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Modeling approaches for early warning and monitoring of pandemic situations as well as decision support.

In Frontiers in public health

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.

Botz Jonas, Wang Danqi, Lambert Nicolas, Wagner Nicolas, Génin Marie, Thommes Edward, Madan Sumit, Coudeville Laurent, Fröhlich Holger

2022

agent-based-modeling, artificial intelligence, compartmental models, machine learning, pandemic