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TZID:Europe/Paris
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BEGIN:VEVENT
UID:0-725@lptms.universite-paris-saclay.fr
DTSTART:20200204T110000Z
DTEND:20200204T120000Z
DTSTAMP:20200128T113624Z
URL:http://www.lptms.universite-paris-saclay.fr/seminars/seminaire-du-lptm
 s-eugenio-valdano-ucla/
SUMMARY:Séminaire du LPTMS: Eugenio Valdano (UCLA) - Salle des séminaires
  du FAST et du LPTMS\, bâtiment Pascal n°530 - 4 Fév 20 11:00
DESCRIPTION:Predicting epidemic risk from contact and mobility data\nEugeni
 o Valdano (UCLA)\n\nThe vulnerability of a host population to a specific d
 isease measures how likely pathogen introduction will lead to an epidemic 
 outbreak\, and how hard it is to contain or eliminate an ongoing one. Pred
 icting vulnerability is thus key to designing risk-reduction strategies th
 at limit disease burden on public health and economic development. To do t
 hat\, highly-resolved data tracking contacts and mobility of the host popu
 lation need to integrate into detailed models of disease dynamics. This re
 presents a twofold challenge. Firstly\, we need theoretical frameworks tha
 t turn data feeds into predictors of epidemic risk\, and can identify whic
 h of the structural features of the host population drive its vulnerabilit
 y. Secondly\, we need new ways to access\, analyze\, and share the relevan
 t contact and mobility data: a necessary step to make our predictions real
 istic and reliable. In my talk\, I will address both issues. I will show h
 ow to analytically derive the conditions that discriminate between epidemi
 c regime and quick pathogen extinction\, by representing diseases spreadin
 g on empirically measured contacts as dynamical processes on time-evolving
  complex networks. The analytical core of this theory leads to a broad ran
 ge of applications. At the same time\, its data-driven nature prompts cont
 ext-specific predictions that can inform policymaking\, as I will show in 
 two case studies: reorganizing nurse scheduling to reduce the risk of spre
 ad of healthcare-associated infections\; linking the features of livestock
  trade movements to the spatial spread of cattle diseases. The latter appl
 ication is also an example of how limited access and incomplete data colle
 ction represent a big hurdle to predictive vulnerability analysis. To over
 come this\, I will present a collaborative platform for analyzing and comp
 aring trade networks coming from several European countries. Using a bring
  code to the data approach\, our platform surmounts the strict regulations
  preventing data sharing\, and builds an algorithm that predicts vulnerabi
 lity even in situations when limited data on cattle trade are available.
CATEGORIES:seminars
LOCATION:Salle des séminaires du FAST et du LPTMS\, bâtiment Pascal n°53
 0\, rue André Riviere\, Orsay\, 91405\, France
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=rue André Riviere\, Orsay\
 , 91405\, France;X-APPLE-RADIUS=100;X-TITLE=Salle des séminaires du FAST 
 et du LPTMS\, bâtiment Pascal n°530:geo:0,0
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