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Review
. 2022 Nov 14:10:994949.
doi: 10.3389/fpubh.2022.994949. eCollection 2022.

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

Jonas Botz  1 , Danqi Wang  1   2 , Nicolas Lambert  3 , Nicolas Wagner  3 , Marie Génin  3 , Edward Thommes  4 , Sumit Madan  1   5 , Laurent Coudeville  4 , Holger Fröhlich  1   2
Affiliations
Review

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

Jonas Botz et al. Front Public Health. .

Abstract

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.

Keywords: agent-based-modeling; artificial intelligence; compartmental models; machine learning; pandemic.

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Conflict of interest statement

Authors NL, NW, and MG are employees of the commercial company Quinten-Health. Authors ET and LC are employees of the commercial company Sanofi. None of the afore mentioned companies had any influence on the scientific content presented in this paper. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Overview of potential impact areas of population-level computational modeling for increased preparedness against pandemic situations, including relevant data sources.
See this image and copyright information in PMC

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References

    1. William OK, McKendrick AG. A contribution to the mathematical theory of epidemics. Proc R Soc Lond A Math Phys Character. (1927) 115:700–21. 10.1098/rspa.1927.0118 - DOI
    1. Shankar S, Mohakuda SS, Kumar A, Nazneen PS, Yadav AK, Chatterjee K, et al. . Systematic review of predictive mathematical models of COVID-19 epidemic. Med J Armed Forces India. (2021) 77:S385–92. 10.1016/j.mjafi.2021.05.005 - DOI - PMC - PubMed
    1. Dogan O, Tiwari S, Jabbar MA, Guggari S. A systematic review on AI/ML approaches against COVID-19 outbreak. Complex Intell Syst. (2021) 7:2655–78. 10.1007/s40747-021-00424-8 - DOI - PMC - PubMed
    1. Höhle M. Surveillance: an R package for the monitoring of infectious diseases. Comput Stat. (2007) 22:571–82. 10.1007/s00180-007-0074-8 - DOI
    1. Stroup DF, Williamson GD, Herndon JL, Karon JM. Detection of aberrations in the occurrence of notifiable diseases surveillance data. Stat Med. (1989) 8:323–29. 10.1002/sim.4780080312 - DOI - PubMed

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