Dhakal Mani, Singh Brajendra K, Azad Rajeev K
Department of Mathematics, University of North Texas, Denton, TX 76203, USA.
The Preserve at Killian Hill, Lilburn, GA 30047, USA.
Pathogens. 2025 Aug 21;14(8):830. doi: 10.3390/pathogens14080830.
Viral-bacterial co-infections can amplify disease severity through complex biological mechanisms. Mathematical models are critical tools for understanding these threats, but it is unclear how well they capture the underlying biology. This systematic review addresses a central question: to what extent does the current generation of models mechanistically represent co-infections, or do the mathematical assumptions underlying these models adequately represent the known biological mechanisms?
Following PRISMA guidelines, we systematically reviewed the literature on mechanistic models of human virus-bacteria co-infections. A systematic search of articles on the scientific literature repositories PubMed, Scopus, and Dimensions was conducted and data on study objectives, model structure, assumptions about biological interactions (e.g., susceptibility, mortality), control measures (if evaluated), and the empirical sources used for key parameters were extracted.
We identified 72 studies for inclusion in this analysis. The reviewed models are consistently built on the established premise that co-infection alters disease severity and host susceptibility. However, we found they incorporate these dynamics primarily through high-level mathematical shortcuts, such as applying static "multiplicative factors" to transmission or progression rates. Our quantitative analysis also revealed questionable approaches; for example, 79% (57) of these studies relied on non-empirical sources (assumed or borrowed values) for parameter values including interaction parameters (e.g., increased susceptibility to a secondary pathogen following primary infection, or elevated mortality rates in co-infected individuals).
An apparently unjustified practice exists in co-infection modeling, where complex biological processes are simplified to fixed numerical assumptions, often without empirical support. This practice limits the predictive reliability of current models. We identify an urgent need for data-driven parameterization and interdisciplinary collaboration to bridge the gap between biological complexity and modeling practice, thereby enhancing the public health relevance of co-infection modeling.
病毒 - 细菌合并感染可通过复杂的生物学机制加剧疾病严重程度。数学模型是理解这些威胁的关键工具，但尚不清楚它们对潜在生物学机制的捕捉程度如何。本系统评价解决一个核心问题：当前一代模型在多大程度上以机械方式表征合并感染，或者这些模型所依据的数学假设是否充分代表了已知的生物学机制？
遵循PRISMA指南，我们系统评价了关于人类病毒 - 细菌合并感染机械模型的文献。对科学文献数据库PubMed、Scopus和Dimensions上的文章进行了系统检索，并提取了关于研究目标、模型结构、生物相互作用假设（如易感性、死亡率）、控制措施（如已评估）以及关键参数所用经验来源的数据。
我们确定了72项研究纳入本分析。所审查的模型始终基于合并感染会改变疾病严重程度和宿主易感性这一既定前提构建。然而，我们发现它们主要通过高层次的数学捷径纳入这些动态变化，例如对传播或进展速率应用静态“乘数因子”。我们的定量分析还揭示了一些有问题的方法；例如，这些研究中有79%（57项）依赖非经验来源（假设值或借用值）来确定参数值，包括相互作用参数（如初次感染后对继发病原体易感性增加，或合并感染个体死亡率升高）。
在合并感染建模中存在一种明显不合理的做法，即复杂的生物学过程被简化为固定的数值假设，且往往没有经验支持。这种做法限制了当前模型的预测可靠性。我们确定迫切需要数据驱动的参数化和跨学科合作，以弥合生物学复杂性与建模实践之间的差距，从而提高合并感染建模对公共卫生的相关性。
