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

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. 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. 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. ERC-consolidator grant - TransMID 4/17
Introduction
EU mixing patterns
common structure
converging off-diagonals: parents get older

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. 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. 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. 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. 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. ERC-consolidator grant - TransMID 10/17
Open Science

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. 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. 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. 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. 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. ERC-consolidator grant - TransMID 16/17
Next Steps

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.