Epidemiological parameters from transmission experiments: new methods for old data Simon Gubbins, David Schley & Ben Hu Transmission Biology Group The.

Slides:

Advertisements

Similar presentations

Modelling Healthcare Associated Infections: A case study in MRSA.

Advertisements

R 0 and other reproduction numbers for households models MRC Centre for Outbreak analysis and modelling, Department of Infectious Disease Epidemiology.

Bayesian dynamic modeling of latent trait distributions Duke University Machine Learning Group Presented by Kai Ni Jan. 25, 2007 Paper by David B. Dunson,

1 Workshop on the immunological basis of vaccine efficacy Vaccine and Infectious Disease Institute December 14, 2009 Ira M. Longini, Jr. Center for Statistical.

Disease Dynamics in a Dynamic Social Network Claire Christensen 1, István Albert 3, Bryan Grenfell 2, and Réka Albert 1,2 Bryan Grenfell 2, and Réka Albert.

Modelling – progress update Stephen Catterall, BioSS 28 th November 2007.

In biology – Dynamics of Malaria Spread Background –Malaria is a tropical infections disease which menaces more people in the world than any other disease.

Analysis to Inform Decisions: Evaluating BSE Joshua Cohen and George Gray Harvard Center for Risk Analysis Harvard School of Public Health.

The Importance of Detail: Sensitivity of Household Secondary Attack Rate and Intervention Efficacy to Household Contact Structure A. Marathe, B. Lewis,

Dengue Transfusion Risk Model Lyle R. Petersen, MD, MPH Brad Biggerstaff, PhD Division of Vector-Borne Diseases Centers for Disease Control and Prevention.

Statistical inference for epidemics on networks PD O’Neill, T Kypraios (Mathematical Sciences, University of Nottingham) Sep 2011 ICMS, Edinburgh.

The construction and analysis of epidemic trees with reference to the 2001 UK FMDV outbreak Dan Haydon, Dept Zoology, University of Guelph, On. Ca.

CSI Uncertainty in A.I. Lecture Three Main Approaches To Approximate Inference MCMC Variational Methods Loopy belief propagation.

Avian Influenza - Pandemic Threat ? Reinhard Bornemann.

Approximate Bayesian Methods in Genetic Data Analysis Mark A. Beaumont, University of Reading,

Classical and Bayesian analyses of transmission experiments Jantien Backer and Thomas Hagenaars Epidemiology, Crisis management & Diagnostics Central Veterinary.

PEPA is based at the IFS and CEMMAP © Institute for Fiscal Studies Identifying social effects from policy experiments Arun Advani (UCL & IFS) and Bansi.

Infectious Disease Epidemiology Sharyn Orton, Ph.D. American Red Cross, Rockville, MD Suggested reading: Modern Infectious Disease Epidemiology (1994)

Modelling the Spread of Infectious Diseases Raymond Flood Gresham Professor of Geometry.

A Provocation: Social insects as an experimental model of network epidemiology Michael Otterstatter (CA)

Crystal Linkletter and Derek Bingham Department of Statistics and Actuarial Science Simon Fraser University Acknowledgements This research was initiated.

Incorporating heterogeneity in meta-analyses: A case study Liz Stojanovski University of Newcastle Presentation at IBS Taupo, New Zealand, 2009.

This presentation is made available through a Creative Commons Attribution- Noncommercial license. Details of the license and permitted uses are available.

Learning Geographical Preferences for Point-of-Interest Recommendation Author(s): Bin Liu Yanjie Fu, Zijun Yao, Hui Xiong [KDD-2013]

A Stochastic Model of Paratuberculosis Infection In Scottish Dairy Cattle I.J.McKendrick 1, J.C.Wood 1, M.R.Hutchings 2, A.Greig 2 1. Biomathematics &

Stefan Ma1, Marc Lipsitch2 1Epidemiology & Disease Control Division

Statistical approach Statistical post-processing of LPJ output Analyse trends in global annual mean NPP based on outputs from 19 runs of the LPJ model.

This presentation is made available through a Creative Commons Attribution- Noncommercial license. Details of the license and permitted uses are available.

Responding To An Infection Transmission Emergency Jim Koopman MD MPH University of Michigan Center for the Study of Complex Systems & Dept. of Epidemiology.

AUSTRALIA INDONESIA PARTNERSHIP FOR EMERGING INFECTIOUS DISEASES Basic Field Epidemiology Session 6 – How disease progresses.

Conceptual Addition of Adherence to a Markov Model In the adherence-naïve model, medication adherence and associated effectiveness assumed to be trial.

Risk Assessment: Questions to the Committee 1.What estimate(s) should be used to reflect the prevalence of vCJD in the U.K.? Proposal: We propose using.

1 Edward Broughton, PhD., MPH Director of Research and Evaluation, USAID Health Care Improvement Project, University Research Co., LLC

 Occupancy Model Extensions. Number of Patches or Sample Units Unknown, Single Season So far have assumed the number of sampling units in the population.

A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation Yee W. Teh, David Newman and Max Welling Published on NIPS 2006 Discussion.

EPIDEMIOLOGY OF INFNT DENGUE CASES ILLUMINATES SEROTYPE- SPECIFICITY IN THE INTERACTION BETWEEN IMMUNITY AND DISEASE AND CHANGES IN TRANSMISSION DYNAMICS.

C-Reactive Protein & Cognitive Function

Conflict of Interest I have acted as a Consultant on an education workshop organised by Gilead Sciences.

Jillian Gauld Institute for Disease Modeling April 20, 2017

Science in School  Issue 40: Summer 2017 

PREVALENCE OF HEPATITIS B VIRUS INFECTION AMONG FEDERAL

Bayesian data analysis

Ruth Doherty, Edinburgh University Adam Butler & Glenn Marion, BioSS

Dr.MUSTAQUE AHMED MBBS,MD(COMMUNITY MEDICINE), FELLOWSHIP IN HIV/AIDS

RESULTS AND DISCUSSION

Observational Studies and Experiments

Middle East respiratory syndrome coronavirus: quantification of the extent of the epidemic, surveillance biases, and transmissibility Simon Cauchemez,

Variable Selection for Gaussian Process Models in Computer Experiments

Modelling infectious diseases

Efficacy of a Foot-and-Mouth Disease inactivated vaccine (AFTOVAXPUR DOE), administered at a 1 mL dose to sheep Claude Hamers.

Chapter 6 Hypothesis tests.

A Non-Parametric Bayesian Method for Inferring Hidden Causes

STA 216 Generalized Linear Models

National estimation of the prevalence of problem drug use: revisiting and extending multiplier methods in a multi-parameter evidence synthesis framework.

Demonstration of early protection against foot-and-mouth disease virus

Epidemiological Modeling to Guide Efficacy Study Design Evaluating Vaccines to Prevent Emerging Diseases An Vandebosch, PhD Joint Statistical meetings,

Horizontal transmissibility of the foot-and-mouth disease virus O/JPN/2010 among different species of animals K. Fukai, T. Nishi, N. Shimada, K. Morioka,

CpG oligodeoxynucleotides allow for effective adoptive T-cell therapy in chronic retroviral infection by Anke R. M. Kraft, Frank Krux, Simone Schimmer,

DEVELOPMENT OF A NOVEL VNT ASSAY USING QRT-PCR-BASED ENDPOINT ASSESSMENT FOR RAPID DETECTION AND TITRATION OF NEUTRALIZING ANTIBODIES AGAINST FMDV.

ICAR-Directorate of Foot-and-mouth disease, Mukteswar, India

Science in School  Issue 40: Summer 2017 

A Web-based Interactive Genome Library for Surveillance, Detection, Characterization and Drug-Resistance Monitoring of Influenza Virus Infection in the.

Nucleotide sequences encoding part of the VP1 capsid protein of FMDVs derived from infected cattle in phase I and phase II of the study. Nucleotide sequences.

The construction and analysis of epidemic trees with reference to the 2001 UK FMDV outbreak Dan Haydon, Dept Zoology, University of Guelph, On. Ca.

A. Papa, K. Dumaidi, F. Franzidou, A. Antoniadis

Department of Biotechnology University of Malakand

Susceptible, Infected, Recovered: the SIR Model of an Epidemic

Middle East respiratory syndrome coronavirus: quantification of the extent of the epidemic, surveillance biases, and transmissibility Simon Cauchemez,

CS639: Data Management for Data Science

FMDV infection dynamics in cattle in phase II of the study.

Presentation transcript:

Epidemiological parameters from transmission experiments: new methods for old data Simon Gubbins, David Schley & Ben Hu Transmission Biology Group The Pirbright Institute

Background Transmission experiments are commonly used in foot-and-mouth disease research They are used to estimate: transmission rates basic reproduction number (R0) latent, infectious and incubation periods vaccine effectiveness

Experimental design Most transmission experiments follow a similar design ... C1 inoculate a number of donors C1 C2 introduce a number of naïve recipients C1 C2 observe the outcome: clinical virological immunological

Features of the experiments We don’t directly observe what we’re interested in! infection times latent periods infectious periods (typically rely on proxy measures) Most commonly used methods for analysing transmission experiments (final size; generalized linear model) have to make assumptions to overcome these features

Bayesian methods: a better approach? Using Bayesian methods allows us to avoid most assumptions Allows us to draw inferences about unobserved processes (data augmentation): infection times latent and infectious periods Allows us to incorporate data from previous experiments (priors)

Example 1: FMDV in lambs Follows the generic experimental design parameter previous Bayes R0 1.14 (0.3, 3.3) 1.45 (0.33, 3.08) mean latent period (days) inoculated - 1.12 (0.68, 1.68) contact 1.50 (0.16, 2.84) mean infectious period (days) 21.1 (10.6, 42.1) 15.4 (11.0, 21.4) Data from Orsel et al. (2007) Vaccine 25, 2673-2679

Example 2: FMDV in pigs Two experimental designs results analysed together Data from Orsel et al. (2007) Vaccine 25, 6381-6391

Example 2 (ctd): FMDV in pigs parameter previous Bayes R0 ∞ 8.54 (4.41, 14.9) transmission rate 6.84 (3.17, 14.8) 1.51 (0.76, 2.55) mean latent period (days) inoculated - 0.97 (0.40, 1.67) contact 0.14 (0.01, 0.33) mean infectious period (days) 4.74 (3.83, 5.86)

Example 2 (ctd): FMDV in pigs Vaccination significantly reduces R0, but not to below 1 previous analyses could not identify a significant effect of vaccination

When is an animal infectious? This is critical to inferring transmission dynamics Often inferred from proxy measures detection of virus in blood, probang, nasal swabs ... Can we infer infectiousness directly? and, hence, identify a robust proxy measure

Experimental design Day 0 Day 2 Day 4 Day 6 Day 8 Virological data: blood, nasal swabs, probang Clinical signs Transmission Data from Charleston et al. (2011) Science 332, 726-729

Quantifying infectiousness We analyse the data assuming infectiousness changes continuously over time cf. latent and infectious periods The approach also links infectiousness and onset of clinical signs allows for individual variation in infectiousness Implemented in a Bayesian framework

Does this matter? Choice of proxy measure influences conclusions about: basic reproduction number generation time effectiveness of reactive control measures These effects scale up to the herd level

Conclusions Bayesian methods facilitate analysis of transmission experiments reduce the number of assumptions to be made obtain estimates where classical methods fail Generate insights into transmission processes dynamics of infectiousness who infects whom Quantification of uncertainty in epidemiological parameters essential when incorporating estimates in regional scale models of spread and control

Acknowledgements Everyone whose data we’ve stolen José Gonzáles (WBR Lelystad) Bryan Charleston (Pirbright) Mark Woolhouse (Edinburgh) Mike Tildesley (Warwick) Leon Danon (Bristol)