时变SEIR模型的校准
Calibration of Time-Varying SEIR Models
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中文总结 AI 辅助
针对时变SEIR模型，提出直接前向求解器LAD校准方法，以应对报告异常下的LSQ高杠杆问题；蒙特卡洛与埃博拉情景验证显示LAD在多数情况下更稳健，并提供了可复现工作流。
中文摘要 AI 辅助
机制性流行病校准可能对短期报告异常敏感，这些异常在最小二乘法（LSQ）下赋予少数观测值高杠杆。我们开发了直接前向求解器最小绝对偏差（LAD）校准方法，用于拟合报告间隔发病率的时变易感-暴露-感染-移除模型，并具备有限时域正则性和大样本结果。一项阶段感知的蒙特卡洛研究在三种传播驱动因素、四个流行病阶段、五种报告机制和四个预测时域下比较了LAD和LSQ。所有120,000次初步拟合均收敛。总体而言，在平均绝对误差比较中LAD占优56.7%，在加权区间得分比较中占优57.9%；在孤立尖峰、积压释放或临时低报情况下，这些比例上升至73.6%和72.2%，且在流行高峰附近有显著增益。对四个合成RAPIDD埃博拉情景的滚动原点验证也识别了LSQ更受青睐的设置，展示了该框架区分稳健性增益与持续趋势行为的能力。多种不确定性和可识别性诊断、完整代码和数据以及一个交互式应用提供了可复现的计算工作流程。
英文摘要
Mechanistic epidemic calibration can be sensitive to short-lived reporting anomalies that give a few observations high leverage under least squares (LSQ). We develop direct forward-solver least absolute deviations (LAD) calibration for time-varying susceptible-exposed-infectious-removed models fitted to reporting-interval incidence, with finite-horizon regularity and large-sample results. A phase-aware Monte Carlo study compares LAD and LSQ across three transmission drivers, four epidemic phases, five reporting mechanisms, and four forecast horizons. All 120,000 primary fits converged. LAD was favored in 56.7% of mean-absolute-error comparisons and 57.9% of weighted-interval-score comparisons overall; under isolated spikes, backlog release, or temporary underreporting, these proportions rose to 73.6% and 72.2%, with strong gains near the epidemic peak. Rolling-origin validation on four synthetic RAPIDD Ebola scenarios also identifies settings where LSQ is preferred, demonstrating the framework's ability to distinguish robustness gains from sustained-trend behavior. Multiple uncertainty and identifiability diagnostics, complete code and data, and an interactive application provide a reproducible computational workflow.
发表机构
- Department of Mathematics and Statistics, Georgia State University（佐治亚州立大学数学与统计系）
机构由 AI 辅助整理，请以论文原文为准。
