What reference supports the statement that transmission dynamic models integrate individual-level phenomena, such as infection or disease episodes and antibiotic (Antimicrobial) treatment, with population-level outcomes, including the prevalence of Antimicrobial Resistance (AMR)?

From the Guidelines

Transmission dynamic models, as described in the work of Lipsitch and colleagues, provide a crucial framework for understanding how individual-level phenomena, such as infection and antibiotic treatment, influence population-level outcomes, including the prevalence of antimicrobial resistance (AMR). This understanding is essential for developing effective strategies to combat AMR, as highlighted in a 2020 study on optimizing the design of research to evaluate antibiotic stewardship interventions 1. The study emphasizes the need for a comprehensive approach to address the growing threat of AMR, which is driven by the overuse of antimicrobials at both the individual and population levels.

Key points to consider when evaluating the role of transmission dynamic models in AMR include:

• The importance of integrating individual-level data, such as infection episodes and antibiotic treatment, with population-level outcomes, such as resistance prevalence
• The need for robust mathematical models that can account for the complex interactions between individual treatment decisions and community-level resistance patterns
• The value of a multidisciplinary approach, incorporating expertise from fields such as epidemiology, microbiology, and clinical medicine, to develop effective strategies for combating AMR

While the provided study 1 focuses on the design of research to evaluate antibiotic stewardship interventions, it underscores the critical role of understanding transmission dynamics in addressing AMR. However, for a direct reference to transmission dynamic models and their application in integrating individual-level phenomena with population-level outcomes, one would need to consult specific literature on mathematical modeling in epidemiology, such as the work by Lipsitch and Samore, which laid foundational principles for this field.

From the Research

Transmission Dynamic Models

Transmission dynamic models provide a framework for understanding the spread of infectious diseases, including the integration of individual-level phenomena, such as infection and treatment, with population-level outcomes, like the prevalence of antimicrobial resistance (AMR).

• These models account for the complex interactions between susceptible and infectious individuals in a population, allowing for the estimation of key transmission variables and the evaluation of intervention strategies 2, 3, 4.
• The use of transmission dynamic models can help inform public health policy and practice, particularly in the context of emerging infectious diseases and the spread of AMR 3, 4.
• Examples of the application of transmission dynamic models include the study of zoonotic origins, microbial adaptation and change, human susceptibility, and climate change 4.
• The integration of transmission dynamic models with data and rigorous testing and evaluation is crucial for their successful application in guiding public health decisions 3.

Individual and Population Level Outcomes

The statement that transmission dynamic models provide an analytical framework for integrating phenomena at the individual level with population-level outcomes is supported by the following evidence:

• Transmission dynamic models can account for changes in incidence rates of infection as the proportions of susceptible and infectious persons change in the population 4.
• These models can be used to evaluate the effectiveness of intervention strategies, such as antibiotic stewardship, in reducing the spread of AMR 5, 6.
• The use of transmission dynamic models can help estimate key transmission variables, such as the basic reproductive number and the effective reproductive number, which are essential for understanding the spread of infectious diseases 2, 4.