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. 2020 Mar;41(1):39-51.
doi: 10.1057/s41271-019-00206-0.

Disease modeling for public health: added value, challenges, and institutional constraints

Affiliations

Disease modeling for public health: added value, challenges, and institutional constraints

Mirjam Kretzschmar. J Public Health Policy. 2020 Mar.

Abstract

Public health policymakers face increasingly complex questions and decisions and need to deal with an increasing quantity of data and information. For policy advisors to make use of scientific evidence and to assess available intervention options effectively and therefore indirectly for those deciding on and implementing public health policies, mathematical modeling has proven to be a useful tool. In some areas, the use of mathematical modeling for public health policy support has become standard practice at various levels of decision-making. To make use of this tool effectively within public health organizations, it is necessary to provide good infrastructure and ensure close collaboration between modelers and policymakers. Based on experience from a national public health institute, we discuss the strategic requirements for good modeling practice for public health. For modeling to be of maximal value for a public health institute, the organization and budgeting of mathematical modeling should be transparent, and a long-term strategy for how to position and develop mathematical modeling should be in place.

Keywords: Infrastructure; Mathematical model; Policy support; Public health.

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Figures

Fig. 1
Fig. 1
Applications of mathematical modeling
Fig. 2
Fig. 2
Life cycle of a model. The phases in Fig. 2 are not necessarily occurring in exactly that order in time. Publication can take place at several points in the cycle. The first publication could be on the theoretical framework of the model that can be before validation using real data. Later on, applied analyses using a validated version of the model can also be peer-reviewed and published

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