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VERSION:2.0
PRODID:https://murmitoyen.com/events/vanille/udem/
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UID:69e0430819a9d
DTSTAMP:20260415T220144
DTSTART:20140120T120000
SEQUENCE:0
TRANSP:OPAQUE
DTEND:20140120T130000
URL:https://murmitoyen.com/events/vanille/udem/detail/362316
LOCATION:Université de Montréal - 7101 avenue du Parc\, 7101\, avenue du 
 Parc\, Montréal\, QC\, Canada\, H3N 1X9
SUMMARY:Influences de l'environnement sur le risque de maladies infectieuse
 s : exemples d'études utilisant différentes échelles spatiales
DESCRIPTION:Ce séminaire est organisé par l'Institut de recherche en sant
 é publique de l'Université de Montréal (IRSPUM).ConférencierMark L. Wi
 lson is an epidemiologist and ecologist with broad interests in infectious
  diseases\, including the analysis of transmission dynamics\, evolution of
  host-vector-parasite systems\, and determinants of human disease risk.  
 Following doctoral training at the Harvard School of Public (Lyme disease)
 \, he lived in Senegal for four years\, studying Rift Valley fever and Cri
 mean-Congo hemorrhagic fever. Later\, at Yale University (1991-96)\, he co
 llaborated on studies of various infectious diseases\, mostly in South Ame
 rica. Since moving to University of Michigan (1997)\, Prof. Wilson and stu
 dents have studied infectious diseases (especially malaria and dengue) in 
 a dozen countries worldwide. Most projects address environmental and socia
 l variation\, in time and space\, as they impact on pathogen transmission 
 patterns\, the ecology of disease risk\, and prevention options.  Résum
 éDiverse features of biophysical and socio-economic environments alter th
 e risk of infectious diseases.  Environmental impacts function\, sometime
 s differently\, at several levels of organization (individuals\, communiti
 es\, social classes) and temporal scales. Some of this variation can be be
 tter understood by analyzing spatial patterns at various resolutions. Appr
 oaches to such spatial analyses will be briefly characterized\, and then i
 llustrated using some resent studies of vector-borne diseases in the Ameri
 cas (West Nile virus\, dengue) and Africa (malaria). When applied intentio
 nally and carefully\, spatial statistical tools can complement more tradit
 ional ecological or epidemiological data and analyses\, thus providing add
 itional insights aimed at developing causal inferences\, evaluating interv
 entions\, and designing policies to reduce disease risk and improve the pu
 blic's health.
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