Close
Found 0 results for your search. Showing 0 to 0.
No results could be found for your search.
x Close Search
Close

Epidemiology and Modelling

"Understanding the evolution, spread, and cost of antimicrobial resistance is vital for sustaining the useful lifespan of the current clinical portfolio of cheap antimicrobial drugs and avert preventable pathogen-related deaths. The world-class research and data from multidisciplinary themes in the AMRC are timely, critical, lifesaving and ensuring healthy lives and promoting well-being across the world (SDG3)." - Professor Alfred Ngwa, MRC Unit The Gambia at LSHTM

Graphs

Quantifying the distribution, patterns and burden of antimicrobial resistance (AMR) is a vital task in the prioritisation and shaping of policy decisions for evidence-based practice. As such, the research in the Epidemiology and Modelling pillar provides the data analysis and modelling that underpins much of the intervention development work across the other pillars of the centre.

Background 

Epidemiological data analysis allows us to track and describe changes in AMR evolution over time, combining routine surveillance with genomic data to understand the hotspots, potential drivers and trends in resistance accumulation over time. These patterns differ according to the type of pathogen - virus, bacterium or parasite; the scale - within an individual human/animal host or between populations; and the setting - hospital, urban community or rural/agricultural. 

Such patterns then govern the structures used in mathematical models to understand the evolution and interventions for the control of AMR. For example, transmission or evolutionary dynamics models have been used to explore the impact of model structure on the interpretation of resistance data and to determine where the burden and drivers of antibiotic use, and hence AMR, are. 

Building on these, researchers within this pillar often use the economic insights from micro-economic and macro-economic models to explore the cost and cost effectiveness of interventions strategies. This evidence is vital in the bolstering of political will to tackle the cross-sectoral problems of AMR. 

Areas of active research

Researchers in the Epidemiology and Modelling pillar address a wide range of research questions related to spread, burden and drivers of AMR across different scales. They are also involved in the development of modelling tools to provide the key quantitative and economic evidence to inform policy. 

  • Researchers at the MRC Unit The Gambia at LSHTM use a combination of epidemiology and genomic data analysis to explore the spread of resistance within malarial, Mycobacterium and invasive bacteria across The Gambia and West Africa. This is led by Dr Alfred Ngwa, Prof Martin Antonio, Prof Anna Roca and several other scientists in the Unit. 
  • The JPIAMR funded SEFASI project led by Dr Gwen Knight aims to select efficient farm-level antimicrobial stewardship interventions using cross-disciplinary statistical, mathematical and economic modelling. Taking a One Health approach across the three countries (England, Denmark and Senegal), the aim is to use a unified framework to determine the potential contribution of antibiotics in food-producing animals to resistance in infections in humans. 
  • Dr Katherine Atkins with Prof. Mark Jit and other researchers at LSHTM consider the ways in which mathematical models can be used to explore the pathways to impact of vaccines on AMR. This research area is expanding to consider the economic burden of AMR that could be averted by vaccines in collaboration with the WHO, in work led by Dr Nichola Naylor and Dr Nicholas Davies. 
  • Modellers within the TB Modelling Group have used combinations of epidemiology, statistical and mathematical models to explore various aspects of drug-resistant M. tuberculosis transmission - from exploring gender related risks of transmission, led by Dr Finn Mcquaid (co-Director of the TB Centre), to estimating the burden of latent multi-drug resistant Mtb carriage in work led by Dr Gwen Knight.
  • The theoretical predictions that models make on AMR prevalence are driven by within-host dynamics, as shown by work by Dr Nicholas Davies within the Centre for Mathematical Modelling of Infectious Diseases. 
  • Statistical and mathematical models can be used to untangle the complex and often hard to measure interactions between microbiological populations that drive the emergence of AMR. For example, dose-response relationships in S. pneumoniae resistance in work led by Dr Laith Yakob and the dynamics of resistance gene movement by phage in S. aureus in ongoing work led by those in Dr Gwen Knight’s group. 

Further resources