O'Sullivan Tim, Karakoç Canan, Wollein Waldetoft Kristofer, Brown Sam P
School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA.
Center for Microbial Dynamics and Infection, Georgia Institute of Technology, Atlanta, Georgia, USA.
mSphere. 2025 May 27;10(5):e0095324. doi: 10.1128/msphere.00953-24. Epub 2025 Apr 28.
Infectious diseases remain a major cause of global mortality, yet basic questions concerning the relationship between within-host processes governing pathogen burden (pathogen replication, immune responses) and population-scale (epidemiological) patterns of mortality remain obscure. We use a structured literature review to leverage the extensive biomedical data generated by controlled host infections to address the epidemiological question of whether infection-induced mortality is constant, accelerating, or follows some other pattern of change and to infer the within-host mechanistic basis of this pattern. We show that across diverse lethal infection models, the risk of death increases approximately exponentially in time since infection, in a manner phenomenologically similar to the dynamics of all-cause death. We further show that this pattern of accelerating risk is consistent with multiple alternate mechanisms of pathogen growth and host-pathogen interaction, underlining the limitations of current experimental approaches to connect within-host processes to epidemiological patterns. We review critical experimental questions that our work highlights, requiring additional non-invasive data on pathogen burden throughout the course of infection.IMPORTANCEHere, we ask a simple question: what are the dynamics of pathogen-induced death? Death is a central phenotype in both biomedical and epidemiological infectious disease biology, yet very little work has attempted to link the biomedical focus on pathogen dynamics within a host and the epidemiological focus on populations of infected hosts. To systematically characterize the dynamics of death in controlled animal infections, we analyzed 209 data sets spanning diverse lethal animal infection models. Across experimental models, we find robust support for an accelerating risk of death since the time of infection, contrasting with conventional epidemiological models that assume a constant elevated risk of death. Using math models, we show that multiple processes of growth and virulence are consistent with accelerating risk of death, and we end with a discussion of critical experiments to resolve how within-host biomedical processes map onto epidemiological patterns of disease.
传染病仍然是全球死亡的主要原因，然而，关于病原体负荷（病原体复制、免疫反应）的宿主内过程与人群规模（流行病学）死亡率模式之间的关系等基本问题仍不清楚。我们通过结构化文献综述，利用受控宿主感染产生的大量生物医学数据，来解决感染引起的死亡率是恒定、加速还是遵循其他变化模式这一流行病学问题，并推断这种模式的宿主内机制基础。我们表明，在各种致死性感染模型中，自感染以来死亡风险随时间大致呈指数增加，在现象学上类似于全因死亡的动态变化。我们进一步表明，这种风险加速模式与病原体生长和宿主 - 病原体相互作用的多种替代机制一致，突显了当前将宿主内过程与流行病学模式联系起来的实验方法的局限性。我们回顾了我们的工作所突出的关键实验问题，这些问题需要在感染全过程中获取有关病原体负荷的额外非侵入性数据。
重要性
在此，我们提出一个简单的问题：病原体诱导死亡的动态变化是怎样的？死亡是生物医学和流行病学传染病生物学中的核心表型，但很少有工作尝试将生物医学对宿主内病原体动态的关注与流行病学对受感染宿主群体的关注联系起来。为了系统地表征受控动物感染中死亡的动态变化，我们分析了涵盖各种致死性动物感染模型的209个数据集。在各个实验模型中，我们发现自感染之时起死亡风险加速这一观点得到了有力支持，这与假设死亡风险持续升高的传统流行病学模型形成对比。通过数学模型，我们表明多种生长和毒力过程与死亡风险加速一致，最后我们讨论了关键实验，以解决宿主内生物医学过程如何映射到疾病的流行病学模式上的问题。
