Estimating the impact of social structure on epidemics and predicting the impact of targeted interventions

Lead Research Organisation: Imperial College London
Department Name: School of Public Health


Communicable diseases like flu or SARS transmit through close inter-personal contacts. So, the risk that you get ill during an outbreak is likely to be influenced by who you mix with. Understanding how people interact with each other may therefore be the key to designing efficient control policies. Examples of public health interventions which are triggered by the structure of the social network include those which target households (for example, all household members are treated with antivirals such as Tamiflu when a member is sick) or specific age groups. Consider for example vaccination against seasonal influenza. A first strategy is to vaccinate the elderly as they constitute the main risk group for severe disease and mortality. However, vaccination of children has been suggested as a better policy to minimise mortality overall, since children are the most important transmitters of flu. To assess whether vaccinating children or the elderly is likely to be more effective, it is important to precisely assess how children interact with each other and with other age groups.

If you had flu, would you be able to say where and by whom you got infected? The frequent difficulty in answering this question is what makes it so challenging to determine ?who acquires infection from whom? and thus assess the effectiveness of the different public health strategies. A first aim of this study will be to develop a set of relatively simple mathematical and statistical tools that can be used to make such an evaluation. On an operational level, those tools will make it possible to gain insight on the potential impact of interventions, to monitor the efficacy of control measures and to support decision making in real-time during an outbreak. This work could for example feed the UK surveillance system designed for pandemic influenza; and should increase the capacity of politicians and other decision makers to make the correct choices at the appropriate moments.

By comparing the route of transmission (?who acquires infection from whom?) for different infections and to other sociological indicators (?who talks to whom?), we will also try to answer fundamental questions on the complex nature of transmission. For example, what types of social contact can lead to transmission? Talking? Hugging? Answers to those seemingly simple questions may have major implications in terms of future disease prevention.

Technical Summary

Quantitative epidemiology is now expected to have a key role in preparing for and responding to novel infectious disease outbreaks. The first aim of this project is to develop new statistical and mathematical modelling methods for use in responding to an emerging epidemic of a directly transmitted pathogen, in a situation where little is known about key epidemiological determinants of spread, and thus where analysis must be responsive and rapid. Recent examples of such a situation include the 2003 SARS epidemic or the 2001 UK foot-and-mouth virus epidemic, and the current focus is on highly pathogenic avian influenza or deliberately release smallpox.

In these examples, a key feature of epidemic spread is granularity of spread linked to structure in the host population. Demographic and social data are routinely recorded during epidemic surveillance in an outbreak, and the aim here is to ensure that best use of made of those data in the public health response to outbreaks, and to improve data collection protocols where possible. Specifically, the analysis will focus on incorporating household and age structure in analyzing outbreaks of human pathogens such as SARS or influenza.

Models which guide the public health response will be developed and assessed. Public health response such as isolation, quarantine or drug treatment will commonly target households rather than individuals as a way of extending the reach of interventions to high risk individuals. Age is also an important risk factor for many infections, and similarly interventions such as vaccination can be targeted at specific age groups. The new suite of methods developed here will inform these detailed policy options in real-time as an epidemic unfolds.

The second aim of the project, closely allied to the first, is to advance the state of understanding of these models and of their key parameters based on extensive literature searches for data. The aim is to compare different infections with similar routes of transmission but differences in natural history and immunity, and thus to disentangle the effects of social structure and host-pathogen biology on transmission and spread. This feeds into the first aim in that these newly inferred parameters will inform model and parameter choice in the epidemiological response to a new pathogen.

The project will involve a multi-disciplinary team and will employ a statistician and a mathematical modeller who will work closely together, and thus who will both acquire skills which underpin this dynamic research area.


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