Yano Timothy Kiprono, Afrifa-Yamoah Ebenezer, Collins Julia, Mueller Ute, Richardson Steven
School of Science, Edith Cowan University, Perth, Western Australia, Australia
School of Science, Edith Cowan University, Perth, Western Australia, Australia.
BMJ Open. 2024 Dec 31;14(12):e084027. doi: 10.1136/bmjopen-2024-084027.
Breaking the chain of transmission of an infectious disease pathogen is a major public health priority. The challenges of understanding, describing and predicting the transmission dynamics of infections have led to a wide range of mathematical, statistical and biological research problems. Advances in diagnostic laboratory procedures with the ability to test multiple pathogens simultaneously mean that co-infections are increasingly being detected, yet little is known about the impact of co-infections in shaping the course of an infection, infectivity, and pathogen replication rate. This is particularly true of the apparent synergistic effects of viral and bacterial co-infections, which present the greatest threats to public health because of their lethal nature and complex dynamics. This systematic review protocol is the foundation of a critical review of co-infection modelling and an assessment of the key features of the models.
MEDLINE through PubMed, Web of Science, medRxiv and Scopus will be systematically searched between 1 December 2024 and 31 January 2025 for studies published between January 1980 and December 2024. Three reviewers will screen articles independently for eligibility, and quality assessment will be performed using the TRACE (TRAnsparent and Comprehensive Ecological) standard modelling guide. Data will be extracted using an Excel template in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis standard reporting guidelines. This systematic review will apply the SWiM (Synthesis Without Meta-analysis) approach in its narrative synthesis coupled with tables and figures to present data. The synthesis will highlight key dynamical co-infection model features such as assumptions, data fitting and estimation methods, validation and sensitivity analyses, optimal control analyses, and the impact of co-infections.
Ethics approval is not required for a systematic review since it will be based on published work. The output of this study will be submitted for publication in a peer-reviewed journal.
CRD42023481247.
打破传染病病原体的传播链是公共卫生的一项重大优先事项。理解、描述和预测感染传播动态所面临的挑战引发了一系列数学、统计和生物学研究问题。能够同时检测多种病原体的诊断实验室程序的进步意味着合并感染越来越多地被检测到，但对于合并感染在塑造感染进程、传染性和病原体复制率方面的影响知之甚少。病毒和细菌合并感染的明显协同效应尤其如此，由于其致命性质和复杂动态，对公共卫生构成了最大威胁。本系统评价方案是对合并感染建模进行批判性综述以及评估模型关键特征的基础。
将在2024年12月1日至2025年1月31日期间对PubMed、Web of Science、medRxiv和Scopus数据库中的MEDLINE进行系统检索，以查找1980年1月至2024年12月期间发表的研究。三位评审员将独立筛选文章的合格性，并使用TRACE（透明和综合生态）标准建模指南进行质量评估。将根据系统评价和Meta分析的首选报告项目标准报告指南，使用Excel模板提取数据。本系统评价将在叙述性综合中应用SWiM（无Meta分析的综合）方法，并结合表格和图表来呈现数据。综合将突出关键的动态合并感染模型特征，如假设、数据拟合和估计方法、验证和敏感性分析、最优控制分析以及合并感染的影响。
由于本系统评价将基于已发表的作品，因此无需伦理批准。本研究的结果将提交至同行评审期刊发表。
PROSPERO注册号：CRD42023481247。
