代理辅助策略（传染病病原体模型的参数化）
Surrogate Assisted Strategies (The Parameterisation of an Infectious Disease Agent-Based Model)
摘要：
参数校准是基于主体的建模与仿真（ABMS）中的一个重要挑战。基于代理的模型（ABM）的复杂性随着需要校准的参数数量的增加而增加。该参数扩展导致ABMS等同于\say{curse of dimensionality}。特别是，不可行的计算要求搜索一个无限的参数空间。我们提出了一个更全面和自适应的ABMS框架，可以有效地交换出参数化策略和代理模型参数化的传染病ABM。该框架允许我们评估不同的策略-代理组合在准确性和效率（加速比）方面的性能。我们表明，我们实现了更好的替代辅助抽样策略和基线的准确性比平价。此外，我们发现，与支持向量机替代相结合的度量随机响应面策略在最接近真实合成参数方面是最好的。此外，我们表明，动态坐标搜索使用响应面模型与XGBoost作为代理相结合，达到近似累积合成每日感染数据分布的最高概率，并实现了最显着的加速与我们的分析。最后，我们在现实世界的设置，DYCORS XGBoost和MSRS SVM可以近似真实世界的累积每日感染分布与$97.12$ \%和$96.75$ \%的相似性。
英文摘要：
Parameter calibration is a significant challenge in agent-based modelling and simulation (ABMS). An agent-based model's (ABM) complexity grows as the number of parameters required to be calibrated increases. This parameter expansion leads to the ABMS equivalent of the \say{curse of dimensionality}. In particular, infeasible computational requirements searching an infinite parameter space. We propose a more comprehensive and adaptive ABMS Framework that can effectively swap out parameterisation strategies and surrogate models to parameterise an infectious disease ABM. This framework allows us to evaluate different strategy-surrogate combinations' performance in accuracy and efficiency (speedup). We show that we achieve better than parity in accuracy across the surrogate assisted sampling strategies and the baselines. Also, we identify that the Metric Stochastic Response Surface strategy combined with the Support Vector Machine surrogate is the best overall in getting closest to the true synthetic parameters. Also, we show that DYnamic COOrdindate Search Using Response Surface Models with XGBoost as a surrogate attains in combination the highest probability of approximating a cumulative synthetic daily infection data distribution and achieves the most significant speedup with regards to our analysis. Lastly, we show in a real-world setting that DYCORS XGBoost and MSRS SVM can approximate the real world cumulative daily infection distribution with $97.12$\% and $96.75$\% similarity respectively.
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