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SUMMARY:Covid and the lockdown effect: semi-parametric inference about inc
 idence and R in England
DTSTART:20220401T151500
DTEND:20220401T164500
DTSTAMP:20260407T042234Z
UID:67d83f3f3fcb81bdfec7577d704b0d1ca1bb782d9af147eafe44e7f5
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Simon Woods\, University of Edinburgh\nThe accepted narr
 ative in the UK has been that only the full Covid lockdowns averted the ca
 tastrophe predicted by epidemic modelling. But a simple spline based decon
 volution applied to daily Covid death data suggested in early May 2020 tha
 t infections were actually in decline days before the first lockdown. Stat
 istical inference based on epidemic models\, told a different story\, of s
 urging infections up until the eve of lockdown (e.g. Flaxman et al. Nature
 ).\n\nHowever that story collapses when the models' rigid parametric assum
 ptions about how R changes are replaced  by something more flexibly data 
 driven.  A later prominent epidemiological modelling  study used a detai
 led age and regionally structured epidemic and health service model to inf
 er R and incidence from multiple data streams on deaths\, hospitalization 
 and testing in England over most of 2020. It also concluded that the first
  2 English lockdowns  were essential to achieving control. Again this res
 ult does not survive replacement of strong parametric assumptions with dat
 a driven semi-parametric alternatives.\n\nThis talk will discuss these res
 ults and the methods permitting inference with the semi-parametric model v
 ersions. In 2021 more direct incidence reconstructions from two major UK s
 tatistical surveillance surveys confirmed the semi-parametric analysis: in
 cidence appears to have been in substantial decline well before all three 
 English lockdowns.\n 
LOCATION:https://epfl.zoom.us/j/68824046325
STATUS:CONFIRMED
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