Project

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Integrating epidemiology and mathematical modelling to investigate the impact of interventions to control infectious diseases

Applicant Low Nicola Minling
Number 118424
Funding scheme Project funding (Div. I-III)
Research institution Institut für Sozial- und Präventivmedizin Universität Bern
Institution of higher education University of Berne - BE
Main discipline Methods of Epidemiology and Preventive Medicine
Start/End 01.05.2008 - 30.04.2011
Approved amount 227'000.00
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All Disciplines (3)

Discipline
Methods of Epidemiology and Preventive Medicine
Infectious Diseases
Public Health and Health Services

Keywords (6)

infection transmission science; mathematical modelling; sexually transmitted infections; chlamydia infections; mass screening;

Lay Summary (English)

Lead
Lay summary
This project is called EpideMMIC, which stands for 'Epidemiology and Mathematical Modelling for Infectious disease Control'. The overall aim of the project is to improve our understanding of how infectious diseases spread, and can be controlled. We start by using Chlamydia trachomatis as an example. C. trachomatis is the most commonly reported sexually transmitted infection in many countries and can cause infertility in women and infections in newborn babies. Most chlamydia infections do not cause symptoms so they are passed from person to person unknowlingly. To control chlamydia transmission, regular screening of young adults has been widely promoted. There are, however, no high-quality studies to shown whether screening prevents chlamydia infection or its complications in the long term. Mathematical models and computer simulations can help us to understand how screening and treating the sexual partners of infected people should work. There are already several models describing the transmission of chlamydia, and we have developed one too. The predictions from the models are, however, very different because of differences in the way the models are constructed, and in the values used to describe sexual partnerships, sexual behaviours, the transmissibility and duration of the infection, the probability and timing of complications, and so on. These factors are very difficult to measure and there is a lot of uncertainty about them.There are two main parts to our project. First, we want to understand why the mathematical models differ and to improve our own models. We are collaborating with the researchers who developed the other models and comparing them in detail. We are determining the most important factors driving the transmission of infection, the importance of how progression to chlamydial complications occurs, and how different ways of screening in the population affects infection and complication rates. Second, we are designing new clinical studies and analysing existing epidemiological data to obtain better estimates of the factors in the models that have most influence on the transmission, progression and prevention of chlamydia. The combination of improved models and improved epidemiological evidence will lead to better understanding about the transmission of sexually transmitted infections and the effects of interventions. Policy makers and public health specialists will have better information on which to make decisions about the control and prevention of sexually transmitted infections.
Direct link to Lay Summary Last update: 21.02.2013

Responsible applicant and co-applicants

Employees

Associated projects

Number Title Start Funding scheme
124952 Epidemiology and Mathematical Modelling for Infectious disease Control (EpideMMIC) 01.04.2009 ProDoc
135654 Epidemiology and Mathematical Modelling for Infectious disease Control (EpideMMIC) 01.05.2011 Project funding (Div. I-III)
136737 Acute and chronic dynamics of HIV and HCV infections, within-host evolution and epidemiological outcomes 01.10.2011 Ambizione
133960 Investigating the transmissibility and early dynamics of Chlamydia trachomatis infections 01.11.2010 International Exploratory Workshops

Abstract

Background Predicting future epidemics of infectious diseases and how to control them have long intrigued scientists and policy makers alike. Infection transmission science refers to interdisciplinary efforts by epidemiologists and mathematical modellers to attain this goal. Chlamydia trachomatis, which is the most commonly reported infection in developed countries, provides an excellent example for studying infection transmission systems because of the complex behaviours that result in non-random transmission between of an infection that does not induce lasting immunity. Individual-based dynamic network models provide the most realistic way of studying these interactions. In the absence of any ongoing or funded randomised controlled trials of chlamydia screening, mathematical modelling, integrated with epidemiological interpretation, provide the only way of providing timely information about the requirements of chlamydia screening programmes that do more good than harm. This project involves a collaboration between mathematical modellers who have developed the three most sophisticated network models of chlamydia transmission and epidemiologists with a strong record of research in chlamydia screening interventions. Objectives and goals The overall goal of the project is to contribute to infection transmission science by integrating epidemiological methods and mathematical modelling techniques for studying the transmission of infectious diseases and the impact of preventive interventions, using Chlamydia trachomatis as an example. Specific objectives are: 1) To integrate existing mathematical models to improve the robustness of inferences from a realistic individual-based network model of C. trachomatis sexual transmission and development of sequelae; 2) To determine the likely impact of screening interventions, based on data from existing screening programmes, and to determine the parameters for which better data are needed; 3) To investigate the potential impact of natural immunity, and its loss, on chlamydia transmission.Methods of investigation 1) We will use three existing individual-based dynamic network models to examine parameter uncertainty and model uncertainty in the transmission of C. trachomatis and progression to its sequelae. We will investigate the effects of incorporating sexual network data and alternative screening interventions on the transmission of chlamydia. We will produce a modified model that incorporates sufficient detail to model C. trachomatis transmission realistically, but omits unnecessary complexity. 2) We will externally validate our model, using a dataset that provides the most complete data about the implementation and outcomes of a 10-year organised chlamydia screening programme in a well-defined target population of school students. 3) We will examine a range of scenarios including key factors that influence trends in transmission of C. trachomatis, including host immunity, changes in screening coverage and frequency, changes in sexual behaviour and changes in diagnostic testing patterns and performance. Significance The main benefit of this project for public health is that health policy makers will be able to make better decisions about appropriate interventions to control the most commonly reported transmissible infection in developed countries. The principles employed in the development of this model can then be applied to other infections. The project will also result in methodological advances in mathematical modelling. Collaboration with colleagues at ETH Zurich, and the training of a PhD student, will contribute to creating a critical mass of epidemiologists and mathematical modellers to strengthen the field of infection transmission science in Switzerland.
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