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Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
Published on: July 4, 2007
Population-level mathematical modeling of antimicrobial resistance: a systematic review
Anna Maria Niewiadomska1, Bamini Jayabalasingham1,2, Jessica C Seidman1
1Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, USA.
Background:
Mathematical transmission models are increasingly used to guide public health interventions for infectious diseases, particularly in the context of emerging pathogens; however, the contribution of modeling to the growing issue of antimicrobial resistance (AMR) remains unclear. Here, we systematically evaluate publications on population-level transmission models of AMR over a recent period (2006-2016) to gauge the state of research and identify gaps warranting further work.
Methods:
We performed a systematic literature search of relevant databases to identify transmission studies of AMR in viral, bacterial, and parasitic disease systems. We analyzed the temporal, geographic, and subject matter trends, described the predominant medical and behavioral interventions studied, and identified central findings relating to key pathogens.
Results:
We identified 273 modeling studies; the majority of which (> 70%) focused on 5 infectious diseases (human immunodeficiency virus (HIV), influenza virus, Plasmodium falciparum (malaria), Mycobacterium tuberculosis (TB), and methicillin-resistant Staphylococcus aureus (MRSA)). AMR studies of influenza and nosocomial pathogens were mainly set in industrialized nations, while HIV, TB, and malaria studies were heavily skewed towards developing countries. The majority of articles focused on AMR exclusively in humans (89%), either in community (58%) or healthcare (27%) settings. Model systems were largely compartmental (76%) and deterministic (66%). Only 43% of models were calibrated against epidemiological data, and few were validated against out-of-sample datasets (14%). The interventions considered were primarily the impact of different drug regimens, hygiene and infection control measures, screening, and diagnostics, while few studies addressed de novo resistance, vaccination strategies, economic, or behavioral changes to reduce antibiotic use in humans and animals.
Conclusions:
The AMR modeling literature concentrates on disease systems where resistance has been long-established, while few studies pro-actively address recent rise in resistance in new pathogens or explore upstream strategies to reduce overall antibiotic consumption. Notable gaps include research on emerging resistance in Enterobacteriaceae and Neisseria gonorrhoeae; AMR transmission at the animal-human interface, particularly in agricultural and veterinary settings; transmission between hospitals and the community; the role of environmental factors in AMR transmission; and the potential of vaccines to combat AMR.
Insights
Mathematical models for antimicrobial resistance (AMR) transmission primarily focus on established pathogens, neglecting emerging threats and upstream prevention strategies. Research gaps include animal-human interfaces and environmental factors in AMR spread.
Area of Science:
- Epidemiology
- Mathematical Modeling
- Public Health
Background:
- Mathematical transmission models are vital for infectious disease control, yet their role in addressing antimicrobial resistance (AMR) is not well-defined.
- A systematic evaluation of AMR transmission modeling research from 2006-2016 was conducted to assess the current state and identify research gaps.
Purpose of the Study:
- To systematically evaluate population-level transmission models of AMR.
- To gauge the state of research in AMR modeling.
- To identify gaps in the current body of AMR transmission modeling research.
Main Methods:
- Systematic literature search across relevant databases for AMR transmission studies.
- Analysis of temporal, geographic, and subject matter trends in identified studies.
- Description of predominant interventions studied and key findings for major pathogens.
Main Results:
- 273 modeling studies identified, predominantly on HIV, influenza, malaria, TB, and MRSA.
- AMR modeling is concentrated in human, community, and healthcare settings, with limited focus on animal-human interfaces or environmental factors.
- Most models were compartmental and deterministic, with less than half calibrated to epidemiological data; few were validated.
Conclusions:
- Current AMR modeling research focuses on long-established resistance, with limited proactive research on emerging pathogens or strategies to reduce antibiotic consumption.
- Significant research gaps exist in modeling AMR transmission at the animal-human interface, between healthcare and community settings, and the role of environmental factors.
- The potential of vaccines and upstream behavioral/economic changes in combating AMR requires further investigation through modeling.
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