Weekly healthcare activity data are typically non-negative counts with temporal dependence and occasional system-wide disruptions, settings in which Gaussian time-series models may be inadequate. Solid organ transplant (SOT) activity provides a representative case study of a count process affected by a large external shock. We analyse weekly SOT counts in the USA and Italy from 2014 to October 2024, stratified by donor type (deceased vs living) and organ (kidney and liver). We fit Poisson and negative-binomial count time-series models incorporating short-term dynamics, calendar effects (holiday weeks), and pre-specified pandemic-period level and/or slope indicators. Candidate specifications are screened within a pre-defined portfolio and selected using BIC within each training window. Forecasting performance is evaluated with an expanding-window design at horizons $h\in\{4,8,12\}$ weeks. Alongside RMSE, we report empirical coverage of nominal $95\%$ predictive intervals and interval widths to summarise calibration and forecast uncertainty. Across strata, selected models capture substantial pandemic-period deviations and varying post-period trajectories. Deceased-donor series are broadly consistent with a return towards pre-pandemic baselines in both countries, whereas the US living-donor series shows a more gradual convergence in this application. Within the explored model class and validation protocol, auxiliary covariates representing COVID burden and mortality add limited incremental predictive contribution beyond autoregressive and calendar components. Our analysis shows that donation time series represent an unconditional phenomenon, with auxiliary variables having a statistically negligible impact on donations, thus allowing a focus on more practical aspects related to ongoing challenges in the post-pandemic era, such as hospital overloads and changes in public perception.

翻译：每周医疗活动数据通常呈现非负计数特征，具有时间依赖性且偶发系统性中断，传统高斯时间序列模型在此类场景中可能失效。实体器官移植活动作为受重大外部冲击影响的计数过程典型案例，本研究分析了2014年至2024年10月期间美国与意大利按捐赠类型（死亡供体与活体供体）及器官类型（肾脏与肝脏）分层的周度移植计数数据。我们构建了包含短期动态、日历效应（节假日周）及预设疫情期水平/斜率指标的泊松与负二项计数时间序列模型。候选模型在预设组合中筛选，并通过各训练窗口内的贝叶斯信息准则进行选择。采用滚动窗口设计在预测跨度h∈{4,8,12}周评估模型预测性能。除均方根误差外，还报告名义95%预测区间的经验覆盖率和区间宽度，以综合评价校准精度与预测不确定性。分层分析显示，选定模型能有效捕捉疫情期显著偏差及疫情后各异的变化轨迹。两国死亡供体序列整体呈现向疫情前基线回归的趋势，而美国活体供体序列在本应用中表现为更渐进的收敛过程。在所探索的模型类别与验证框架内，代表疫情负担与死亡率的辅助协变量相较于自回归成分和日历成分，仅能提供有限的增量预测贡献。研究表明捐赠时间序列呈现无条件特征，辅助变量对捐赠行为的统计学影响可忽略不计，从而可将研究聚焦于后疫情时代持续面临的实践问题，如医院负荷过重与公众认知变迁。