Author: Romney B. Duffey; Enrico Zio
Title: Analysing recovery from pandemics by Learning Theory: the case of CoVid-19 Document date: 2020_4_14
ID: mh7mzuoe_61
Snippet: The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.04.10.20060319 doi: medRxiv preprint exposure after the rate has peaked (number of days after peak/day of peak). In relation to the equations (1) and (2) of learning theory above, the infection rate takes the role of the error rate R, the risk exposure time, ε , corresponds to the accumulated experience/knowledge from which we learn and is .....
Document: The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.04.10.20060319 doi: medRxiv preprint exposure after the rate has peaked (number of days after peak/day of peak). In relation to the equations (1) and (2) of learning theory above, the infection rate takes the role of the error rate R, the risk exposure time, ε , corresponds to the accumulated experience/knowledge from which we learn and is measured in days, the time of peak εT, is the time for the rate to approach its achievable minimum value, Rm (the lowest or minimum achievable error rate, in equation (2). Based on the available data for China and S Korea, using countermeasures, the overall recovery timescale is about 20-30 days to attain the minimum infection rate of about 50 per day .
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