Using novel causal inference approaches to improve decision-making in infectious diseases epidemiology

由于耐药病原体的迅速传播，卫生保健专业人员在传染病的治疗和控制方面面临挑战。大多数证据来自随机临床试验（RCT）；然而，这些方法的运行成本很高，而且它们的通用性受到了质疑。因果推理方法可用于解决这个问题，但这些方法在ID中的采用受到限制。本研究旨在利用这些流行病学模型来提高未来随机对照试验的质量。

Health care professionals face challenges in infectious diseases(ID) treatment and control from rapid spread of drug-resistant pathogens. Most evidence has come from randomised clinical trials (RCT); however, these are expensive to run, and their generalisability has been questioned. Causal inference methods are available to address this issue, but the uptake of these methods in ID has been limited. This study aims to utilise these epidemiological modelling to enhance the quality of future RCTs.