Mathematical modelling and analysis for the co-infection of viral and bacterial diseases: a systematic review
Timothy Kiprono Yano1, Ebenezer Afrifa-Yamoah2, Julia Collins2
1School of Science, Edith Cowan University, Perth, Western Australia, Australia t.yano@ecu.edu.au.
Introduction:
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.
Methods And Analysis:
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 And Dissemination:
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.
Prospero Registration Number:
CRD42023481247.
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