCC SAGE SPI-BC SPI-M SPI-JCVI SPI-CC [diagram: courtesy of Peter Grove] Quest-ce quon savait en t? Transmission au Mexique en Avril-Mai R=1.5 (95% Cr.I.:1.2-1.9) partir de la courbe des cas confirms.

R=1.4 (95% Cr.I.:1.1-1.9) par une mthode de calcul spatial backward. R=1.2 (95% Cr.I.:1.1-1.9) partir de lanalyse des squences gntiques. [Fraser et al, Science, en ligne 11 Mai 2009] Intervalle de gnration et isolation des cas Intervalle de gnration: 2.5-3 jours a partir de donnees britanniques; 2.6 jours a partir de donnees americaines. Dure dincubation de 1.5-2 jours. 0.25 0.20 Proportion

0.15 0.10 0.05 0.00 -2 0 2 4 6

8 Serial interval, days 10 12 Susceptibilit par age Enfants >2 fois plus susceptibles que les adultes, mme en tenant compte des diffrences de taux de contact avec les adultes. Incidence per 100k relative to overall average

Donnes sur le Mexique, la GB, les mnages amricains, et plus tard la surveillance sentinelle donnaient la mme image. 3 model 2.5 Qflu (15 -Jun to 13-July) 2 1.5 1

0.5 0 0-4 5-14 15-24 25-44 45-64 65-74 75+ Age group Transmission dans les mnages Analyse USA (collaboration avec CDC Cauchemez et al NEJM 2010): Analyse GB (collaboration avec HPA Ghani et al PLoS Current 2009): Taux dattaque secondaire 8% pour infection H1N1 confirme, 11% pour syndrome grippal (en prsence de traitements antiviraux).

Secondary attack rate Taux dattaque secondaire 8% pour syndrome grippal plutt faible par rapport aux pandmies prcdentes. 0.6 0.5 0.4 0.3 0.2 0.1 0.0 2 3

4 5 Household size 6 Fermeture des coles Fermeture peut prsenter un intrt en particulier une rduction de ~40% de lincidence au pic; mais a un cout trs lev [Cauchemez et al, Lancet ID, 2009]. Peut-on maximiser limpact (e.g. rduction du pic de demande de soin), en

minimisant le cot? Travail effectu pour CDC montre que des fermetures de 1-2 semaines bases sur un suivi de labsentisme local peuvent rduire le pic local de faon substantielle. Mais la pandmie H1N1 na pas t suffisamment svre pour justifier une fermeture des coles Angleterre analyse et modlisation Mais besoin de connatre le vrai nombre dinfections

pour faire des prdictions. Daily case incidence/100k 160 140 120 100 80 60 40 20 0 1-Jun 1-Jul

1-Aug 1-Sep 1-Oct 1-Nov 1-Dec 1-Jan Date 180 Daily case incidence/100k Modles reproduisent bien la croissance observe en Juin-Juillet, et les rductions aprs le dbut des vacances. 180 160

140 120 100 80 60 40 20 0 Date 1-Feb 1-Mar Svrit Evaluation de la svrit au tout dbut Inquitude initiale partir des donnes mexicaines mais

seulement les cas svres taient dtects. En mai, nous avons estim 23,000 le nombre dinfects au Mexique fin avril partir de lextension de la diffusion internationale en avril, en faisant une hypothse de mlange homogne entre les voyageurs et les locaux. Donnait un taux de fatalit de ~0.4% partir des morts suspectes ou confirmes et de ~0.04% pour les morts confirmes seules. De ath s ICU Hospitalizations Symptomatic

Infections [Fraser et al, Science, en ligne 11 Mai 2009] Lexprience de lhmisphre sud Analyse commence en Aout, aprs le pic. R: 1.25-1.55 Estimations du taux dinfection: 20-40%. Trs faible taux de fatalit (<0.01%). Opatowski et al, Epidmiologie de la grippe

H1N1pdm community outbreak in Pennsylvania [with CDC] 2 interviews of households Demographic & clinical information collected on students and their family members 2 phone interviews: May 16-19th -141 ARI cases among 370 students (AR:38%) - 129 ARI cases among 899 household members (AR:14%) Censoring: 40% of people only in 1st

interview Around June 4th Surveys in school for 4th graders: Activities, Seating charts, Playmates. Activities Social networks - Who are your playmates? Seating charts Girls

Boys Transmission rates in the school and in the household School Age dependent susceptibility Household Generation time in places Gender-related mixing & transmission patterns Social networks: Students are 4 times more likely to play with students of the same gender

Girls Boys Evidence that this aspect of mixing patterns affected the transmission dynamics i) Boys had onset before girls! (p=0.023) ii) Bayes Factor for model with gender-effect: 8.0 Seating plan: higher risk of infection if neighbour is sick? Simple approach: Count the number of pairs of neighbours that are sick in each class room. Compare with the number of pairs expected if the seating arrangement was

due to chance. Permutation test. Conclusion: observed numbers of pairs are not significantly different than what would be generated by chance. Observed Expected Overall 4th graders 19 20 [16,24] (p=0.58) Classroom A

12 13 [11,15] (p=0.62) Classroom B 2 2 [0,4] (p=0.23) Classroom C 1 2 [0,3] (p=0.53) Classroom D

4 1 [4,6] (p=0.25) Impact of a late 1-week school closure Late closure: 27% of students had already had symptoms at time of closure No noticeable reduction detected i) Estimate of transmission rates between students after closure relative to before closure: 0.8 (0.4,1.4) ii) School reproduction number - on the week of the closure: 0.3 (0.1,0.6) - on the following week: 0.3 (0.0, 0.7)

Les leons Problmes lis aux donnes & incertitudes Dnominateurs ne pas connaitre le nombre dinfections a rendu lestimation de la svrit et la prdiction du pic difficile: Serosurveillance Cohortes Web / tlphone Impact des variations saisonnires sur la transmission. Communication Difficult de communiquer lincertitude. Prdictions vs scenarios pire scenario raisonnable utile pour planifier, mais diffrent dune prdiction.

Conclusions Analyses et modlisation: Beaucoup d analyses statistiques; Un des lments dinformation parmi dautres pour les dcideurs; Pas une boule de cristal consommateurs de plus en plus informs. A pu savrer utile sur certaines questions stratgiques.

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