Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany.
Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, Bonn, Germany.
Front Public Health. 2022 Nov 14;10:994949. doi: 10.3389/fpubh.2022.994949. eCollection 2022.
The COVID-19 pandemic has highlighted the lack of preparedness of many healthcare systems against pandemic situations. In response, many population-level computational modeling approaches have been proposed for predicting outbreaks, spatiotemporally forecasting disease spread, and assessing as well as predicting the effectiveness of (non-) pharmaceutical interventions. However, in several countries, these modeling efforts have only limited impact on governmental decision-making so far. In light of this situation, the review aims to provide a critical review of existing modeling approaches and to discuss the potential for future developments.
COVID-19 大流行凸显了许多医疗体系在应对大流行情况方面准备不足。有鉴于此，已经提出了许多基于人群的计算模型方法，用于预测疫情爆发、时空预测疾病传播、评估和预测（非）药物干预措施的效果。然而，到目前为止，这些建模工作在一些国家对政府决策的影响有限。有鉴于此，本综述旨在对现有的建模方法进行批判性评估，并讨论未来发展的潜力。
