Just to comment on a few points that have come up in relation to the preprint we put out yesterday on VOC 202012/01, the new variant of SARS-CoV-2 in the UK. (
Is the apparent spread of VOC due to increased testing? This comes up often when cases rise & indeed case data is subject to biases. But we don't fit to case data in the model. Hospitalisations, deaths, and relative frequency (not abs. number) of the new variant define the trend.
There are other ways of measuring community prevalence besides case data. For example, the ONS just released a new round of estimates based on swabbing random people in England. Also shows increases in prevalence in the 3 regions we highlight to Dec 18. ons.gov.uk/peoplepopulati…
Can we avoid closing schools? Model would say yes, provided transmission can be reduced in other sectors to compensate.
It's also possible that contact patterns will drop more in Tier 4 than they did during the November lockdown, which could help keep more schools open. Early data from mobility indices (like Google's Community Mobility) will help scientists to assess this.
I suspect UK scientists will be asked soon to look at different policy options in much greater detail than we were able to do here. This will give others and ourselves a chance to check our preliminary findings against new data coming in.
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Late last week, it was announced that a new variant of SARS-CoV-2 (VOC 202012/01) was detected and appeared to be spreading rapidly in the south east of England. We analysed the transmissibility and severity of this new variant. [cmmid.github.io/topics/covid19…] NOT PEER REVIEWED 1/9
We fitted a mathematical model to the growth of VOC 202012/01 in these three regions of England. If current trends continue, the new variant could represent 90% of cases by mid-January. NOT PEER REVIEWED 2/9
We estimate the transmission rate of this variant is 50–74% higher than existing variants; no clear evidence it leads to higher rates of hospitalisation or death. Although rates of both appear slightly higher in the SE, this could easily be noise in the data. NOT PEER REVIEWED 3/