Study population and setting
Authors used detailed contact data collected from 468 individuals during the 2017/2018 citizen-science BBC Pandemic Project in Haslemere, Surrey, UK. These high-resolution GPS data were collected to be specifically relevant for infectious disease modelling, and were used to construct a “social network” that represented real-world close contacts that could result in SARS-CoV-2 transmission such as “face-to-face” contacts. COVID-19 outbreaks were simulated using a mathematical model across this social network testing different network structures on how individuals within the network may be connected to each other. Authors allowed factors such as the proportion of asymptomatic individuals and their infectiousness to vary. Four control scenarios and their impact on the epidemic were assessed: 1) no control, where no individuals are isolated or quarantined; 2) case isolation, where individuals isolate when they start having symptoms; 3) primary contact tracing with quarantine, where individuals isolate once they have symptoms and traced contacts are quarantined upon their infector’s symptom onset (after a delay); and 4) secondary contact tracing which is the same as (3) but includes tracing of the contacts of contacts. The model assumed that isolated and quarantined individuals were all isolated for 14 days and explored how well contact tracing was conducted (% of contacts traced) impacted the effect of control. Finally authors explored a range of “test and release” strategies by varying the time from quarantine to being tested, and also looked at two social distancing scenarios whereby individuals either reduce or completely stop contacts with people they spend the least time with (e.g. outside the household) and instead spend this time with frequent contacts (e.g. within the household).
Summary of Main Findings
Compared to an estimated 12% of the Haslemere network being infected after 70 days in an uncontrolled epidemic scenario, case isolation of symptomatic individuals (control scenario 2); primary contact tracing (control scenario 3); and contact tracing contact of contacts (control scenario 4) resulted in a median 9.3%, 9%, and 7.3% of the population being infected respectively. Tracing contacts of contacts led to the greatest reduction in epidemic size, but also the highest proportion (29%) of individuals under quarantine at any one time. All control scenarios reduced the overall size and growth rate of the simulated outbreaks, with more efficient contact tracing (higher % of contacts traced) also leading to smaller outbreaks. Increasing test capacity, allowing more isolated cases and non-infectious contacts to be released from quarantine, led to substantial reductions in the number of quarantined cases but only a very small increase in outbreak size in both contact tracing scenarios. However this test and release strategy required high test capacity. Social distancing measures combined with contact tracing substantially reduced the number of tests required and the number of individuals in quarantine at any one time.
This study uses very detailed app-based social contact data which were not limited by recall bias (often the case for other self-reported contact surveys), or restricted to a certain setting (such as schools or workplaces). By examining transmission dynamics in a social network that better captures real world interactions, authors were able to examine how different intervention methods may impact local epidemics. The study highlights how specific social structures should be considered in the design of control measures.
The social contact data did not include children <13 years and were collected in a small geographic area over a short period of time. Therefore the social network explored here may not extrapolate to larger populations or over longer time periods. A short delay between isolation/quarantine and testing was assumed (48 hours) and so these testing strategies may only apply when there is sufficient testing capacity. Although social distancing was considered this was not in the context of specific strategies such as workplace closures. Therefore conclusions on what would be an effective social distancing intervention cannot be drawn.
This study highlights the value of real-world social contact data in epidemic modelling and the optimization of different control measures. It also shows the potential trade-offs between level of control (tracing contacts of contacts leading to smallest outbreaks), resources required (high testing capacity required), and the feasibility of intervention strategies (results in almost 30% of the population in quarantine).
This review was posted on: 18 July 2020