| A) What is emerging infectious disease modelling? Ζήτημα1 #714630 Emerging infectious disease modelling is the use of mathematics and computer methods to explore the transmission of disease and its health outcomes, and the effectiveness of pharmaceutical and nonpharmaceutical interventions. By its synthetic nature, modelling for public health allows the integration of economics and socioeconomics to obtain a broad societal viewpoint. |
- This kind of modelling uses science for action in the public health sphere. Intrinsically it provides synthetic intelligence on the transmission and trajectory of an endemic disease, outbreak or epidemic. Explicitly it assesses the impacts of different public health measures, and it explores and illustrates uncertainty in an open and transparent way.
- Infectious disease modelling for public health purposes is not a simple matter of putting data, mathematics, coding and informatics together—to be effective it demands knowledge of the biological and epidemiological reality, and public health to guide it in the development of “truths” that are “serviceable” for public health policy. “Serviceable truths” are “a state of knowledge that satisfies tests of scientific acceptability and supports reasoned decisionmaking,” while primarily aimed not at furthering scientific research but at providing a basis for action in the face of remaining uncertainty (Jasanoff 2015).
- There is much to be gained from collaboration between modellers in public health and academia. Scientists undertaking disease modelling in public health and in academia have a common goal of providing the “serviceable truths” on which to base public health policies. Contributions from academia include methods, innovations, multimodel approaches, peer-review and enhanced modelling capacity. Contributions from public health modellers include an understanding of public health needs and effective communication to policy makers from public-health-based modellers. Furthermore, a community of academic modellers that is collaborative with, yet independent from public health organisations, ensures objectivity of the science underpinning the modelling. Developing a foundation for sustained collaboration in emerging infectious disease modelling between public health and academia is a key desired outcome of this paper.
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