Modelling social clustering of susceptibles and its impact on measles elimination

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BackgroundDespite 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 MethodsA 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 benefitsAll 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