Modelling social clustering of susceptibles and its impact on measles elimination
Lead Research Organisation:
London School of Hygiene & Tropical Medicine
Department Name: Epidemiology and Population Health
Abstract
Background
Despite the very widespread use of measles vaccination, few countries have been able to achieve elimination of the measles virus, which refers to the interruption of endemic transmission in a defined geographical area. Vaccine efficacy is high (two doses is roughly 97% effective at preventing measles; one dose is about 90% effective) and the burden of measles has been substantially reduced after its introduction. Nevertheless, frequent outbreaks continue to affect populations around the world.
The United Kingdom is a representative example of the difficulties experienced by developed countries to control measles. In the last ten years, surveillance systems observed hundreds of annual cases in England and Wales. After the interruption of endemic transmission for the last 36 months, the United Kingdom achieved elimination of measles in 2017. Nonetheless, increasing mistrust in vaccine is observed in the community, which may lead to endemic transmission in the years to come. A better understanding of recent outbreaks and the current state of surveillance is required to propose new methods to maintain elimination. The objectives of such work should therefore be:
i/ Describe and understand recent outbreaks, in order to
ii/ evaluate the weaknesses of the current vaccination routine and
iii/ propose guidelines which will maintain elimination and, ultimately, make eradication of the disease an achievable objective.
This project will assess what are the weaknesses of the vaccination routine, the profiles of the individuals this routine are unable to reach, and the current high-risk modes of transmission.
Aims and Methods
A particular challenge for control of measles at high levels of vaccination is clustering of susceptibles, whereby those lacking immunity are preferentially in social contact with each other, either because they share a set of values or beliefs, or because they are part of the same community underserved by public health. We can identify three different approaches to analysing a patient-level dataset made available through contacts at Public Health England (PHE):
i/ The description of the previous outbreaks,
ii/ An investigation of the importance of social networks and clustering of susceptibles
iii/ The modifications of the spread of the virus due to social behaviour changes.
To perform this analysis, we will apply quantitative skills in mathematics, statistics and computation to the patient-level dataset. This work aims to reconstruct a probabilistic transmission network to gain insights of the spread of the virus at an individual level.
Genetic sequence data routinely collected by PHE will be used to enhance the accuracy of the probabilistic transmission trees, and to point out the proximity between two cases (same index case, direct transmission between them...). We will use cutting-edge phylogenetic tools to generate a probabilistic transmission tree and track common features to describe the individuals able to catalyse the spread of the virus.
Finally, using data on social mixing patterns, demography and social behaviour changes, we aim to quantify processes that lead to modifications in the dynamics of transmission of childhood diseases in the community. This will lead to a health economics analysis of vaccination campaigns' impact, to determine the cost-effectiveness of different interventions.
Potential benefits
All of these analyses will provide important insights into the recent spread of measles in the United Kingdom and the potential future risks of spreading of the virus in the country. We expect the proposed studies to lead to suggestions for improved vaccination strategies towards elimination and mitigation of outbreak risk. On a broader scale, as the United Kingdom is a good example of the complications Western European countries are facing to fully eradicate measles virus, this study will also give insight on how control measures should be used to reduce the risk of
Despite the very widespread use of measles vaccination, few countries have been able to achieve elimination of the measles virus, which refers to the interruption of endemic transmission in a defined geographical area. Vaccine efficacy is high (two doses is roughly 97% effective at preventing measles; one dose is about 90% effective) and the burden of measles has been substantially reduced after its introduction. Nevertheless, frequent outbreaks continue to affect populations around the world.
The United Kingdom is a representative example of the difficulties experienced by developed countries to control measles. In the last ten years, surveillance systems observed hundreds of annual cases in England and Wales. After the interruption of endemic transmission for the last 36 months, the United Kingdom achieved elimination of measles in 2017. Nonetheless, increasing mistrust in vaccine is observed in the community, which may lead to endemic transmission in the years to come. A better understanding of recent outbreaks and the current state of surveillance is required to propose new methods to maintain elimination. The objectives of such work should therefore be:
i/ Describe and understand recent outbreaks, in order to
ii/ evaluate the weaknesses of the current vaccination routine and
iii/ propose guidelines which will maintain elimination and, ultimately, make eradication of the disease an achievable objective.
This project will assess what are the weaknesses of the vaccination routine, the profiles of the individuals this routine are unable to reach, and the current high-risk modes of transmission.
Aims and Methods
A particular challenge for control of measles at high levels of vaccination is clustering of susceptibles, whereby those lacking immunity are preferentially in social contact with each other, either because they share a set of values or beliefs, or because they are part of the same community underserved by public health. We can identify three different approaches to analysing a patient-level dataset made available through contacts at Public Health England (PHE):
i/ The description of the previous outbreaks,
ii/ An investigation of the importance of social networks and clustering of susceptibles
iii/ The modifications of the spread of the virus due to social behaviour changes.
To perform this analysis, we will apply quantitative skills in mathematics, statistics and computation to the patient-level dataset. This work aims to reconstruct a probabilistic transmission network to gain insights of the spread of the virus at an individual level.
Genetic sequence data routinely collected by PHE will be used to enhance the accuracy of the probabilistic transmission trees, and to point out the proximity between two cases (same index case, direct transmission between them...). We will use cutting-edge phylogenetic tools to generate a probabilistic transmission tree and track common features to describe the individuals able to catalyse the spread of the virus.
Finally, using data on social mixing patterns, demography and social behaviour changes, we aim to quantify processes that lead to modifications in the dynamics of transmission of childhood diseases in the community. This will lead to a health economics analysis of vaccination campaigns' impact, to determine the cost-effectiveness of different interventions.
Potential benefits
All of these analyses will provide important insights into the recent spread of measles in the United Kingdom and the potential future risks of spreading of the virus in the country. We expect the proposed studies to lead to suggestions for improved vaccination strategies towards elimination and mitigation of outbreak risk. On a broader scale, as the United Kingdom is a good example of the complications Western European countries are facing to fully eradicate measles virus, this study will also give insight on how control measures should be used to reduce the risk of
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| MR/N013638/1 | 01/10/2016 | 30/09/2025 | |||
| 1923699 | Studentship | MR/N013638/1 | 01/10/2017 | 31/05/2021 | Alexis Robert |
| Title | o2geosocial: Integrating geographical and social contact data to reconstruct transmission chains of infectious disease |
| Description | As regional immunity against infectious diseases is built up by past infections and, if a vaccine is available, vaccination campaigns, social and spatial heterogeneity in disease incidence or vaccine coverage lead to under-immunised areas, also called pockets of susceptibles. Importation of cases into these areas can cause large transmission clusters and long-lasting outbreaks. The most vulnerable areas of a country could be identified using historical data on local vaccine coverage and incidence, but these data can be scarce or unreliable. Another solution is to infer probabilistic transmission trees and clusters in order to identify in which regions importations repeatedly caused large transmission clusters. The Wallinga-Teunis method was developed to infer probabilistic transmission trees from onset dates, serial interval and latent periods in a maximum likelihood framework. As genetic sequencing of cases during an outbreak became more common, new tools such as the R package outbreaker2 showed that combining the timing of infection and the genetic sequences could improve the accuracy of inferred transmission trees. Nevertheless, sequencing cases remains costly, and the efficacy of the reconstruction methods depends on the proportion of sequenced cases, the quality of the sequences, and on the characteristics of the virus. For instance, the measles virus evolves very slowly, so sequences from unrelated cases can be very similar, which makes methods combining onset dates and genetic sequences ineffective. Building upon the framework presented in outbreaker2, we developed the R package o2geosocial to estimate the cluster size distribution from the onset date, age, location and genotype of the cases. Those variables are generally collected by surveillance system and well reported. Using age-stratified contact matrices and mobility models, we combined the different variables into a likelihood of connection between cases. The package o2geosocial is ideal to study outbreaks where sequences are uninformative, either because only a small proportion of cases were sequenced or because the virus evolves too slowly. |
| Type Of Material | Model of mechanisms or symptoms - human |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| Impact | The method is about to be submitted as a CRAN package, so that other teams and researchers can use it on different projects. We also applied this method to cluster the measles cases reported in the United States between 2001 and 2016 (https://www.medrxiv.org/content/10.1101/2020.02.13.20020891v1). |
| URL | https://github.com/alxsrobert/o2geosocial |
| Description | Outbreak control: an introduction to careers in public health |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Schools |
| Results and Impact | We visited 3 schools in the London area to deliver a total of 6 2-hour workshops on careers in public health. A class of between 20 and 40 students in year 10/11 were asked to help answer questions to advise the government on how to stop a simulated malaria outbreak that had just begun in London. Students were asked at the beginning of the session for what careers they thought would be involved in this work. They then got to see how people at LSHTM would be involved - epidemiologist, entomologists, statisticians, modellers and diagnosticians - to demonstrate the range of science careers available in public health. Each career had a stand where students learned about the work of that profession in 15 minutes with interactive learning tools used. All five stands involved interactive games or demonstrations e.g. box of mosquitoes to demonstrate the effects of repellent, model figures to calculate sensitivity and specificity of a diagnostic test. Students had to work together on tasks in small groups (4-9 students), and come together at the end to combine evidence from different stands (careers) to inform the government on the best available strategy to control the 'outbreak'. We received very positive feedback from all teachers, all saying they would want to run the workshop again. After the workshops were over, several students at each school approached members of our team to ask for advice on pursuing specific scientific careers. After hearing of our workshop through school newsletters/websites/word of mouth, other classes and year groups from the schools we visited contacted us. |
| Year(s) Of Engagement Activity | 2018,2019,2020 |