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Scenario-driven forecasting: modeling peaks and paths. Insights from the COVID-19 pandemic in Belgium
Scientometrics ( IF 3.9 ) Pub Date : 2020-07-13 , DOI: 10.1007/s11192-020-03591-6
Kristof Decock 1, 2 , Koenraad Debackere 1 , Anne-Mieke Vandamme 3, 4 , Bart Van Looy 1, 2
Affiliation  

The recent ‘outburst’ of COVID-19 spurred efforts to model and forecast its diffusion patterns, either in terms of infections, people in need of medical assistance (ICU occupation) or casualties. Forecasting patterns and their implied end states remains cumbersome when few (stochastic) data points are available during the early stage of diffusion processes. Extrapolations based on compounded growth rates do not account for inflection points nor end-states. In order to remedy this situation, we advance a set of heuristics which combine forecasting and scenario thinking. Inspired by scenario thinking we allow for a broad range of end states (and their implied growth dynamics, parameters) which are consecutively being assessed in terms of how well they coincide with actual observations. When applying this approach to the diffusion of COVID-19, it becomes clear that combining potential end states with unfolding trajectories provides a better-informed decision space as short term predictions are accurate, while a portfolio of different end states informs the long view. The creation of such a decision space requires temporal distance. Only to the extent that one refrains from incorporating more recent data, more plausible end states become visible. Such dynamic approach also allows one to assess the potential effects of mitigating measures. As such, our contribution implies a plea for dynamically blending forecasting algorithms and scenario-oriented thinking, rather than conceiving them as substitutes or complements.

中文翻译:

情景驱动的预测:建模峰值和路径。来自比利时 COVID-19 大流行的见解

最近 COVID-19 的“爆发”促使人们努力对其扩散模式进行建模和预测,无论是在感染、需要医疗援助的人(ICU 职业)还是伤亡方面。当在扩散过程的早期阶段可用的(随机)数据点很少时,预测模式及其隐含的最终状态仍然很麻烦。基于复合增长率的推断不考虑拐点或最终状态。为了纠正这种情况,我们提出了一套结合预测和情景思考的启发式方法。受情景思维的启发,我们允许广泛的最终状态(及其隐含的增长动态、参数)根据它们与实际观察的吻合程度进行连续评估。在将这种方法应用于 COVID-19 的传播时,很明显,将潜在的最终状态与展开的轨迹相结合提供了一个更明智的决策空间,因为短期预测是准确的,而不同最终状态的组合则为长远观点提供了信息。创建这样一个决策空间需要时间距离。只有在避免合并最新数据的情况下,更合理的最终状态才会变得可见。这种动态方法还允许人们评估缓解措施的潜在影响。因此,我们的贡献意味着动态融合预测算法和面向场景的思维,而不是将它们视为替代品或补充品。而不同最终状态的组合则体现了长远的眼光。创建这样一个决策空间需要时间距离。只有在避免合并最新数据的情况下,更合理的最终状态才会变得可见。这种动态方法还允许人们评估缓解措施的潜在影响。因此,我们的贡献意味着动态融合预测算法和面向场景的思维,而不是将它们视为替代品或补充品。而不同最终状态的组合则体现了长远的眼光。创建这样一个决策空间需要时间距离。只有在避免合并最新数据的情况下,更合理的最终状态才会变得可见。这种动态方法还允许人们评估缓解措施的潜在影响。因此,我们的贡献意味着动态融合预测算法和面向场景的思维,而不是将它们视为替代品或补充品。
更新日期:2020-07-13
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