Abstract
Evolutionary operations (evops) tries and finds improved industrial processes by exploring the effect of small changes in an experimental setting. It stems from evolutionary algorithms, and uses rules based on biological evolution mechanisms.
In this chapter three subsequent studies of the determinants of infectious disease in eight operation rooms were studied. The effects of humidity, filter capacity change, and airvolume on numbers of infections was assessed. After the previous study, small changes in the experimental settings were made. The meta-data allowed for relevant conclusions about the optimization of infection free operation rooms.
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Reference
More background, theoretical and mathematical information of evops is given in Machine learning in medicine part three, Chap.2, Evolutionary operations, pp 11–18, Springer Heidelberg Germany, 2013, from the same authors.
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Cleophas, T.J., Zwinderman, A.H. (2017). Meta-analysis with Evolutionary Operations (EVOPs). In: Modern Meta-Analysis. Springer, Cham. https://doi.org/10.1007/978-3-319-55895-0_23
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DOI: https://doi.org/10.1007/978-3-319-55895-0_23
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