<http://www.w3.org/2009/08/skos-reference/skos.html#note>
"the open-source outbreak: H1N1, the olympics and new directions for public health dr. jennifer gardy bc centre for disease control genome research laboratory outline \u2022\u202F pandemic H1N1: the first open- source outbreak \u2022\u202F H1N1/Olympics research project \u2022\u202F descriptive epidemiology \u2022\u202F predictive epidemiology genomics enables: present: descriptive epidemiology of a bacterial/viral pathogen  future: predictive epidemiology via genome surveillance part 1 \u2022\u202F pandemic H1N1: the first open- source outbreak \u2022\u202F H1N1/Olympics research project \u2022\u202F descriptive epidemiology \u2022\u202F predictive epidemiology rewind to march 2009 pandemic!open source outbreak sharing germs, sharing genomes pandemic! april 25: 1st genome april 26: international wiki 13 people, 8 institutes, 4 countries http://tree.bio.ed.ac.uk/groups/influenza april 26: origins of the virus calculated april 30: origins data published 5 days from sequence  to open-access paper pandemic! april 25: 1st genome may 6: 69 virus\u2019 RNA virus entered human population late 08/early 09 may 5: first major paper submitted may 11: first major paper published pandemic! april 25: 1st genome may 6: 69 virus\u2019 RNA june 11: 250+ papers SARS, 2003 day 0 virus isolation H1N1, 2009 day 19  one viral genome day 19  100+ viral genomes where/when it arose multiple papers vaccine seed strain day 0 virus isolation how? technological advances shift in scientists\u2019 attitudes genomes = easy, cheap, fast human genome project (1990) 10 years to draft 3 more to complete $3 billion 100s of people spring 2009 four weeks $48,000 worth of reagents three-person team stephen quake, stanford bioengineering data = easy, cheap, fast from flickr user amy and casey  the file-sharing generation Mann et al, Comm. of the ACM 52(3):135. (2009) collaboration pandemic H1N1: the first open-source outbreak part 2 \u2022\u202F pandemic H1N1: the first open- source outbreak \u2022\u202F H1N1/Olympics research project \u2022\u202F descriptive epidemiology \u2022\u202F predictive epidemiology BCCDC &amp; pH1N1: lab testing \u2022\u202F april/may: surge in lab test volume BCCDC &amp; pH1N1: research &amp; activities \u2022\u202F \u201Cone-stop pandemic shop\u201D \u2022\u202F sero-epi survey \u2022\u202F vaccine uptake campaign \u2022\u202F mathematical modelling \u2022\u202F informatics infrastructure \u2022\u202F genomics sequence 400-500 H1N1 genomes, observe viral evolution in real-time. \u201Cinfluenza virus is sloppy, capricious and promiscuous\u201D \u2013 world health organization intersubtypic reassortment intrasubtypic reassortment point mutation H1N1 genomics project overview 1. targeted sequencing: public health outcomes \u2022\u202FSNP-type and sequence key regions \u2022\u202Fmonitor changes \u2022\u202Fadjust public health interventions as needed \u2022\u202Fidentify interesting virus e.g. 4 samples with point mutations in M gene rendered typing assay probe ineffective = new probe. 2. whole-genome sequencing: evolution \u2022\u202Fmonitor viral evolution in real-time \u2022\u202Fdetermine effect of Olympics on viral evolution 3. metagenomics: co-infections \u2022\u202Fcapitalize on available samples \u2022\u202Fexplore patterns of co- infection BCCDC\u2019s H1N1 genomics project \u2022\u202F all sequence will be made publicly available \u2022\u202F collaborating with social scientists, FNIH, GSC, international group of phylodynamics researchers using orwik, GoogleWave part 3 \u2022\u202F pandemic H1N1: the first open- source outbreak \u2022\u202F H1N1/Olympics research project \u2022\u202F descriptive epidemiology \u2022\u202F predictive epidemiology what is descriptive epidemiology of a pathogen? where did it come from, how is it spreading, what makes it pathogenic? story 1: where did it come from? SARS \u2022\u202F first novel EID of 21st century \u2022\u202F Nov. 2002 \u2013 atypical pneumonia, China \u2022\u202F March 2003 \u2013 international spread \u2022\u202F July 2003 \u2013 containment (~800 deaths) \u2022\u202F suspected animal origin \u2022\u202F sequenced by BCCDC &amp; others but\u2026 high nucleic acid identity, not found in wild civets \u2022\u202F SARS CoV and others found in bats \u2022\u202F older, evolutionarily stable \u2022\u202F endemic since mid-1980s story 2: how is it spreading? influenza \u2022\u202F IGSP: 4000 influenza genomes across time, space, species, type  source-sink model of emergence doi:10.1038/nature06945 co-circulating lineages w/ reassortment doi:10.1371/journal.ppat.1000133 antiviral resistance is dynamic story 3: what makes it pathogenic? Dengue \u2022\u202F 50-100 million infections per year \u2022\u202F four serotypes, each with multiple genotypes, geographic distribution \u2022\u202F large-scale sequencing effort underway (target= 3500 genomes) \u2022\u202F genomic correlates of severity bccdc story 1: outbreak evolution TB \u2022\u202F 36 complete M. tuberculosis genomes from VI outbreak to compare molecular evolution vs. field epidemiology data bccdc story 1: unusual isolate S. pneumo \u2022\u202F genome from serotype 5 DTES outbreak contains an unusual genomic island (sugar usage?) descriptive epidemiology: the future \u2022\u202F can answer questions around origins, evolution, pathogenicity, but not clinically- relevant questions \u2022\u202F effect of co-infections? links between evolution of co-infecting pathogens? role of immunity? viral quasispecies within an individual? epistatic interactions? genomes of most common pathogens? virus discovery?  \u2022\u202F genome data must be collected along with extensive host, co-infection data descriptive epidemiology part 4 \u2022\u202F pandemic H1N1: the first open- source outbreak \u2022\u202F H1N1/Olympics research project \u2022\u202F descriptive epidemiology \u2022\u202F predictive epidemiology stopping the next outbreak before it starts months of undiscovered circulation in people sometimes cover-ups, infrastructure problems most often poor surveillance, novel pathogens population sampling to pick up threats before the lab or clinic predictive epidemiology: genome surveillance \u2022\u202F genomics technology exists, global sentinel system is the roadblock \u2022\u202F needs infrastructure, standards, reporting \u2022\u202F local/national sentinel systems effective, start by incorporating genomics into these \u2022\u202F must ultimately consider diverse species, geography, demographics over time to be effective 10.1038/nature05775 wildlife other animals antimicrobial resistance vector-borne 0.1038/nature06536 from flickr user stuck in customs "@en
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