Modelling disease emergence in heterogeneous populations

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


Diseases that can make the leap from animal to human are an important focus for research. Some, such as SARS, have already caused outbreaks; others, like avian influenza, have the potential to do so. However, there is still much we do not understand about these infections. In particular, can we distinguish between pathogens that transmit only from animal to human, and those than can transmit from person to person? And how can we estimate the rate at which a new infection spreads?

Part of the answer lies in Southeast Asia. The region forms an evolutionary hub for many pathogens, and events there are likely to have a major impact on global health in the future. In particular, how people interact with each other, and animals, in these countries could affect disease emergence and subsequent transmission in a population. Therefore London School of Hygiene and Tropical Medicine recently led a study that collected data on social contact networks in Vietnam, Thailand, Indonesia and Taiwan. However, to understand the dynamics of emerging infections, this data needs to be incorporated into mathematical models, which can be used to simulate outbreaks. This will form the first part of the project; specifically, I will work on methods to identify infections that have adapted to spread between humans, based on case reports stratified by age. The potential for these approaches to be applied during the early stages of an outbreak will be investigated using data on the 2009 H1N1 pandemic in the UK.

The second part of the project will develop statistical methods to better understand disease transmission following the emergence of a new pathogen. These techniques could provide essential information in the early stages of an outbreak, and will be tested against real data from Vietnam and Indonesia.

The project will use ideas and techniques from several fields to achieve these aims, with collaborators including specialists in evolutionary biology, statistics and epidemiology. The insights generated by the project will help improve our understanding of pathogen emergence, create better ways to spot human-adapted strains, and develop tools to provide crucial information about new outbreaks.

Technical Summary

Data on social interactions will be used to approximate contact structure, including interaction with animals. Disease transmission will be modelled stochastically; in particular, I will use multi-type branching processes to develop a framework in which heterogeneity in social contact - and hence transmission - can be incorporated along with pathogen adaptation. Analysis of the age distribution of disease cases during an outbreak will then be used to generate an inference method of distinguishing between different situations - for instance, stuttering transmission, whereby the disease fails to establish in the human population, or a full-scale epidemic. This will involve using approximate Bayesian computation (ABC), which bypass exact likelihood calculations by using summary statistics (such as the distribution of cases at the end of the outbreak). The potential for these techniques to be applied during an outbreak will be explored using data on the early progression of the 2009 H1N1 pandemic in the UK.

Using simulated epidemic data generated from these models developed, I will also explore a statistical framework for inference of the effective reproductive ratio, with contact data used as a prior. Existing 'gold-standard' data augmentation approaches are computationally intensive for large populations, so I will look at alternative such as ABC and ABC based on sequential Monte Carlo (ABC-SMC), as well as branching process approximations. The performance of these frameworks will be tested against real disease case data from Southeast Asia, as well as simulated data.

Planned Impact

Disease surveillance and control is a major concern for public health agencies. The research will provide insights into how to effectively monitor for new infections, and tools with which to assess the likely impact of a new outbreak. The work may also help inform surveillance measures, such as what data are best to collect before, and during, an outbreak. These are critical pieces of information for public health officials, both at the national and international level.

The burden disease brings - both in Asia and abroad - can have a negative impact on economies, both in terms of healthcare costs and absenteeism. Understanding the emergence of new infections is an important step to reducing this burden. By investigating the dynamics of zoonoses, the project also has the opportunity improve the effectiveness of disease control for affected countries.

As pandemic emergence is a topic of national importance, the research will also be relevant to the public. The proposed research into new outbreaks - communicated effectively to this general audience - will improve individuals' knowledge and understanding of such events.


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Description Sir Henry Dale Fellowship
Amount £808,171 (GBP)
Funding ID 206250/Z/17/Z 
Organisation Wellcome Trust 
Sector Charity/Non Profit
Country United Kingdom
Start 09/2017 
End 08/2022
Description Online article 
Form Of Engagement Activity A magazine, newsletter or online publication
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Public/other audiences
Results and Impact This article covered some of the methods involved in calculating the reproduction number of an emerging infection - a central focus of my fellowship project. To date, the piece has received over 14,000 views.

The piece stimulated interested discussion from fellow researchers and the public both via e-mail and Twitter.
Year(s) Of Engagement Activity 2014
Description Science Museum Lates (interactive outbreak) 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach Local
Primary Audience Public/other audiences
Results and Impact We staged an activity in the Ebola zone where they spread and live-tracked a 'sticker infection' through the Museum over the course of the evening. At some points in the evening there were as many as 20 new cases of infection reported in just five minutes! This interactive game illustrated not only how an infection can spread through a population but also served as a backdrop to discuss how mathematical modelling is used to track and predict infection spread during a real epidemic, such as the recent Ebola epidemic in West Africa.
Year(s) Of Engagement Activity 2016