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Gdrp pres oct_2018_niels_hen

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ERC - consolidator grant - TransMid Towards Open Science by Niel Hens

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Gdrp pres oct_2018_niels_hen

  1. 1. ERC-consolidator grant - TransMID 1/17 ERC-consolidator grant - TransMID Towards Open Science Niel Hens www.simid.be www.simpact.org www.socialcontactdata.org 24 October 2018
  2. 2. ERC-consolidator grant - TransMID 2/17 Introduction Social contact data in mathematical epidemiology Several infectious diseases are transmitted by air. Social contact data help to identify social mixing behaviour and implied changes in during holidays or when ill. This translates into: better targets for vaccination better prediction of epidemic spread better assessing impact of containment/isolation strategies enabling better estimation of age-specific parameters
  3. 3. ERC-consolidator grant - TransMID 3/17 Introduction Social contact data Record of interactions among people in different settings Measured with sensors and/or surveys Example: Belgian Contact Survey Part of POLYMOD project Period March - May 2006 750 participants, selected through random digit dialing Diary-based questionnaire Two main types of contact: close and non-close Total of 12,775 contacts No ethical approval requested Hens et al. (2009a,b)
  4. 4. ERC-consolidator grant - TransMID 4/17 Introduction EU mixing patterns common structure converging off-diagonals: parents get older
  5. 5. ERC-consolidator grant - TransMID 5/17 Introduction TransMID Translational and Transdisciplinary research in Modelling Infectious Diseases It’s importance: testing fundamental hypotheses e.g. household random mixing assumption (Goeyvaerts et al., 2017) behavioural changes e.g. week/weekend, holiday patterns (De Luca et al., 2017) environmental and demographic changes e.g. density and frequency dependence (Hoang et al. ongoing) proper integration in dynamic models of infectious diseases e.g. PDE implementation on GPU (Kovac et al., 2018)
  6. 6. ERC-consolidator grant - TransMID 6/17 Introduction Studies conducted: systematic review (Hoang et al., 2018) All these datasets need to be shared among researchers!
  7. 7. ERC-consolidator grant - TransMID 7/17 Introduction Studies conducted: systematic review (Hoang et al., 2018) Systematic review: overview of existing studies qualitative comparison of methods qualitative comparison of implementation of surveys Emerged from a kick-off meeting Providing ownership → open science approach research efficiency
  8. 8. ERC-consolidator grant - TransMID 8/17 Introduction www.socialcontactdata.org Open science approach All publications made available on BioRXiv first (Review: March-October: 1113 abstract and 301 pdf reads) Collection of datasets and information on social contact surveys made available via Zenodo with doi for the original authors with articles Data currently available: POLYMOD (8), Peru, Zimbabwe, France, Hong Kong, Vietnam R package by Sebastian Funk Directly links to and operates on available datasets Hackaton to add other methods of analysis
  9. 9. ERC-consolidator grant - TransMID 9/17 Open Science Sharing social contact data: caveats and solutions Two aspects need to be taken care of: anonymity: The data must not allow for individual identification. avoid names, use Ids geographical information aggregated (e.g. avoid zipcodes) avoid ”unique” situations (e.g. a large family in a small town) informed consent: ask for informed consent of participant if not available (e.g. oral consent) ask for ethical approval Data owners are provided with guidelines to: coding data - anonymity formatting data consistently with existing datasets In this way confidential information is handled by who is entitled to
  10. 10. ERC-consolidator grant - TransMID 10/17 Open Science
  11. 11. ERC-consolidator grant - TransMID 11/17 Open Science Data structure duplicate structure: people share what they can/are willing to share personal data in data collector’s hands foresee most common (future) designs: there is no overarching ideal structure people are now using this format as a default to organise new data
  12. 12. ERC-consolidator grant - TransMID 12/17 Open Science Modelling approaches Providing data access is one thing Providing code for others to use is another Hens and Wallinga - (Wiley StatsRef, 2018) Wallinga, van de Kassteele and Hens - HIDDA-project new smoothing techniques - Vandendijck et al. (2018) 0 10 20 30 40 50 60 70 010203040506070 Age of the respondent Ageofthecontact 50000 100000 150000 200000 250000
  13. 13. ERC-consolidator grant - TransMID 13/17 Open Science Don’t carry the world on your shoulders Providing data access is one thing Providing code really helps dessimination The excuse of not sharing data/code, etc that can be misused is not an excuse organise workshops to teach others (PRISM workshop, August 2018) opportunity to publish tutorials
  14. 14. ERC-consolidator grant - TransMID 14/17 Lessons learned Lessons learned GDPR (the way forward): our experience with GDPR is limited: need for constructive dialogue ethical approval and GDPR should be integrated sort out GDPR with researchers from countries outside EU Unclear whether the EC will impose other guidelines: Open science event Jonge Academie and departement EWI (2017) ERC workshop on open science (2018) . . .
  15. 15. ERC-consolidator grant - TransMID 15/17 Next Steps Next steps social contact data new data collection is planned Internal sharing of data via Google Drive serological data the ERC part I didn’t talk about . . . more troublesome: more sensitive data, especially if moving science forward 15-17 May 2018: workshop discussing ideas with researchers willingness to share data, but unclear what is possible longevity?
  16. 16. ERC-consolidator grant - TransMID 16/17 Next Steps
  17. 17. ERC-consolidator grant - TransMID 17/17 Next Steps References De Luca, G., Van Kerckhove, K., Coletti, P., Poletto, C., Bossuyt, N., Hens, N., and Colizza, V. (2017). The impact of regular school closure on seasonal influenza epidemics: a data-driven spatial transmission model for Belgium. bioRxiv. Goeyvaerts, N., Santermans, E., Potter, G., Torneri, A., Kerckhove, K. V., Willem, L., Aerts, M., Beutels, P., and Hens, N. (2017). Household Members Do Not Contact Each Other at Random: Implications for Infectious Disease Modelling. BioRXiv, page 220202. Hens, N., Ayele, G. M., Goeyvaerts, N., Aerts, M., Mossong, J., Edmunds, J. W., and Beutels, P. (2009a). Estimating the impact of school closure on social mixing behaviour and the transmission of close contact infections in eight European countries. BMC Infectious Diseases, 9:187. Hens, N., Goeyvaerts, N., Aerts, M., Shkedy, Z., Damme, P. V., and Beutels, P. (2009b). Mining social mixing patterns for infectious disease models based on a two-day population survey in Belgium. BMC Infectious Diseases, 9:5. Hoang, T. V., Coletti, P., Melegaro, A., Wallinga, J., Grijalva, C., Edmunds, J., Beutels, P., and Hens, N. (2018). A systematic review of social contact surveys to inform transmission models of close contact infections. bioRxiv. Kovac, T., Haber, T., Reeth, F. V., and Hens, N. (2018). Heterogeneous computing for epidemiological model fitting and simulation. BMC Bioinformatics, 19(1):101. Vandendijck, Y., Camarda, C. G., and Hens, N. (2018). Cohort-based smoothing methods for age-specific contact rates. bioRxiv.

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