Simple smooth additive models for the observed death-with-COVID-19 series adequately capture the underlying death rate and strong weekly pattern in the data. Clear inference about peak timing is then possible. Further, inference about the earlier infection rate dynamics driving the death rate dynamics can be treated as a simple Bayesian inverse problem, which can be readily solved by imposing a smoothness assumption on the infection rate. This straightforward semi-parametric approach is substantially better founded than the running mean smoothers which generally form the basis for public debate. In the absence of direct statistically based measurement of infection rates, it also offers a usefully assumption-light approach to data analysis, for comparison with the results of the more assumption-rich process simulation models used to inform policy. An interesting result of the analysis is that it suggests that the number of new daily infections in the UK peaked some days before lock down was implemented, although it does not completely rule out a slightly laterpeak .
The number of new infections per day is a key quantity for effective epidemic management. It can be estimated by testing of random population samples. Without such direct epidemiological measurement, other approaches are required to infer whether the number of new cases is likely to be increasing or decreasing: for example, estimating the pathogen reproductive rate, R, using data gathered from the clinical response to the disease. For COVID-19 such R estimation is heavily dependent on modelling assumptions, because the available clinical case data are opportunistic observational data subject to severe temporal confounding. Given this difficulty it is useful to reconstruct the time course of infections from the least compromised available data, using minimal prior assumptions. A Bayesian inverse problem approach applied to UK data on COVID-19 deaths and the published disease duration distribution suggests that infections were in decline before UK lockdown, and that infections in Sweden started to decline only a short time later .
R
Simple smooth additive models for the observed death-with-COVID-19 series adequately capture the underlying death rate and strong weekly pattern in the data. Clear inference about peak timing is then possible. Further, inference about the earlier infection rate dynamics driving the death rate dynamics can be treated as a simple Bayesian inverse problem, which can be readily solved by imposing a smoothness assumption on the infection rate. This straightforward semi-parametric approach is substantially better founded than the running mean smoothers which generally form the basis for public debate. In the absence of direct statistically based measurement of infection rates, it also offers a usefully assumption-light approach to data analysis, for comparison with the results of the more assumption-rich process simulation models used to inform policy. An interesting result of the analysis is that it suggests that the number of new daily infections in the UK peaked some days before lock down was implemented, although it does not completely rule out a slightly laterpeak
The number of new infections per day is a key quantity for effective epidemic management. It can be estimated by testing of random population samples. Without such direct epidemiological measurement, other approaches are required to infer whether the number of new cases is likely to be increasing or decreasing: for example, estimating the pathogen reproductive rate, R, using data gathered from the clinical response to the disease. For COVID-19 such R estimation is heavily dependent on modelling assumptions, because the available clinical case data are opportunistic observational data subject to severe temporal confounding. Given this difficulty it is useful to reconstruct the time course of infections from the least compromised available data, using minimal prior assumptions. A Bayesian inverse problem approach applied to UK data on COVID-19 deaths and the published disease duration distribution suggests that infections were in decline before UK lockdown, and that infections in Sweden started to decline only a short time later