Unlocking the black box – Agent-based simulations to measure the effectiveness of sample storage and retesting

Summary

The study addresses the gap between doping abuse and its detection, highlighting the potential of long-term storage and retesting of samples to bridge this gap. This project aims to quantify the impact of different strategies for sample storage and retesting on doping behavior using agent-based modeling (ABM). 

Methodology

The research employed a multi-faceted approach, beginning with a comprehensive screening of the WADA Code, International Standards, and Guidelines to understand the regulations surrounding long-term storage and retesting. A systematic literature review was conducted to identify existing research on the effectiveness of anti-doping measures, resulting in the identification of 35 eligible studies. Expert interviews with representatives from National Anti-Doping Organizations (NADOs) and Regional Anti-Doping Organizations (RADOs) provided insights into the current practices and perceptions of long-term storage and retesting. 

The core of the study involved the development and extension of an agent-based model (ABM) to simulate the doping behavior of athletes under various scenarios. The model incorporated findings from social science and game theory, allowing for the examination of different anti-doping strategies, including the number of stored samples, storage duration, and selection procedures. The simulation was run using a software tool called NetLogo, which is designed for building and running agent-based models. Throughout the process, the simulation was improved and adjusted multiple times based on feedback from partner Anti-Doping Organizations (ADOs). Researchers worked closely with these organizations to ensure the simulation was as accurate and useful as possible, making changes as needed to better reflect real-world conditions and insights. 

Results

The study identified a significant gap in research regarding the long-term storage and retesting of anti-doping samples. Through expert interviews and simulations, it was found that the application of these measures varies widely among ADOs. This variation is due to different interpretations of the value of long-term storage and retesting, as well as strategic priorities. 

The simulations showed that increasing the number of stored samples and the duration of storage can effectively reduce doping. The "Mixed" testing plan, which combines testing top performers with random testing, was found to be the most effective strategy. This approach ensures that both high-achieving athletes and a broader athlete population are tested, maintaining an element of unpredictability. 

The analysis also revealed that while increasing stored samples and storage duration initially reduces doping, there is a point where further increases offer diminishing returns. This suggests an optimal level of implementation for these measures.

Significance for Clean Sport

The findings highlight the importance of long-term storage and retesting as crucial tools in the fight against doping. Although other measures like improved diagnostics, bans, and frequent testing have a more immediate impact, long-term storage and retesting extend the detection window and leverage advancements in testing methods. This approach enhances the deterrence effect and helps maintain the integrity of sports by ensuring athletes are accountable over time. 

The study's results align with athletes' perceptions of anti-doping measures, emphasizing the need for a comprehensive strategy in clean sport efforts. The collaboration between academia and anti-doping practitioners in this project provides valuable insights and recommendations for optimizing sample storage and retesting strategies, contributing to the ongoing fight against doping in sports.