With reference to Adam Kucharski's book the Rules of Contagion (page 58) formulates that R is a function of Duration of infection, Opportunities to spread, Transmission probability and Susceptibility. This appears to be reasonable however with a little thought it can be seen that a clear definition needs to be given to each. For example the transmission probability is likely to vary depending on the nature of the opportunity face to face indoors, face to face outdoors, infection left on a surface etc. All the variables in the DOTS function are incredibly difficult to establish accurately (using either real or modelled data). Since the Ro value is similarly constrained it is difficult to see how it is so accurately reported. In real life statistics would be used to try and obtain acceptable values. The current COVID testing data does not seem to imply that we have enough meaningful data to do this. I assume that if we had a reliable source of up to date data the R value could be calculated, but this does not appear to be the case. It is not in the Scientific interest to pretend that Science can always be the saviour. Again to reference Adam Krucharski's book (page 154) 'According to Chris Whitty, now Their Medical Officer for England, the best mathematical models are not necessarily the ones that try to make the most accurate forecast about the future. What matters is having analysis that can reveal gaps in our understanding of a situation. 'They are generally most useful when they identify impacts of policy decisions which are not predictable by commonsense,' Whitty has suggested. 'The key is usually not that they are "right", but that they provide an unpredicted insight. I have worked in the modelling environment in the Aircraft industry and have seen FEM models used successfully however I have also seen models that can only predict very simple senerios.
With reference to Adam Kucharski's book the Rules of Contagion (page 58) formulates that R is a function of Duration of infection, Opportunities to spread, Transmission probability and Susceptibility. This appears to be reasonable however with a little thought it can be seen that a clear definition needs to be given to each. For example the transmission probability is likely to vary depending on the nature of the opportunity face to face indoors, face to face outdoors, infection left on a surface etc. All the variables in the DOTS function are incredibly difficult to establish accurately (using either real or modelled data). Since the Ro value is similarly constrained it is difficult to see how it is so accurately reported. In real life statistics would be used to try and obtain acceptable values. The current COVID testing data does not seem to imply that we have enough meaningful data to do this. I assume that if we had a reliable source of up to date data the R value could be calculated, but this does not appear to be the case. It is not in the Scientific interest to pretend that Science can always be the saviour. Again to reference Adam Krucharski's book (page 154) 'According to Chris Whitty, now Their Medical Officer for England, the best mathematical models are not necessarily the ones that try to make the most accurate forecast about the future. What matters is having analysis that can reveal gaps in our understanding of a situation. 'They are generally most useful when they identify impacts of policy decisions which are not predictable by commonsense,' Whitty has suggested. 'The key is usually not that they are "right", but that they provide an unpredicted insight. I have worked in the modelling environment in the Aircraft industry and have seen FEM models used successfully however I have also seen models that can only predict very simple senerios.