Simulating the use of digital contact tracing to control COVID-19

Previously, we showed in our Science paper how a digital contact tracing smartphone app could be an effective method for achieving COVID-19 epidemic control. We demonstrated that it can provide a practical, feasible and ethical method for scaling-up traditional contact tracing to match the fast pace of COVID-19 spread, while targeting quarantine measures to those at risk in order to stop the spread of the disease. We have now developed an individual-based epidemic simulation which enables epidemiologists, app designers and policy makers to compare a variety of algorithm configurations for digital contact tracing under a range of assumptions about the epidemic, the technology, a country’s demographics, and user engagement (see our report). We identified a safe and effective starting configuration for the UK that can be subsequently optimised to prevent transmission while minimising the number of people in isolation. Here we give a brief overview of the simulation and our key results.

The simulation considers a “model city” of 1 million people whose ages and contact patterns have been calibrated to UK demographics. All of our parameters are openly documented and modifiable so that they can be adapted to fit other countries’ data, and refined to match our understanding of COVID-19 as the epidemic progresses. The computer model simulates people moving around between their homes, workplaces, schools, and random social gatherings. It allows us to fast-forward through an epidemic to consider what happens when some of these people are infected. Crucially, it should not be thought of as a precise forecast but as a means of comparing the effectiveness of interventions. For example, we might find that interventions A and B result in similarly low numbers of infections but B involves quarantining fewer people.

The model should not be thought of as a precise forecast but as a means of comparing the effectiveness of interventions.

The spread of COVID-19 in the simulation is determined by a collection of parameters including the type of interaction (household interactions are more likely to result in spreading an infection than workplace interactions), the infectiousness of the transmitter, and the susceptibility of the recipient. An individual’s susceptibility, severity of infection and infectiousness are dependent on their age, as are their probabilities of being hospitalised, requiring critical care, and either dying or recovering.

A brief summary of current UK government advice for COVID-19 is 7 days of self-isolation for symptomatic individuals, 14 days for their household, and persisting isolation for individuals over the age of 70. We apply these instructions in our simulation, taking into account realistic levels of adherence and a daily dropout rate for people giving up on self-isolating. Against this backdrop we test the effectiveness of a national lockdown and of different algorithms for digital contact tracing being introduced at the end of the lockdown. 

When we model app-based interventions we do not assume that everyone would use a digital contact tracing smartphone app. Although our user acceptance survey indicated broad support and uptake, we also tested the impact of low uptake. Using OFCOM data on UK smartphone ownership in each age group, we ran our simulation with varying uptake of 0-80% amongst those who actually own a suitable smartphone: uptake of 80% is shown by the blue bars in the plot below.

We consider six key variations on digital contact tracing which are summarised in the diagram below. These range from the scenario where there is no contact tracing app through scenarios with app implementations which vary in their levels of contact tracing applied, their “release mechanisms” which allow individuals to end their self-isolation earlier, and their use of testing, clinical diagnosis or self-reporting for the identification of cases. The full details can be found in our report.

Our results show that digital contact tracing can have a profound, life-saving impact on the progression of COVID-19. We quantify its impact on numbers of new infections, hospitalisations, ICU admissions and deaths, as well as the number of people in quarantine and the number of tests required each day. We consider six scenarios and test 20 variations on our key assumptions, plus varying levels of app-uptake - our full set of results can be found in our report. Here we’ll focus on describing the impacts of the six scenarios on numbers of new infections when using our best-estimate parameters and varying the percentage of app uptake.

The graphs below correspond to the five variations on app implementations. In each graph, the first vertical dashed line corresponds to the start of the UK lockdown and the second line is for the end of lockdown in our model, 35 days later. The height of the coloured lines shows the size of the peak of new infections if there are no further lockdowns, with different levels of app uptake (dark blue for 0%, yellow for 80%). This demonstrates the power of digital contact tracing in limiting the number of infections. We show that any use of digital contact tracing has an overall protective effect on the population, including those without smartphones, and that this effect is stronger the more people use the app. With high app uptake it is plausible that a second lockdown would not be required. This is based on our estimate that the first UK lockdown happened when around 1% of the population were infected: some of the interventions we model prevent infection levels getting that high again. 

Our model is open-source so that everyone can use it. Policy-makers can calibrate it to their country’s demographics and decide which scenario produces the most tolerable trade-off between numbers of infected people and number quarantined, taking into account factors like the availability of tests, anticipated app uptake, and existing interventions. It is worth repeating that these results should not be thought of as a precise forecast but as a means of comparing the effectiveness and impact of interventions. Different scenarios may be suitable within the same country at different stages of the epidemic or as post-intervention data emerges or if the situation changes, for example if widespread testing becomes available. It is therefore essential to design digital contact tracing apps so that their underlying algorithms can be updated at any time to better respond to current epidemiological analysis.

by Michelle Kendall

The icons used in our diagram are taken from Font Awesome and licensed under the Creative Commons Attribution 4.0 International license, which can be found here.