The dynamics and control of infectious diseases close to elimination
Lead Research Organisation:
London School of Hygiene & Tropical Medicine
Department Name: Epidemiology and Population Health
Abstract
Measles is a childhood disease currently estimated to be responsible for over 100,000 annual deaths. Since there is a highly efficient vaccine conferring lifelong immunity, the World Health Organization has declared that measles can and should be eradicated. For a long time, Europe did indeed seem on the way to stopping measles transmission, with a 10-fold reduction in cases between 1998 and 2008. After years of decline, however, measles is on the rise again in Europe. Between 2009 and 2011, cases have increased over 4-fold. In 2011, there were more than 30,000 cases reported in countries of the European Union and European Economic Area, including more than 15,000 in France, more than 5,000 in Italy, and more than 1,000 in each of Germany, Spain and the United Kingdom.
There is a strong history of mathematical models to describe measles dynamics in time and space. At high vaccination levels, however, infectious diseases pose particular challenges, which models have not often been adapted to. Those still susceptible to infection are often far from the reach of public health, or actively refusing to be vaccinated. If they are preferentially in contact with each other (i.e., if there are communities of refusers), there is an increased potential for outbreaks, or even for endemic transmission if there is contact between such communities. At the same time, the patterns of spread in time and space change compared to the pre-vaccination era.
The objective of this research project is to combine a unique set of epidemiological, genetic and behavioural data in mathematical models and develop novel methods for analysing outbreaks, to
1.) explain the dynamics of measles outbreaks at high vaccination coverage,
2.) establish what drives the geographical pattern of cases in Europe,
3.) assess the feasibility of measles elimination in Europe and find the best strategies.
The results will have direct scientific value both through improvement of our understanding of outbreak patterns and the development of novel mathematical models and statistical methods to combine different sources of data. There is also clear social value as the findings will directly translate into recommendations for health policy in the effort to eliminate measles from Europe.
There is a strong history of mathematical models to describe measles dynamics in time and space. At high vaccination levels, however, infectious diseases pose particular challenges, which models have not often been adapted to. Those still susceptible to infection are often far from the reach of public health, or actively refusing to be vaccinated. If they are preferentially in contact with each other (i.e., if there are communities of refusers), there is an increased potential for outbreaks, or even for endemic transmission if there is contact between such communities. At the same time, the patterns of spread in time and space change compared to the pre-vaccination era.
The objective of this research project is to combine a unique set of epidemiological, genetic and behavioural data in mathematical models and develop novel methods for analysing outbreaks, to
1.) explain the dynamics of measles outbreaks at high vaccination coverage,
2.) establish what drives the geographical pattern of cases in Europe,
3.) assess the feasibility of measles elimination in Europe and find the best strategies.
The results will have direct scientific value both through improvement of our understanding of outbreak patterns and the development of novel mathematical models and statistical methods to combine different sources of data. There is also clear social value as the findings will directly translate into recommendations for health policy in the effort to eliminate measles from Europe.
Technical Summary
We are currently lacking methods to analyse infectious disease at high levels of vaccination coverage. In that scenario, assumptions of random mixing break down, and notions such as herd immunity and critical community size need to be reconsidered.
The objective of this project is to develop new statistical methods to analyse infectious diseases at the fringe of elimination. This will be linked to a topic of great current public health importance, the ongoing spread of measles in Europe. More specifically, I will
1) extend existing methods for inferring transmission trees from time series of case data to link outbreaks on a continental scale, taking into account genotype and sequence data and vaccination uptake;
2) extend methods for inferring transmission parameters from outbreak size distributions to take into account distribution of genotypes and sequence data, to map measles risk in different parts of Europe;
3) develop inference methods for fitting network models with different distributions of susceptibility to observed outbreak patterns.
The objective of this project is to develop new statistical methods to analyse infectious diseases at the fringe of elimination. This will be linked to a topic of great current public health importance, the ongoing spread of measles in Europe. More specifically, I will
1) extend existing methods for inferring transmission trees from time series of case data to link outbreaks on a continental scale, taking into account genotype and sequence data and vaccination uptake;
2) extend methods for inferring transmission parameters from outbreak size distributions to take into account distribution of genotypes and sequence data, to map measles risk in different parts of Europe;
3) develop inference methods for fitting network models with different distributions of susceptibility to observed outbreak patterns.
Planned Impact
Since this is a project dedicated to a clear public health question of great current interest (the feasibility of measles elimination in Europe), the main impact will be with policy makers. Both greater understanding of current measles epidemiology in Europe and assessment of different vaccination and outbreak response strategies have the potential to inform health policy in the UK and the rest of Europe.
I have chosen the sponsor of this research, the London School of Hygiene \& Tropical Medicine (LSHTM), specifically with the aim in mind to maximise public health impact. A number of LSHTM staff are involved in both polio and measles eradication, in studies on public trust in vaccines, and in national and international vaccine policy committees. LSHTM has identified vaccine-related research as a key strategic area and is in the process of setting up a Vaccine Centre to provide a focus for the dissemination of vaccine-related research across the School.
My sponsor, John Edmunds, is a member of the World Health Organization's (WHO) standing committee on quantitative methods for evaluation of vaccine programmes (QUIVER), and WHO Europe's European Technical Advisory Group of Experts on Immunization (ETAGE) which provides independent review and expert technical input to WHO/Europe's Vaccine-preventable Diseases and Immunization programme. These will ensure that my results will be disseminated to public health officials.
My host during the period overseas, Bryan Grenfell, also co-ordinates Research and Policy for Infectious Disease Dynamics (RAPIDD), a unique virtual international centre for disease modelling and policy-making. During my time in Princeton, I will be part of this programme. Moreover, I will be able to organise an international RAPIDD workshop, which will be a great opportunity to discuss my results with both scientific peers and policy makers.
Moreover, several important public health organisations are collaborators in this project: the US Centre for Disease Control, the European Centre for Disease Control and the UK Health Protection Agency. Regular contact with all of these partners will ensure that findings are disseminated to relevant policy makers.
I have chosen the sponsor of this research, the London School of Hygiene \& Tropical Medicine (LSHTM), specifically with the aim in mind to maximise public health impact. A number of LSHTM staff are involved in both polio and measles eradication, in studies on public trust in vaccines, and in national and international vaccine policy committees. LSHTM has identified vaccine-related research as a key strategic area and is in the process of setting up a Vaccine Centre to provide a focus for the dissemination of vaccine-related research across the School.
My sponsor, John Edmunds, is a member of the World Health Organization's (WHO) standing committee on quantitative methods for evaluation of vaccine programmes (QUIVER), and WHO Europe's European Technical Advisory Group of Experts on Immunization (ETAGE) which provides independent review and expert technical input to WHO/Europe's Vaccine-preventable Diseases and Immunization programme. These will ensure that my results will be disseminated to public health officials.
My host during the period overseas, Bryan Grenfell, also co-ordinates Research and Policy for Infectious Disease Dynamics (RAPIDD), a unique virtual international centre for disease modelling and policy-making. During my time in Princeton, I will be part of this programme. Moreover, I will be able to organise an international RAPIDD workshop, which will be a great opportunity to discuss my results with both scientific peers and policy makers.
Moreover, several important public health organisations are collaborators in this project: the US Centre for Disease Control, the European Centre for Disease Control and the UK Health Protection Agency. Regular contact with all of these partners will ensure that findings are disseminated to relevant policy makers.
Publications
Adler A
(2014)
Incidence and risk factors for influenza-like-illness in the UK: online surveillance using Flusurvey
in BMC Infectious Diseases
Bajardi P
(2014)
Determinants of follow-up participation in the Internet-based European influenza surveillance platform Influenzanet.
in Journal of medical Internet research
Bajardi P
(2014)
Association between recruitment methods and attrition in Internet-based studies.
in PloS one
Blumberg S
(2014)
Detecting differential transmissibilities that affect the size of self-limited outbreaks.
in PLoS pathogens
Camacho A
(2017)
Real-time dynamic modelling for the design of a cluster-randomized phase 3 Ebola vaccine trial in Sierra Leone.
in Vaccine
Camacho A
(2014)
Potential for large outbreaks of Ebola virus disease.
in Epidemics
Checchi F
(2015)
Updated estimate of the duration of the meningo-encephalitic stage in gambiense human African trypanosomiasis.
in BMC research notes
Checchi F
(2018)
The impact of passive case detection on the transmission dynamics of gambiense Human African Trypanosomiasis.
in PLoS neglected tropical diseases
Funk S
(2014)
Ebola: the power of behaviour change.
in Nature
Description | Presentation at the Global Task Force for Cholera Control meeting |
Geographic Reach | Multiple continents/international |
Policy Influence Type | Participation in a guidance/advisory committee |
Description | Presentation at the WHO Strategic Advisory Group of Exports on Immunisation and change of WHO recommendations on measles elimination strategies |
Geographic Reach | Multiple continents/international |
Policy Influence Type | Participation in a guidance/advisory committee |
Impact | WHO recommendations have been changed to encourage member states to achieve high levels of immunity against measles in all age groups, in order to achieve elimination. If this recommendation is followed by WHO member states, it will facilitate elimination of measles and, ultimately, bring the world close to complete eradication of measles |
URL | http://www.who.int/immunization/sage/meetings/2017/october/presentations_background_docs/en/ |
Title | RBi and RBi.helpers R packages |
Description | These are R packages to be used with the Bayesian state-space modelling platform libbi. |
Type Of Technology | Software |
Year Produced | 2015 |
Open Source License? | Yes |
Impact | none yet |
URL | http://github.com/sbfnk/RBi |
Title | socialmixr: Social mixing matrices for infectious disease modelling in R |
Description | socialmixr is an R package to derive social mixing matrices from survey data. |
Type Of Technology | Software |
Year Produced | 2016 |
Open Source License? | Yes |
Impact | It is being used by other researchers working on infectious diseases, especially childhood infections. |
URL | https://github.com/sbfnk/socialmixr |