多波COVID-19纽约市发病率的自适应行为SIR模型的贝叶斯校准
Bayesian calibration of adaptive-behavior SIR models for multi-wave COVID-19 incidence in New York City
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中文总结 AI 辅助
本研究校准四种SIR模型拟合纽约市多波COVID-19数据，发现波浪初始化与行为适应机制显著改善重构，但汇总数据难以分离传播与行为影响。
中文摘要 AI 辅助
流行病发病率既反映传播动态，也反映自适应的人类行为，然而仅凭汇总的病例数据可能难以区分这些机制。我们校准了四种易感-感染-恢复（SIR）模型设定，以拟合2020年6月至12月纽约市每周确诊的COVID-19发病率，比较了单一连续SIR轨迹、波浪初始化SIR模型，以及两种具有共享或波浪特异性传播参数的自适应行为模型。推断采用拒绝近似贝叶斯计算（ABC）方法进行，样本内重构效果通过均方根误差（RMSE）和加权区间得分（WIS）进行评估。按波浪重新初始化疫情状态相比连续SIR轨迹产生了最大的结构性改进，使基于均值的RMSE降低了48.6%，WIS降低了17.6%。加入具有共享传播参数的延迟患病率依赖性行为适应，进一步使基于均值的RMSE降低了23.4%，但相对于波浪初始化SIR模型，WIS基本保持不变。允许传播参数随波浪变化并未提供一致的额外优势，并产生了强烈不对称的后验模拟轨迹。行为敏感性、响应中点和延迟仍仅被弱识别或部分识别。最清晰的后验结构是传播强度与行为中点之间的负相关，表明较高的传播可通过在较低患病率时激活的行为响应得到补偿。对有序行为先验的敏感性进一步表明，重构和行为推断实质上依赖于结构性先验假设。这些结果表明，自适应机制可以改善多波发病率重构，而仅凭汇总发病率不足以清晰区分传播与行为适应。
英文摘要
Epidemic incidence reflects both transmission dynamics and adaptive human behavior, yet these mechanisms may be difficult to distinguish from aggregate case data alone. We calibrated four susceptible--infected--recovered (SIR) specifications to weekly confirmed COVID-19 incidence in New York City from June to December 2020, comparing a single continuous SIR trajectory, a wave-initialized SIR model, and two adaptive-behavior models with either shared or wave-specific transmission. Inference was performed using rejection Approximate Bayesian Computation (ABC), and in-sample reconstruction was assessed using root mean squared error (RMSE) and the weighted interval score (WIS). Reinitializing the epidemic state by wave produced the largest structural improvement over the continuous SIR trajectory, reducing mean-based RMSE by 48.6\% and WIS by 17.6\%. Adding delayed prevalence-dependent behavioral adaptation with shared transmission further reduced mean-based RMSE by 23.4\%, but yielded essentially unchanged WIS relative to the wave-initialized SIR model. Allowing transmission to vary by wave did not provide a consistent additional advantage and produced strongly asymmetric posterior-simulation trajectories. Behavioral sensitivity, response midpoint, and delay remained only weakly to partially identified. The clearest posterior structure was a negative association between transmission intensity and the behavioral midpoint, indicating that higher transmission could be compensated by behavioral responses activated at lower prevalence. Sensitivity to ordered behavioral priors further showed that reconstruction and behavioral inference depend materially on structural prior assumptions. These results suggest that adaptive mechanisms can improve multi-wave incidence reconstruction, while aggregate incidence alone is insufficient to sharply separate transmission from behavioral adaptation.
