Antimicrobial resistance; sexually transmitted infections; chlamydia infections; Human papillomavirus; Mycoplasma genitalium; infection disease dynamics; prevention
Cina Manuel, Baumann Lukas, Egli-Gany Dianne, Halbeisen Florian, Hammad Ali, Scott Pippa, Low Nicola (2019), Mycoplasma genitalium incidence, persistence, concordance between partners and progression: systematic review and meta-analysis, in Sexually Transmitted Infections
Smid Joost H., Garcia Victor, Low Nicola, Mercer Catherine H., Althaus Christian L. (2018), Age difference between heterosexual partners in Britain: Implications for the spread of Chlamydia trachomatis, in Epidemics
, 24, 60-66.
Cadosch D Garcia V Althaus CL Jensen JS Low N (2018), De novo mutations drive the spread of macrolide resistant Mycoplasma genitalium: mathematical modelling study, in bioRxiv
Low N Smid JH (2018), Changes in chlamydia prevalence over time: how to observe the unobserved, in Lancet Public Health
BackgroundNew interventions are being developed to control sexually transmitted infections and antimicrobial resistance and existing interventions are being re-evaluated. GoalsThe overall goal of the Epidemiology and Mathematical Modelling for Infectious disease Control (EpideMMIC) project is to strengthen the integration of epidemiological, statistical and mathematical modelling methods for evaluating preventive interventions. Specific objectives focus on the following interventions: Objective 1, screening programmes for Chlamydia trachomatis; Objective 2, point-of-care tests to distinguish aetiologies of male urethritis and control antimicrobial resistant Mycoplasma genitalium; Objective 3, human papillomavirus (HPV) vaccination strategies to prevent anal cancer. Methods of investigationObjective 1: We will evaluate new data about chlamydia prevalence and screening impact using two approaches, 1) a Bayesian inferential framework for a C. trachomatis transmission model using observational data from Great Britain and 2) compare two dynamic modelling frameworks using randomised controlled trial data from Australia. We then aim to apply the Bayesian framework to objectives 2 and 3. Objective 2: a dual infection model of C. trachomatis and M. genitalium transmission, incorporating resistant strains to investigate the impact on antimicrobial resistance of point-of-care aetiological diagnosis of male urethritis and detection of genetic resistance markers. Objective 3: a model that combines HPV transmission and anal disease progression to investigate male HPV vaccination strategies in men who have sex with men, taking into account HPV and HIV interactions.TimescaleApril 2015 to March 2018Importance and impactThe EpideMMIC project is important for its contributions in three areas. First, EpideMMIC project outputs have already contributed to public health decision making for infectious disease control both internationally and in Switzerland. The research themes that we propose now are both topical and important. Second, we will contribute to methodological advances through the development of a Bayesian inferential framework for age-structured transmission dynamic models for non-immunising infections. Third, the EpideMMIC project has a record of providing a stimulating environment to develop careers in infectious disease epidemiology and this proposal offers new opportunities for original research.