Discussion
Having a clear understanding of the historic recovery in the community is a critical piece of information to policymakers as higher levels mitigate the impact associated with relaxing the social constraints. A published piece of work not yet reviewed shows serology results from 1/4/2020 carried out on 3,300 people in Santa Clara California that show 40-80 times as many people in the community have had the disease than was reported by their testing program (14)
The analysis shown in Figure 2 highlights that current lockdown measures are reducing the daily R-value down to well below one. However, to commence relaxing these measures, we suggest several principles need to be in place to ensure the R-value of COVID-19 does not rise above 1, triggering a second pandemic (there is general acceptance that the disease will inevitably become endemic).
Figure 3 highlights how the disease progression varies across UTLAs and how that impacts the infection rate and its relative speed of change. Regions with history of the most cases/population have the lowest infection rate RADIR and lowest rate of change in infection rate ΔIR.
Social distancing behaviour and rules implementation could be expected to vary across different communities/groups, and as the different UTLAs have varying amounts of these different communities, examining the variation of infection rate across UTLAs one would hope to see which community groups were responding well and which were responding less well to social distancing. Figure 4 shows the only factor that could be related to the RADIR in this analysis was the historic number of confirmed number infection/,000 population suggesting that some of the reduction in reported cases is due to the build-up of immunity due to larger numbers of historic cases in the population.
An important comparative R-value reference would be another coronavirus endemic infection, influenza. During seasonal periods, research indicates that influenza has an R-value of around 1.3 (15) and can result in the highest periods up to 200 additional deaths per day above mortality from other causes, although these figures are constrained by the provision of flu vaccine which is available particularly for the high-risk group. However, if the current pandemic can be switched to a similar mortality rate (with carefully phases social behaviour policies in place, along with population testing) then unlocking can be managed in a politically and socially acceptable way. Some observations around this included
  1. The principle of self-isolation following infection/symptoms is now well in place in the population
  2. The track and contain mechanisms to identify next line contacts of infected people can also be increased with technical support
  3. The vulnerable groups can continue to be isolated with their 13-week restriction kept in place but supported by the general population
  4. Health service is now better able to cope with the load
Adding to this, experiences with different pandemic policy frameworks suggest that a looser more flexible approach to social activity can be managed if high-risk groups are more carefully protected. This is particularly pertinent given the news this week that elderly care homes are a significant area of both infections and pandemic mortality (16).
The speed of the unlocking process will depend on the level of unlocking. However, what is clear is that with the potentially reduced at-risk population any further peaks will be lower. We looked in UTLAs at the potential determinants of the ADIR and found that the only factor that related to this was the historic number of confirmed number infection/,000 population. This suggests that removing the lockdown from areas with higher historic caseloads should present a lower risk of R-value reversal.
However, a ‘one size fits all’ approach to pandemic policy does not consider the variation in both infection rates and impact across localities. When the data at the regional level is analysed there seems to be a wide variety of R-values and slope of extrapolated R-line over time, implying that unlocking needs to have a certain level of ‘tailoring’ of social behavioural policies and testing to be effective. These differences are likely to be due to differences in local factors such as infection drivers and underlying population morbidities. This has been explored in a separate publication by the same authors (17).