Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts

Hellewell, Joel; Abbott, Sam; Gimma, Amy; Bosse, Nikos I; Jarvis, Christopher I; Russell, Timothy W; Munday, James D; Kucharski, Adam J; Edmunds, W John; Funk, Sebastian; Eggo, Rosalind M

The number of initial cases had a large effect on the probability of achieving control. With five initial cases, there was a greater than 50% chance of achieving control in 3 months, even at modest contact-tracing levels For code see https://github.com/ cmmid/ringbp (figure 4). More than 40% of these outbreaks were controlled with no contact tracing because of the combined effects of isolation of symptomatic cases and stochastic extinction. The probability of control dropped as the number of initial cases increased-eg, for 40 initial cases, 80% contact tracing did not lead to 80% of simulations controlled within 3 months. The delay from symptom onset to isolation had a major role in achieving control of outbreaks ( figure 4) . At 80% of contacts traced, the probability of achieving control fell from 89% to 31%, with a long delay from onset to isolation. If no transmission occurred before symptom onset, then the probability of achieving control was higher for all values of contacts traced ( figure 4) . The difference between 15% and 30% of transmission before symptoms had a marked effect on probability to control. We found this effect in all scenarios tested (appendix p 5). In scenarios in which only 10% of cases were asymptomatic, the probability that simulations were controlled by isolation and contact tracing for all values of contact tracing decreased ( figure 4) . For 80% of contacts traced, only 37% of outbreaks were controlled, compared with 89% without subclinical infection. These figures show the effect of changing one model assumption at a time; all combinations are given in the appendix, in comparison to the baseline scenario (appendix pp 2-5).