Sai Aditya, Kong Nan
a Weldon School of Biomedical Engineering , Purdue University , West Lafayette , IN , USA.
J Biol Dyn. 2018 Dec;12(1):731-745. doi: 10.1080/17513758.2018.1508761.
Sparse grid interpolation is a popular numerical discretization technique for the treatment of high dimensional, multivariate problems. We consider the case of using time-series data to calibrate epidemiological models from both phenomenological and mechanistic perspectives using this computational tool. By capturing the dynamics underlying both global and local spaces, our algorithm identifies potentially optimal regions of the parameter space and directs computational effort towards resolving the dynamics and resulting fits of these regions. We demonstrate how sparse grid interpolants can be effectively deployed to fit available data and discriminate between competing hypotheses to explain the current cholera epidemic in Yemen.
稀疏网格插值是一种用于处理高维多元问题的流行数值离散化技术。我们考虑使用该计算工具从现象学和机理学角度利用时间序列数据校准流行病学模型的情况。通过捕捉全局和局部空间潜在的动态变化，我们的算法识别出参数空间中潜在的最优区域，并将计算资源导向解析这些区域的动态变化及由此产生的拟合情况。我们展示了如何有效地部署稀疏网格插值来拟合现有数据，并区分相互竞争的假设以解释也门当前的霍乱疫情。
