In malaria epidemiology, interpolation frameworks based on available observations are critical for policy decisions and interpreting disease burden. Updating our understanding of the empirical evidence across different populations, settings, and timeframes is crucial to improving inference for supporting public health. Here, via individual-based modeling, we evaluate a large, multicountry, contemporary Plasmodium falciparum severe malaria dataset to better understand the relationship between prevalence and incidence of malaria pediatric hospitalizations - a proxy of malaria severe outcomes- in East-Africa. We find that life-long exposure dynamics, and subsequent protection patterns in children, substantially determine the likelihood of malaria hospitalizations relative to ongoing prevalence at the population level. Unsteady transmission patterns over a lifetime in children -increasing or decreasing- lead to an exponential relationship of hospitalization rates versus prevalence rather than the asymptotic pattern observed under steady transmission. Addressing this increase in the complexity of malaria epidemiology is crucial to update burden assessments via inference models that guide current and future policy decisions.

在疟疾流行病学中，基于现有观察结果的插值框架对于政策决策和解释疾病负担至关重要。更新我们对不同人群、环境和时间框架的经验证据的理解对于改进支持公共卫生的推论至关重要。在这里，通过基于个人的建模，我们评估了一个大型、多国、当代恶性疟原虫严重疟疾数据集，以更好地了解东非疟疾儿科住院率（疟疾严重后果的代表）的患病率和发病率之间的关系。我们发现，儿童的终生暴露动态以及随后的保护模式在很大程度上决定了相对于人口水平持续患病率而言，疟疾住院的可能性。儿童一生中不稳定的传播模式（增加或减少）导致住院率与患病率呈指数关系，而不是稳定传播下观察到的渐近模式。解决疟疾流行病学复杂性的增加对于通过指导当前和未来政策决策的推理模型更新负担评估至关重要。