influenza models: values & limits
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2 influenza models: values & limits Vittoria Colizza INSERM U Pierre et Marie Curie vittoria.colizza@inserm.fr
![[dynamical] modeling 101](/docs-images/80/81731575/images/3-1.jpg "REALITY aims ingredients")
3 [dynamical] modeling 101 REALITY aims ingredients ABSTRACTION OF REALITY assumptions limitations validation
4 aims UNDERSTAND identify key mechanisms obtain basic conceptual results build/test theories identify trends estimate parameters PREDICT explore scenarios test/evaluate/compare interventions answer specific questions provide forecasts (?)
5 ingredients PEOPLE INTERACTIONS /MOBILITY DISEASE
6 models complexity REALISM low moderate high >> DATA...
7 riding the data tsunami Helen Fischer of chemistry.com, available at 90% of the world s digital data has been created in just the last two years (source: IBM)
8 data
9 data time of infection infectious recovered
10 3 applications to H1N1 pandemic Carrat et al.: Planning for the next influenza H1N1 season: a modelling study. BMC Inf Dis (2010)
11 weekly incidence /100,000 Carrat et al. study THE PROBLEM use the RS data of the first pandemic season in France to: estimate attack rates, rates of preand post-exposure immunity explore hypothetical scenarios of virus reintroduction with varying levels of cross-immunity THE APPROACH individual-centered model demographic characteristics and household sizes network of contacts detailed description of healthcare seeking behavior, treatment RESULTS pre-exposure immunity: young adults elderly 36% 85% second pandemic season, with cross protection: 70% 50% 30% small epidemic size disease burden ~ 1st season substantial burden
12 some limitations... difficult to parameterize in real-time highly detailed for single population, but space is missing
13 2 nd application to H1N1 pandemic Determinants of the spatiotemporal dynamics of the 2009 H1N1 pandemic in Europe: implications for real-time modelling. PLOS Comp Biol (2011) Merler et al.
14 Predicted peak Merler et al. study THE PROBLEM use epidemic data and available estimates up to June 2009 to: evaluate the role of EU sociodemographic structures, school calendars, intra-eu mobility in shaping the spatio-temporal spread RESULTS THE APPROACH agent-based model informed with EU synthetic population explicit spatial structure restricted to EU + model for seeding events school calendars available epidemic estimates
15 some limitations... difficult to parameterize in real-time high resolution data available for few developed countries
16 3 rd application to H1N1 pandemic Seasonal transmission potential and activity peaks of the new influenza A/H1N1: a Monte Carlo likelihood analysis based on human mobility. BMC Med (2009) Real-time numerical forecast of global epidemic spreading: case study of 2009 A/H1N1pdm. BMC Med, in press. Balcan et al. Tizzoni et al.
17 the GLEAM study THE PROBLEM use the epidemic data up to June 2009 to: estimate the seasonal transmission potential of the pandemic predict in real-time the future spatiotemporal propagation at the global level and in particular the peak timing for the upcoming winter season end of april mid may mid june oct/.../feb parameter estimation predictions
18 the approach population data in cells ¼ x ¼ daily commuting data 3400 airports in 220 countries 20,000 connections traffic data (IATA, OAG) >99% commercial traffic customizable compartmental model
19 GLEAM: H1N1 output example
20 predictions and assessment
21 importance of mobility
22 some limitations... lack of high resolution description of socio-demographic structures
23 remaining challenges data to inform models social, behavioral, demographic, mobility... underdeveloped countries? models: integrating different approaches sharing preparedness for emergency (building up collaborative workflows in peacetime) surveillance: high resolution, same case-definition, healthseeking behavior,...
24 influenzanet.org
25 acknowledgments & refs EPICX lab U707 INSERM & UPMC
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