Towards an open-source, unified platform for disease outbreak analysis using
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1 Towards an open-source, unified platform for disease outbreak analysis using XXIV Simposio Internacional De Estadística Bogotá 24-26th July 2014 Thibaut Jombart, Caitlin Collins, Anne Cori, Neil Ferguson MRC Centre for Outbreak Analysis and Modelling Imperial College London
2 Outline Introduction Implementing cutting-edge methodology Laying the basis for epidemics analysis in R Towards a new, unified platform for epidemics analysis Conclusion T. Jombart et al, Imperial College London Tools for outbreak analysis 1/34
3 Outline Introduction Implementing cutting-edge methodology Laying the basis for epidemics analysis in R Towards a new, unified platform for epidemics analysis Conclusion T. Jombart et al, Imperial College London Tools for outbreak analysis 2/34
4 Infectious disease epidemiology Infectious diseases caused by pathogens (e.g. viruses, bacteria) infection from the environment or between-host transmissions examples: influenza, SARS, HIV, plague, malaria, dengue, nosocomial infections outbreak/epidemic: increased number of cases T. Jombart et al, Imperial College London Tools for outbreak analysis 3/34
5 Infectious disease epidemiology Infectious diseases caused by pathogens (e.g. viruses, bacteria) infection from the environment or between-host transmissions examples: influenza, SARS, HIV, plague, malaria, dengue, nosocomial infections outbreak/epidemic: increased number of cases Infectious Disease Epidemiology (IDE): some core objectives understand the infectious process uncover/predict the spatio-temporal dynamics of infectious diseases inform Public Health policies T. Jombart et al, Imperial College London Tools for outbreak analysis 3/34
6 Infectious disease epidemiology is changing Fraser et al., Science, 2009 T. Jombart et al, Imperial College London Tools for outbreak analysis 4/34
7 Infectious disease epidemiology is changing Fraser et al., Science, 2009 Increased risks, but increased means of research. T. Jombart et al, Imperial College London Tools for outbreak analysis 4/34
8 Challenges and opportunities Increased risks populations are increasingly connected (air travel) population densities increase intensive agriculture makes animal reservoirs more dangerous antibiotic resistance increasing T. Jombart et al, Imperial College London Tools for outbreak analysis 5/34
9 Challenges and opportunities Increased risks populations are increasingly connected (air travel) population densities increase intensive agriculture makes animal reservoirs more dangerous antibiotic resistance increasing Increased means revolution caused by next-generation sequencing refined statistical methods/models improved surveillance systems T. Jombart et al, Imperial College London Tools for outbreak analysis 5/34
10 Challenges and opportunities Increased risks populations are increasingly connected (air travel) population densities increase intensive agriculture makes animal reservoirs more dangerous antibiotic resistance increasing Increased means revolution caused by next-generation sequencing refined statistical methods/models improved surveillance systems New data, new methods, new surveillance systems: how can help stitch things together? T. Jombart et al, Imperial College London Tools for outbreak analysis 5/34
11 Outline Introduction Implementing cutting-edge methodology Laying the basis for epidemics analysis in R Towards a new, unified platform for epidemics analysis Conclusion T. Jombart et al, Imperial College London Tools for outbreak analysis 6/34
12 Estimating time-varying reproduction numbers Reproduction number (R) central concept in IDE different flavours, but roughly: number of new infections created by each infected used for: final size prediction, useful vaccination effort, etc. assessing its variation in time is essential T. Jombart et al, Imperial College London Tools for outbreak analysis 7/34
13 Estimating time-varying reproduction numbers Reproduction number (R) central concept in IDE different flavours, but roughly: number of new infections created by each infected used for: final size prediction, useful vaccination effort, etc. assessing its variation in time is essential Problem: traditional reproduction numbers are only retrospective. T. Jombart et al, Imperial College London Tools for outbreak analysis 7/34
14 The package EpiEstim Instantaneous reproduction number (R t ) (Cori et al, Am J Epidemiol, 2013) R t can vary over time R t predicts new infections based on current data short-term predictions possible pure- package EpiEstim with shiny application T. Jombart et al, Imperial College London Tools for outbreak analysis 8/34
15 Reconstructing disease outbreaks: who infected whom? Not necessarily an easy task. T. Jombart et al, Imperial College London Tools for outbreak analysis 9/34
16 Reconstructing disease outbreaks: who infected whom? Not necessarily an easy task. T. Jombart et al, Imperial College London Tools for outbreak analysis 9/34
17 Reconstructing disease outbreaks: who infected whom? Not necessarily an easy task. Things can get difficult symptom onset: not enough to infer transmissions contact information: difficult to get and unsufficient many trees to explore: 5 cases = 120 trees, 10 cases = 3,628,800 trees, 100 cases (there are around atoms in the universe) T. Jombart et al, Imperial College London Tools for outbreak analysis 9/34
18 Using genetic and epidemiological data to reconstruct disease outbreaks How to infer the infector of a given individual? T. Jombart et al, Imperial College London Tools for outbreak analysis 10/34
19 Using genetic and epidemiological data to reconstruct disease outbreaks How to infer the infector of a given individual? T. Jombart et al, Imperial College London Tools for outbreak analysis 10/34
20 Using genetic and epidemiological data to reconstruct disease outbreaks Approach based on generation time distribution (Ferguson et al. 2001, Nature; Wallinga & Teunis 2004, Am. J. Epidemiol.) T. Jombart et al, Imperial College London Tools for outbreak analysis 10/34
21 Using genetic and epidemiological data to reconstruct disease outbreaks Approach based on generation time distribution (Wallinga & Teunis 2004, Am. J. Epidemiol.) T. Jombart et al, Imperial College London Tools for outbreak analysis 10/34
22 Using genetic and epidemiological data to reconstruct disease outbreaks Approach based on genetic data (SeqTrack) (Jombart et al. 2010, Heredity.) T. Jombart et al, Imperial College London Tools for outbreak analysis 10/34
23 Using genetic and epidemiological data to reconstruct disease outbreaks Approach based on genetic data (SeqTrack) (Jombart et al. 2010, Heredity.) T. Jombart et al, Imperial College London Tools for outbreak analysis 10/34
24 Using genetic and epidemiological data to reconstruct disease outbreaks outbreaker: use generation time distribution and genetic data: T. Jombart et al, Imperial College London Tools for outbreak analysis 10/34
25 Using genetic and epidemiological data to reconstruct disease outbreaks outbreaker: use generation time distribution and genetic data: T. Jombart et al, Imperial College London Tools for outbreak analysis 10/34
26 The method, in a nutshell Framework (Jombart et al, PLoS Comp Biol, 2014) Bayesian framework augmented data for ancestries, infection dates, unobserved cases MCMC (Metropolis-Hasting) for sampling from posterior T. Jombart et al, Imperial College London Tools for outbreak analysis 11/34
27 The method, in a nutshell Framework (Jombart et al, PLoS Comp Biol, 2014) Bayesian framework augmented data for ancestries, infection dates, unobserved cases MCMC (Metropolis-Hasting) for sampling from posterior General likelihood p(transmission tree) = i p(branch i) p(branch) = p(infection date) p(collection date) p(unobserved cases) p(genetic differences) T. Jombart et al, Imperial College London Tools for outbreak analysis 11/34
28 The package Content implement model and simulation tool R interface, C engine supports parallelization (chain-wise) developped on Sourceforge, distributed on CRAN T. Jombart et al, Imperial College London Tools for outbreak analysis 12/34
29 Implementing cutting-edge methodology: summary ideal for implementing new method for IDE interfacing C/C++ code essential for complex models (Bayesian methods) parallelization becomes increasingly important limit: not all models can be generalized T. Jombart et al, Imperial College London Tools for outbreak analysis 13/34
30 Outline Introduction Implementing cutting-edge methodology Laying the basis for epidemics analysis in R Towards a new, unified platform for epidemics analysis Conclusion T. Jombart et al, Imperial College London Tools for outbreak analysis 14/34
31 Case study 1: analysis of SARS outbreaks Once upon a time, data were (fairly) simple. Data symptom onset generation time distribution T. Jombart et al, Imperial College London Tools for outbreak analysis 15/34
32 Case study 2: outbreak of avian influenza (H7N7) But then, things got complicated. Data dates of infection dates of culling geographic locations DNA sequences T. Jombart et al, Imperial College London Tools for outbreak analysis 16/34
33 Case study 3: same outbreak, more data Complicated, indeed. Data dates of infection dates of culling geographic locations DNA sequences hourly measurements of wind speed/direction (!) T. Jombart et al, Imperial College London Tools for outbreak analysis 17/34
34 Things are not getting any simpler Outbreak data are diverse and complex: patient information, observations (e.g. swabs, symptoms,...), geographic locations, contacts between patients, DNA sequences,... T. Jombart et al, Imperial College London Tools for outbreak analysis 18/34
35 Things are not getting any simpler Outbreak data are diverse and complex: patient information, observations (e.g. swabs, symptoms,...), geographic locations, contacts between patients, DNA sequences,... Models and analysis tools are increasingly integrating various streams of data. T. Jombart et al, Imperial College London Tools for outbreak analysis 18/34
36 Things are not getting any simpler Outbreak data are diverse and complex: patient information, observations (e.g. swabs, symptoms,...), geographic locations, contacts between patients, DNA sequences,... Models and analysis tools are increasingly integrating various streams of data. Problem: basic tools for storing, handling, and visualizing such data are lacking. T. Jombart et al, Imperial College London Tools for outbreak analysis 18/34
37 Hackout: a hackathon for the analysis of disease outbreaks in Where-when-who-what? Imperial College, London, 9-11th January 2013 funded by MRC Centre for Outbreak Analysis & Modelling 22 participants (UK, France, Holland, Belgium, USA) T. Jombart et al, Imperial College London Tools for outbreak analysis 19/34
38 Hackout: a hackathon for the analysis of disease outbreaks in Where-when-who-what? Imperial College, London, 9-11th January 2013 funded by MRC Centre for Outbreak Analysis & Modelling 22 participants (UK, France, Holland, Belgium, USA) Purpose: develop basic tools for outbreak data analysis T. Jombart et al, Imperial College London Tools for outbreak analysis 19/34
39 Why and where do hackathons go wrong? It all started quite well... T. Jombart et al, Imperial College London Tools for outbreak analysis 20/34
40 Why and where do hackathons go wrong? People were thinking (you can tell by the posture) T. Jombart et al, Imperial College London Tools for outbreak analysis 20/34
41 Why and where do hackathons go wrong? Classical thinking posture: the beard stroke T. Jombart et al, Imperial College London Tools for outbreak analysis 20/34
42 Why and where do hackathons go wrong? Note: it works even without a beard T. Jombart et al, Imperial College London Tools for outbreak analysis 20/34
43 Why and where do hackathons go wrong? Here is another popular variant T. Jombart et al, Imperial College London Tools for outbreak analysis 20/34
44 Why and where do hackathons go wrong? Then we split into groups people work... T. Jombart et al, Imperial College London Tools for outbreak analysis 20/34
45 Why and where do hackathons go wrong?...well, most of them anyway. Some start relaxing... T. Jombart et al, Imperial College London Tools for outbreak analysis 20/34
46 Why and where do hackathons go wrong?...and then it starts going south... T. Jombart et al, Imperial College London Tools for outbreak analysis 20/34
47 Why and where do hackathons go wrong?...and you can forget about the coding. T. Jombart et al, Imperial College London Tools for outbreak analysis 20/34
48 And still: here comes OutbreakTools Features S4 class obkdata: patients data, cases obervations (e.g. symptoms), DNA sequences, static /dynamic contacts, phylogenetic trees and contextual data T. Jombart et al, Imperial College London Tools for outbreak analysis 21/34
49 And still: here comes OutbreakTools Features S4 class obkdata: patients data, cases obervations (e.g. symptoms), DNA sequences, static /dynamic contacts, phylogenetic trees and contextual data ggplot2 graphics: sample timelines, maps, phylogenies and minimum spanning trees T. Jombart et al, Imperial College London Tools for outbreak analysis 21/34
50 And still: here comes OutbreakTools Features S4 class obkdata: patients data, cases obervations (e.g. symptoms), DNA sequences, static /dynamic contacts, phylogenetic trees and contextual data ggplot2 graphics: sample timelines, maps, phylogenies and minimum spanning trees outbreak simulation: SIR model with epidemic curves, transmission trees, genetic sequences T. Jombart et al, Imperial College London Tools for outbreak analysis 21/34
51 And still: here comes OutbreakTools Features S4 class obkdata: patients data, cases obervations (e.g. symptoms), DNA sequences, static /dynamic contacts, phylogenetic trees and contextual data ggplot2 graphics: sample timelines, maps, phylogenies and minimum spanning trees outbreak simulation: SIR model with epidemic curves, transmission trees, genetic sequences basic analysis tools: data summaries, incidence from symptoms T. Jombart et al, Imperial College London Tools for outbreak analysis 21/34
52 OutbreakTools: current state Resources 49-pages manual 50-pages tutorial: data structure, data handling, graphics, outbreak simulation Website: The -epi project Mailing list: T. Jombart et al, Imperial College London Tools for outbreak analysis 22/34
53 OutbreakTools: current state Resources 49-pages manual 50-pages tutorial: data structure, data handling, graphics, outbreak simulation Website: The -epi project Mailing list: Development is open! hosted on Sourceforge (GIT, ticket system, mailing lists) currently 20 developers Join us: T. Jombart et al, Imperial College London Tools for outbreak analysis 22/34
54 Hackout 2: G.R.I.N.D.E.R. is coming Imperial College, London, February 2015 focussing on graphics more on: T. Jombart et al, Imperial College London Tools for outbreak analysis 23/34
55 Outline Introduction Implementing cutting-edge methodology Laying the basis for epidemics analysis in R Towards a new, unified platform for epidemics analysis Conclusion T. Jombart et al, Imperial College London Tools for outbreak analysis 24/34
56 Introduction New methods Laying the basis A new platform Conclusion is emerging as an environment of choice for infectious disease epidemiology (taken from poster for Epidemics4 ) T. Jombart et al, Imperial College London Tools for outbreak analysis 25/34
57 But is not for everyone is powerful and flexible, but reserved to experts. People like: T. Jombart et al, Imperial College London Tools for outbreak analysis 26/34
58 But is not for everyone is powerful and flexible, but reserved to experts. People like: T. Jombart et al, Imperial College London Tools for outbreak analysis 26/34
59 But is not for everyone is powerful and flexible, but reserved to experts. People like: Need to make these resources more broadly accessible. T. Jombart et al, Imperial College London Tools for outbreak analysis 26/34
60 episerve: a -based web-interface for outbreak analysis Who? main developper: Caitlin Collins collaboration with David Aanensen (Imperial College/Sanger Institute) collaboration Alert & Response Team, World Health Organization T. Jombart et al, Imperial College London Tools for outbreak analysis 27/34
61 episerve: a -based web-interface for outbreak analysis What? Who? tool aimed at Public Health agencies engine, web interface by shiny server deployment via main developper: Caitlin Collins collaboration with David Aanensen (Imperial College/Sanger Institute) collaboration Alert & Response Team, World Health Organization package episerve interfaced with mobile data collection tool epicollect+ embedded Javascript graphics: sigmajs, leaflet, D3 status: working proof of concept; first release scheduled for late 2014 T. Jombart et al, Imperial College London Tools for outbreak analysis 27/34
62 episerve: some examples Visualize data tables: T. Jombart et al, Imperial College London Tools for outbreak analysis 28/34
63 episerve: some examples Timeline of samples per individual: T. Jombart et al, Imperial College London Tools for outbreak analysis 28/34
64 Introduction New methods Laying the basis A new platform Conclusion episerve: some examples Maps of samples/cases using Leaflet: T. Jombart et al, Imperial College London Tools for outbreak analysis 28/34
65 episerve: some examples Dynamic contact networks using SigmaJS: T. Jombart et al, Imperial College London Tools for outbreak analysis 28/34
66 episerve: some examples Estimation of incidence based on symptoms: T. Jombart et al, Imperial College London Tools for outbreak analysis 28/34
67 episerve: some examples Reproduction number estimation (Cori et al s method): T. Jombart et al, Imperial College London Tools for outbreak analysis 28/34
68 Outline Introduction Implementing cutting-edge methodology Laying the basis for epidemics analysis in R Towards a new, unified platform for epidemics analysis Conclusion T. Jombart et al, Imperial College London Tools for outbreak analysis 29/34
69 Summary emerges as a new environment of choice for epidemics analysis T. Jombart et al, Imperial College London Tools for outbreak analysis 30/34
70 Summary emerges as a new environment of choice for epidemics analysis basis for new platform for epidemics analysis T. Jombart et al, Imperial College London Tools for outbreak analysis 30/34
71 Summary emerges as a new environment of choice for epidemics analysis basis for new platform for epidemics analysis challenges remain computational time: complex model still challenging interoperability: limited so far graphics: interface with Javascript libraries (e.g. sigmajs, Leaflet, D3) T. Jombart et al, Imperial College London Tools for outbreak analysis 30/34
72 Research in an emergency context Fraser et al., Science, 2009 T. Jombart et al, Imperial College London Tools for outbreak analysis 31/34
73 Research in an emergency context Fraser et al., Science, 2009 At the time, little use of. T. Jombart et al, Imperial College London Tools for outbreak analysis 31/34
74 New emergency: Middle-East Respiratory Syndrom (MERS) coronavirus T. Jombart et al, Imperial College London Tools for outbreak analysis 32/34
75 New emergency: Middle-East Respiratory Syndrom (MERS) coronavirus involved in about half of the analyses. T. Jombart et al, Imperial College London Tools for outbreak analysis 32/34
76 centralize resources for infectious disease epidemiology (documentation, teaching material,...) contributions from the community: let s join forces! T. Jombart et al, Imperial College London Tools for outbreak analysis 33/34
77 Acknowledgements Liliana López-Kleine & organising committee Imperial College London: Neil Ferguson, Caitlin Collins, Anne Cori, David Aanensen, Maria Van Kerkhove, Christophe Fraser The Hackout Team Pasteur Institute: Simon Cauchemez Funding: MIDAS & MRC Beamer theme: Cedric Mauclair Thanks for your attention. T. Jombart et al, Imperial College London Tools for outbreak analysis 34/34
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