Powell-Romero Francisca, Wells Konstans, Clark Nicholas J
School of Veterinary Science, The University of Queensland, 5391 Warrego Hwy, Gatton, Queensland 4343, Australia.
Department of Biosciences, Swansea University, Singleton Park, Swansea SA2 8PP, UK.
R Soc Open Sci. 2024 Oct 4;11(10):231589. doi: 10.1098/rsos.231589. eCollection 2024 Oct.
The simultaneous infection of organisms with two or more co-occurring pathogens, otherwise known as co-infections, concomitant infections or multiple infections, plays a significant role in the dynamics and consequences of infectious diseases in both humans and animals. To understand co-infections, ecologists and epidemiologists rely on models capable of accommodating multiple response variables. However, given the diversity of available approaches, choosing a model that is suitable for drawing meaningful conclusions from observational data is not a straightforward task. To provide clearer guidance for statistical model use in co-infection research, we conducted a systematic review to (i) understand the breadth of study goals and host-pathogen systems being pursued with multi-response models and (ii) determine the degree of crossover of knowledge among disciplines. In total, we identified 69 peer-reviewed primary studies that jointly measured infection patterns with two or more pathogens of humans or animals in natural environments. We found stark divisions in research objectives and methods among different disciplines, suggesting that cross-disciplinary insights into co-infection patterns and processes for different human and animal contexts are currently limited. Citation network analysis also revealed limited knowledge exchange between ecology and epidemiology. These findings collectively highlight the need for greater interdisciplinary collaboration for improving disease management.
生物体同时感染两种或更多种同时存在的病原体，即所谓的共感染、合并感染或多重感染，在人类和动物传染病的动态变化及后果中起着重要作用。为了理解共感染，生态学家和流行病学家依赖于能够容纳多个响应变量的模型。然而，鉴于可用方法的多样性，选择一个适合从观测数据中得出有意义结论的模型并非易事。为了在共感染研究中为统计模型的使用提供更清晰的指导，我们进行了一项系统综述，以（i）了解使用多响应模型所追求的研究目标和宿主 - 病原体系统的广度，以及（ii）确定各学科之间知识交叉的程度。我们总共识别出69项经过同行评审的初步研究，这些研究共同测量了自然环境中人类或动物感染两种或更多种病原体的模式。我们发现不同学科在研究目标和方法上存在明显分歧，这表明目前对于不同人类和动物背景下共感染模式和过程的跨学科见解有限。引文网络分析还揭示了生态学和流行病学之间的知识交流有限。这些发现共同凸显了加强跨学科合作以改善疾病管理的必要性。
