Infectious disease epidemiology: slowly moving towards open science 1/24 Infectious disease epidemiology: slowly moving towards open science Niel Hens ‘Embracing Data Management - Bridging the Gap Between Theory and Practice’, FOSTER, 4 June 2015, Brussels Infectious disease epidemiology: slowly moving towards open science 2/24 Outline 1 Introduction 2 Infectious Disease Epidemiology & Statistics Statistics Infectious Disease Epidemiology 3 Discussion & Recommendations Infectious disease epidemiology: slowly moving towards open science 3/24 Introduction Opinions are contagious, or not? LaCour, M. and Green, D. Science, 2014 - retracted When contact changes minds: an experiment on transmission of support for gay equality → Outcome: Contact with minorities coupled with discussion of issues pertinent to them is capable of producing a cascade of opinion change. → Retraction because, among other issues: ‘LaCour has not produced the original survey data from which someone else could independently confirm the validity of the reported findings.’ The Chronicle, 21 May 2015: How 2 Persistent Grad Students Upended a Blockbuster Study Infectious disease epidemiology: slowly moving towards open science 4/24 Introduction Compliance with data sharing is challenging. Allison, D. Science, 2009: The movement toward open data has begun: NIH, Science, Nature journals. Nature Genetics requires authors to publicly deposit microarray gene expression data: reproduction of published work for only 2 of 18 papers. data were either not found or incomplete or original analysis descriptions were unclear Public Library of Science journals require data sharing until recently this didn’t happen now they insist Infectious disease epidemiology: slowly moving towards open science 5/24 Introduction Open Science ‘Open Science - Prinzipien’ by Andreas E. Neuhold - Own work. Licensed under CC BY 3.0 Infectious disease epidemiology: slowly moving towards open science 6/24 Infectious Disease Epidemiology & Statistics Statistics Statistics: clinical trials Clinical trials are the best regulated studies https://clinicaltrials.gov https://www.clinicaltrialsregister.eu JAMA: clinical trials sponsored by drug companies have to be reanalyzed by ‘academic statisticians’ before acceptance for publication in the journal Keiding, N. Biostatistics, 2010 There is usually much more to ‘research’ than the statistical analysis. A ‘reasonable choice of statistical model’ Clinical trials have changed data management Unfortunately not all guidelines and recommendations make sense and require constant re-evaluation. Infectious disease epidemiology: slowly moving towards open science 7/24 Infectious Disease Epidemiology & Statistics Statistics Statistics: sharing code Editorial board meeting @Biometrics: issue: mandatory submission of R-package (stats software) AEs voted against: burden & risk of packages with limited lifespan Sharing code: ‘available upon request’ The Journal of Statistical Software: 1st rank Infectious disease epidemiology: slowly moving towards open science 8/24 Infectious Disease Epidemiology & Statistics Infectious Disease Epidemiology Infectious Disease Epidemiology: sharing data? Mostly observational studies or surveys: outbreak data: SARS, H1N1, Ebola, MERS, . . . - no clear data format social contact survey (EU FP6 project POLYMOD) Emerging trend: syndromic surveillance the great influenza survey funding issues little political support Infectious disease epidemiology: slowly moving towards open science 8/24 Infectious Disease Epidemiology & Statistics Infectious Disease Epidemiology Infectious Disease Epidemiology: sharing data? The Good, the Bad and the Ugly Infectious disease epidemiology: slowly moving towards open science 9/24 Infectious Disease Epidemiology & Statistics Infectious Disease Epidemiology The good, . . . Benjamin Cowling and colleagues: influenza research: https://sites.google.com/site/bencowling88/datasets shares raw data & code according to NIH guidelines promote reproducibility of results allow others to conduct their own analyses allow others to compare other data allow others to plan their own studies John Edmunds and colleagues: Ebola http://cmmid.lshtm.ac.uk/research/ebola/ shares code and results in realtime on website publications follow later . . . Infectious disease epidemiology: slowly moving towards open science 10/24 Infectious Disease Epidemiology & Statistics Infectious Disease Epidemiology The good, . . . Jombaert et al. (Epidemics, 2014): OutbreakTools - Hackathon - Git repository Hens et al. (2012): ±9800 e-chapter downloads - all data & code available 1Hens · Shkedy · AertsFaes · Van Damme · BeutelsStatistics for Biology and HealthNiel Hens · Ziv Shkedy · Marc Aerts · Christel Faes · Pierre Van Damme · Philippe BeutelsModeling Infectious Disease Parameters Based on Serological and Social Contact Data A Modern Statistical Perspective Statistics for Biology and HealthModeling Infectious Disease Parameters Based on Serological and Social Contact DataNiel Hens · Ziv ShkedyMarc Aerts · Christel FaesPierre Van Damme · Philippe BeutelsStatistics / Life Sciences,Medicine, Health Sciences SBH Modeling Infectious Disease Parameters Based on Serological and Social Contact Data A Modern Statistical Perspective Mathematical epidemiology of infectious diseases usually involves describing the ! ow of individuals between mutually exclusive infection states. One of the key parameters describing the transition from the susceptible to the infected class is the hazard of infection, o" en referred to as the force of infection. # e force of infection re! ects the degree of contact with potential for transmission between infected and susceptible individuals. # e mathematical relation between the force of infection and e$ ective contact patterns is generally assumed to be subjected to the mass action principle, which yields the necessary information to estimate the basic reproduction number, another key parameter in infectious disease epidemiology.It is within this context that the Center for Statistics (CenStat, I-Biostat, Hasselt Uni-versity) and the Centre for the Evaluation of Vaccination and the Centre for Health Economic Research and Modelling Infectious Diseases (CEV, CHERMID, Vaccine and Infectious Disease Institute, University of Antwerp) have collaborated over the past 15 years. # is book demonstrates the past and current research activities of these institutes and can be considered to be a milestone in this collaboration.# is book is focused on the application of modern statistical methods and models to estimate infectious disease parameters. We want to provide the readers with so" ware guidance, such as R packages, and with data, as far as they can be made publicly available. 9 781461 440710ISBN 978-1-4614-4071-0 Infectious disease epidemiology: slowly moving towards open science 11/24 Infectious Disease Epidemiology & Statistics Infectious Disease Epidemiology the bad, . . . violation of scientific integrity tax payer’s money human health Infectious disease epidemiology: slowly moving towards open science 12/24 Infectious Disease Epidemiology & Statistics Infectious Disease Epidemiology and the ugly. Sharing of raw data without documentation: Even with documentation it can go wrong . . . Infectious disease epidemiology: slowly moving towards open science 13/24 Infectious Disease Epidemiology & Statistics Infectious Disease Epidemiology Social Contact Survey Goal: Using data on social contacts to estimate age-specific transmission parameters for respiratory-spread infectious agents. Objectives Disentangle contact behaviour from transmission process Get insights in predictiveness of social contact data Get new insights in the transmission process Infectious disease epidemiology: slowly moving towards open science 14/24 Infectious Disease Epidemiology & Statistics Infectious Disease Epidemiology Social Contact Survey Wallinga et al. (2006): Utrecht POLYMOD pilot study: Beutels et al. (2006) main study: Mossong et al. (2008) Social Contacts and Mixing Patterns Relevant tothe Spread of Infectious Diseases Joël Mossong1,2*, Niel Hens3, Mark Jit4, Philippe Beutels5, Kari Auranen6, Rafael Mikolajczyk7, Marco Massari8, Stefania Salmaso8, Gianpaolo Scalia Tomba9, Jacco Wallinga10, Janneke Heijne10, Malgorzata Sadkowska-Todys11, Magdalena Rosinska11, W. John Edmunds4 1 Microbiology Unit, Laboratoire National de Santé, Luxembourg, Luxembourg, 2 Centre de Recherche Public Santé, Luxembourg, Luxembourg, 3 Center for Statistics, Hasselt University, Diepenbeek, Belgium, 4 Modelling and Economics Unit, Health Protection Agency Centre for Infections, London, United Kingdom, 5 Unit Health Economic and Modeling Infectious Diseases, Center for the Evaluation of Vaccination, Vaccine & Infectious Disease Institute, University of Antwerp, Antwerp, Belgium, 6 Department of Vaccines, National Public Health Institute KTL, Helsinki, Finland, 7 School of Public Health, University of Bielefeld, Bielefeld, Germany, 8 Istituto Superiore di Sanità, Rome, Italy, 9 Department of Mathematics, University of Rome Tor Vergata, Rome, Italy, 10 Centre for Infectious Disease Control Netherlands, National Institute for Public Health and the Environment, Bilthoven, The Netherlands, 11 National Institute of Hygiene, Warsaw, Poland Funding: This study formed part ofPOLYMOD, a European Commissionproject funded within the SixthFramework Programme, Contractnumber: SSP22-CT-2004–502084.The funders had no role in studydesign, data collection and analysis,decision to publish, or preparationof the manuscript. Competing Interests: The authorshave declared that no competinginterests exist. Academic Editor: Steven Riley,Hong Kong University, Hong Kong Citation: Mossong J, Hens N, Jit M,Beutels P, Auranen K, et al. (2008)Social contacts and mixing patternsrelevant to the spread of infectiousdiseases. PLoS Med 5(3) e74. doi:10.1371/journal.pmed.0050074 Received: August 8, 2007Accepted: February 15, 2008Published: March 25, 2008 Copyright: ! 2008 Mossong et al.This is an open-access articledistributed under the terms of theCreative Commons AttributionLicense, which permits unrestricteduse, distribution, and reproductionin any medium, provided theoriginal author and source arecredited. Abbreviations: BE, Belgium; DE,Germany; FI, Finland; GB, GreatBritain; IT, Italy; LU, Luxembourg; NL,The Netherlands; PL, Poland * To whom correspondence shouldbe addressed. E-mail: joel.mossong@lns.etat.lu A B S T R A C T Background Mathematical modelling of infectious diseases transmitted by the respiratory or close-contactroute (e.g., pandemic influenza) is increasingly being used to determine the impact of possibleinterventions. Although mixing patterns are known to be crucial determinants for modeloutcome, researchers often rely on a priori contact assumptions with little or no empirical basis.We conducted a population-based prospective survey of mixing patterns in eight Europeancountries using a common paper-diary methodology. Methods and Findings 7,290 participants recorded characteristics of 97,904 contacts with different individualsduring one day, including age, sex, location, duration, frequency, and occurrence of physicalcontact. We found that mixing patterns and contact characteristics were remarkably similaracross different European countries. Contact patterns were highly assortative with age:schoolchildren and young adults in particular tended to mix with people of the same age.Contacts lasting at least one hour or occurring on a daily basis mostly involved physicalcontact, while short duration and infrequent contacts tended to be nonphysical. Contacts athome, school, or leisure were more likely to be physical than contacts at the workplace or whiletravelling. Preliminary modelling indicates that 5- to 19-year-olds are expected to suffer thehighest incidence during the initial epidemic phase of an emerging infection transmittedthrough social contacts measured here when the population is completely susceptible. Conclusions To our knowledge, our study provides the first large-scale quantitative approach to contactpatterns relevant for infections transmitted by the respiratory or close-contact route, and theresults should lead to improved parameterisation of mathematical models used to designcontrol strategies. The Editors’ Summary of this article follows the references. PLoS Medicine | www.plosmedicine.org March 2008 | Volume 5 | Issue 3 | e740381 PLoSMEDICINE (WoK: 447 citations) Infectious disease epidemiology: slowly moving towards open science 15/24 Infectious Disease Epidemiology & Statistics Infectious Disease Epidemiology Social Contact Survey 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: non-close and close contacts Total of 12775 contacts (≈ 16 contacts per person per day) Hens et al. (2009a,b) Infectious disease epidemiology: slowly moving towards open science 16/24 Infectious Disease Epidemiology & Statistics Infectious Disease Epidemiology Mixing patterns in EU Infectious disease epidemiology: slowly moving towards open science 17/24 Infectious Disease Epidemiology & Statistics Infectious Disease Epidemiology Mixing patterns in EU Data were shared, via research gate, the VENICE platform etc Data were misused because they were made available because researcher did not check the supplementary material ‘supplementary’? . . . ? Examples: hepatitis A impact of weather on social contact behaviour reciprocity Infectious disease epidemiology: slowly moving towards open science 18/24 Infectious Disease Epidemiology & Statistics Infectious Disease Epidemiology Mixing patterns in EU Infectious disease epidemiology: slowly moving towards open science 19/24 Infectious Disease Epidemiology & Statistics Infectious Disease Epidemiology Mixing patterns in EU Infectious disease epidemiology: slowly moving towards open science 20/24 Infectious Disease Epidemiology & Statistics Infectious Disease Epidemiology Mixing patterns in EU Infectious disease epidemiology: slowly moving towards open science 21/24 Infectious Disease Epidemiology & Statistics Infectious Disease Epidemiology Mixing patterns in EU Infectious disease epidemiology: slowly moving towards open science 22/24 Discussion & Recommendations Discussion & Recommendations The Good, the Bad and the Ugly: → trade-off: sharing raw data requires documenting the data and sharing code → worthwhile the effort There is hope: moving towards a unifying format for outbreaks (FF100, UK, 2009) Much more work needs to be done though! Infectious disease epidemiology: slowly moving towards open science 23/24 Discussion & Recommendations Infectious disease epidemiology: slowly moving towards open science 24/24 Discussion & Recommendations References 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.