CC BY 4.0Botz, JonasJonasBotzWang, DanqiDanqiWangLambert, NicolasNicolasLambertWagner, NicolasNicolasWagnerGénin, MarieMarieGéninThommes, EdwardEdwardThommesMadan, SumitSumitMadanCoudeville, LaurentLaurentCoudevilleFröhlich, HolgerHolgerFröhlich2022-11-142022-11-142022https://publica.fraunhofer.de/handle/publica/428664https://doi.org/10.24406/publica-49110.3389/fpubh.2022.99494910.24406/publica-491The 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.enMachine LearningArtificial IntelligenceAgent-Based-ModelingCompartmental ModelsPandemicDDC::600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit::610 Medizin und Gesundheitreview