Statistical inference with mechanistic models on heterogeneous data: improving the control of infectious diseases

Lead Research Organisation: London Sch of Hygiene and Trop Medicine
Department Name: Epidemiology and Population Health

Abstract

Influenza epidemics occur every year, resulting in large amounts of illness in the community, many early deaths, major disruption to the health services, and significant economic losses. This is despite widespread vaccination. Although the vaccine contains three strains, it is not possible to say ahead of the epidemic which (if any) of the strains will circulate, and how severe will the resultant epidemic be. This means that planning by public health authorities, physicians, and hospitals is difficult, resulting in significant inefficiencies (such as the unnecessary cancelling elective surgeries, etc).

The size of an influenza epidemic is governed, amongst other things, by the level of immunity in the population (it is for this reason that pandemics are so feared, as the novel virus tends to be very different from existing strains, and so the level of immunity in the population is low). The emergence of a novel H1N1 (swine flu) virus in 2009 has been closely monitored and studied. The UK has one of the best influenza surveillance systems in the world, and has amassed a great deal of data on the spread, severity, and population immunity to this virus. Despite this, the virus has surprised public health officials and mathematical modellers alike, as a significant epidemic was observed during the winter of 2010/11 despite apparently high levels of population immunity. What may happen in the coming years is equally unknown. The emergence of this virus and the wealth of data available provide an unique opportunity to better understand the dynamics of a new influenza virus following its introduction into the human population. We intend to develop and test a number of different mathematical models to build a better picture of the dynamics and evolution of influenza in the population. The models will be fitted to the range of epidemiological data using state-of-the art statistical techniques, which will have general applicability within the fields of infectious disease dynamics and statistical inference. The statistical framework will shed light on the effective level of protection in the population against subsequent drifted variants, and pave the way for the next generation of predictive tools. These investigation are critical to improve the effectiveness of public health measures, like vaccination, and determine which data should be prioritised to help make predictive models of seasonal and pandemic influenza.

This multi-disciplinary project involves many different stakeholders, including the bodies that are collecting the data, experts in disease transmission and host-pathogen interactions, mathematical modellers who formalize biological mechanisms, statisticians who develop rigorous and robust methods to confront models to data, and finally, public health experts who ask the questions that the model must address. It is envisaged that the project will help improve public health policy in this high-profile area, develop new methods for fitting models to data, and provide an ideal training ground for the lead applicant to become an established leader in mathematical epidemiology.

Technical Summary

Understanding what governs the size and severity of seasonal influenza epidemics is a key public health priority. This can be obtained by fitting mechanistic models to the many different and varied surveillance data available in the UK to draw inference on key epidemiological drivers. However, the non-linear and stochastic nature of epidemiological processes pose serious challenges to classical statistical methods.

To address these issues we will consider three classes of algorithms that have recently been developed to perform inference on partially observed Markov processes: Approximate Bayesian Computation [Toni et al. 2009 J R Soc Interface], Particle Markov Chain Monte Carlo [Andrieu et al. 2010 J R Stat Soc B] and Maximum Likelihood via Iterated Filtering [Ionides et al. 2006 PNAS]. An attractive feature of these new methodologies is that the only operation applied to the underlying Markov process model is the generation of a draw from the transition density.

The accuracy, precision, robustness and practical use of these different methods on simulated data sets will be compared in order to select the most appropriate one to analyse the uniquely rich and detailed influenza data available in the UK. These include serological, virological, immunological, epidemiological and sociological data available at both the individual and population level over many years.
These data will be combined and linked to the statistical models through observation processes accounting for noise, overdispersion and biases (e.g. negative-binomial). These heterogeneous data will be exploited in a quantitative and rigorous manner to test alternative hypotheses on the mechanisms underlying disease spread.

In the medium term this will facilitate accurate near-to-real-time predictions on the severity and extent of a given epidemic of influenza. In addition, it will help design more efficient and cost-effective vaccination policies.

Planned Impact

Influenza remains one of the most serious public health threats, with as many as 10,000 deaths annually attributed to influenza in the UK, despite the existence of widespread vaccination programmes. The threat of a pandemic causing large-scale morbidity and mortality has not receded, despite the emergence of nH1N1 in 2009. Understanding what drives the emergence, spread and evolution of seasonal influenza, and how pandemic strains become established in the population are therefore key public health questions. The surprising spread of H1N1 during the winter of 2010/11 despite apparently high levels of pre-existing immunity, and the resultant impact on deaths, and primary and secondary care demonstrates our lack of a good quantitative understanding of seasonal influenza dynamics.

Public health officials, general practitioners and acute care trusts are therefore expected to be amongst the major beneficiaries of this project. The methods developed will help determine how immunity is built up within individuals, how this maps to population level immunity, and how this is passed from one year to the next. In the medium term this may facilitate accurate near-to-real-time predictions on the severity and extent of a given epidemic of influenza. In addition, by building statistically rigorous mechanistic models of influenza that take account of the dynamics of immunity, we will help design more efficient and cost-effective vaccination policies. Improved vaccine decision-making in the new post-pandemic era, are expected within the time-frame of the fellowship. It is also envisaged that this work may help design more efficient surveillance systems by demonstrating which aspects (e.g. serology) are most important for helping to understand and predict the spread of influenza. In addition, the results will be communicated to national and international scientific advisory bodies, such as the JCVI and Scientific Pandemic Influenza Advisory Group (SPI). This will be facilitated through Professor Edmunds, who regularly advises these bodies and is a member of SPI and SPI-M (its modelling subgroup), as well as WHO's QUIVER (Quantitative Immunisation and Vaccine Related Research Committee).

Publications

10 25 50
 
Description Introduction to epidemic modelling with the SSM package: Ebola as a case study.
Geographic Reach Local/Municipal/Regional 
Policy Influence Type Influenced training of practitioners or researchers
Impact Delivering a 1 day tutorial that we have set up with a colleague (Joseph Dureau) on cutting-edge inference tools for fitting mathematical models to time-series data.
 
Description Invitation as an expert at several SAGE working group meetings on Ebola vaccination.
Geographic Reach Africa 
Policy Influence Type Participation in a advisory committee
 
Description Short course on Model fitting and inference for infectious disease dynamics
Geographic Reach Multiple continents/international 
Policy Influence Type Influenced training of practitioners or researchers
URL http://www.lshtm.ac.uk/study/cpd/infectious_disease_modelling.html
 
Description change vaccine policy
Geographic Reach National 
Policy Influence Type Participation in advisory committee
 
Description genepi workshop
Geographic Reach National 
Policy Influence Type Influenced training of practitioners or researchers
 
Title Member of the StateSpaceModel github organisation 
Description SSM is a library of inference for time series analysis with State Space Models, like playing with duplo blocks. 
Type Of Material Improvements to research infrastructure 
Year Produced 2014 
Provided To Others? Yes  
Impact I use this library on a daily basis for fitting my models on heterogeneous data. I provided a seminar to Imperial College London on how to use this tool. 
URL https://github.com/StateSpaceModels/ssm
 
Title R package Flusurvey 
Description R package dedicated to the compilation, cleaning and analysis of the data collected through the internet-based cohort survey "FluSurvey". FluSurvey has been running in the UK since 2009 and similar surveys have been conducted in Europe as part of the Epiwork program. The R package is currently designed for the UK database and will be extended for compatibility with other countries/databases. 
Type Of Material Improvements to research infrastructure 
Provided To Others? No  
Impact Estimation, for the fisrt time, of the quality adjusted life impact associated with acute respiratory infection and influenza-like illness. 
URL https://flusurvey.org.uk/
 
Title R package SSMinR 
Description SSMinR is a R package that provides an interface to SSM: a C library to perform inference for time series analysis with State Space Models, like playing with duplo blocks. 
Type Of Material Improvements to research infrastructure 
Year Produced 2015 
Provided To Others? Yes  
Impact Simplifying the use of SSM to other researchers. Presented during a 1-day totorial at Imperial College, London. 
URL https://github.com/ntncmch/SSMinR
 
Title R package outbreaktools 
Description Epibase is a R package providing basic tools for the analysis of disease outbreaks. Its main features lie in handling possibly very different types of data within a coherent framework, represented by the class obkData (for "outbreak data"). This class allows to store and manipulate data on samples, in- dividuals, records interventions, genetic sequences, phylogenetic trees and contact networks, most of this information being time-stamped and possibly geo-referenced. 
Type Of Material Improvements to research infrastructure 
Year Produced 2013 
Provided To Others? Yes  
Impact not yet 
URL http://cran.r-project.org/web/packages/OutbreakTools/index.html
 
Title R package: abc.star 
Description The abc.star R package implements ABC calibration routines for the most commonly occurring scenarios. The corresponding paper is now on arxiv http://arxiv.org/abs/1305.4283 
Type Of Material Improvements to research infrastructure 
Year Produced 2015 
Provided To Others? Yes  
Impact Improve the accuracy of ABC techniques, widely used in computational biology. 
URL https://github.com/olli0601/abc.star
 
Description Modelling the dynamics of influenza and other respiratory diseases in Japan 
Organisation Nagasaki University
Country Japan 
Sector Academic/University 
PI Contribution Trained staff from Nagasaki University to several statistical techniques to analyse their data. Gave a lecture on mathematical modelling of influenza. Initiated a modelling project to unravelling the transmission and dynamics of influenza and other respiratory diseases among the population of Kamigoto island. Initiated a modelling project to analyse the dynamics of influenza at the level of Japan, using routine surveillance data.
Collaborator Contribution Collect and clean all the data necessary for the two modelling projects. Facilitate networking and data access with Japanese authorities.
Impact doi: 10.1017/S0950268814000107
Start Year 2014
 
Description Prediction of cholera dynamics in Haiti following the passage of Hurricane Matthew. 
Organisation Médecins Sans Frontières (MSF)
Department Epicentre - Médecins Sans Frontières (MSF Epicentre)
Country France 
Sector Academic/University 
PI Contribution The aim of this study is to provide additional information to health actors responding to the post-hurricane cholera outbreak in Haiti. To this end, we calibrated a mechanistic model of cholera transmission on currently available data for Haiti in order to forecast the spatio-temporal dynamics of the cholera epidemic at the departmental level from November 2016 to January 2017. Model outputs have been translated into operational recommendations, with a focus on the scheduled OCV campaign.
Collaborator Contribution EPFL designed the model and performed the analysis. Epicentre produced the report and recommendations. Both teams interpreted model results.
Impact This collaboration is multi-disciplinary, it involved epidemiologist at Epicentre/MSF and modellers at Ecole Polytechnique de Lausanne. The main output was an online dashboard (url above) as well as PDFs reports sent regularly to the actors of the response in Haiti.
Start Year 2016
 
Description Prediction of cholera dynamics in Haiti following the passage of Hurricane Matthew. 
Organisation Swiss Federal Institute of Technology in Lausanne (EPFL)
Department Laboratory of Ecohydrology
Country Switzerland 
Sector Academic/University 
PI Contribution The aim of this study is to provide additional information to health actors responding to the post-hurricane cholera outbreak in Haiti. To this end, we calibrated a mechanistic model of cholera transmission on currently available data for Haiti in order to forecast the spatio-temporal dynamics of the cholera epidemic at the departmental level from November 2016 to January 2017. Model outputs have been translated into operational recommendations, with a focus on the scheduled OCV campaign.
Collaborator Contribution EPFL designed the model and performed the analysis. Epicentre produced the report and recommendations. Both teams interpreted model results.
Impact This collaboration is multi-disciplinary, it involved epidemiologist at Epicentre/MSF and modellers at Ecole Polytechnique de Lausanne. The main output was an online dashboard (url above) as well as PDFs reports sent regularly to the actors of the response in Haiti.
Start Year 2016
 
Description Genepi workshop 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Type Of Presentation Workshop Facilitator
Geographic Reach National
Primary Audience Other academic audiences (collaborators, peers etc.)
Results and Impact I organized a one-day workshop bringing together researchers at the interface of epidemiology, genetics and modelling.

Participants learned how infectious disease epidemiology uses genetic data to understand host-to-host transmission, community outbreaks and evolution of pathogens.
Year(s) Of Engagement Activity 2013
URL https://sites.google.com/site/genepiday/
 
Description Young Statistical Society Funding Workshop 
Form Of Engagement Activity A formal working group, expert panel or dialogue
Part Of Official Scheme? Yes
Geographic Reach National
Primary Audience Postgraduate students
Results and Impact talk sparked questions and discussion afterwards

Not any notable
Year(s) Of Engagement Activity 2014
URL http://statsyss.wordpress.com/2014/03/30/announcement-yss-funding-workshop-2014-on-30-may-2014/