Feasibility of Controlling COVID-19 Outbreaks in the UK by Rolling Interventions

Po Yang; Jun Qi; Shuhao Zhang; Gaoshan Bi; Xulong Wang; Yun Yang; Bin Sheng; Xuxin Mao

Notably, two important features in our model differ with other SIR or SEIR models. 12.13 The first one was that we built two direct relationships between Exposed and Recovered population, Infections with mild symptoms and Recovered population. It was based on an observation of COVID-19 breakouts in Wuhan that a large portion (like 42.5% in Wuhan) of self-recovered population were asymptomatic or mild symptomatic. 14 They did not go to hospital for official COVID-19 tests but actually were infected. Without considering this issue, the estimation of total infections were greatly underestimated. 13 In order to measure portion of self-recovery population, we assumed that exposed individuals at home recovered in 3.5 days; mild case at home recovered in 7 days. 19 The second feature was to consider shortage of health sources (hospital beds) in the early breakouts of COVID-19 might lead to more deaths, because some severe or critical cases cannot be accommodated in time and led to death at home (non-hospital). For instance, in Wuhan, taking an immediate suppression intervention on 23 rd Jan 2020 increased serious society anxiety and led to a higher mortality rate. In order to accurately quantify deaths, our modal considered percentage of elder people in the UK at a ratio O, occupancy of available NHS hospital beds over time at a ratios H t and their availability for COVID-19 critical cases at a ratio J t . We assumed that critical cases at non-hospital places led to death in 4 days; elderly people in critical condition at hospital led to death in 14 days, and non-elderly people in critical condition at hospital led to death in 21 days. 19 One parameter was defined to measure intervention intensity over time as M t . which was presented by average number of contacts per person per day. We assumed that transmission ratio β equals to the product of intervention intensity M t and the probability of transmission (b) when exposed (i.e., β= mb). In Wuhan, intervention intensity was assumed within [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] , and gave with a relatively accurate estimation of COVID-19 breakouts. 13 We calibrated its value with respect to the population density and human mobility in London and the UK, and estimated outcomes of COVID-2019 outbreaks by implementing different interventions.