On the contact tracing for COVID-19: A simulation study

Abstract

Background: Contact tracing is one of the most effective non-pharmaceutical interventions in the COVID-19 pandemic. This study uses a multi-agent model to investigate the impact of four types of contact tracing strategies to prevent the spread of COVID-19.

Methods: In order to analyse individual contact tracing in a reasonably realistic setup, we construct an agent-based model of a small municipality with about 60.000 inhabitants (nodes) and about 2.8 million social contacts (edges) in 30 different layers. Those layers reflect demographic, geographic, sociological and other patterns of the TTWA (Travel-to-work-area) Hodonín in Czechia. Various data sources such as census, land register, transport data or data reflecting the shopping behaviour, were employed to meet this purpose. On this multi-graph structure we run a modified SEIR model of the COVID-19 dynamics. The parameters of the model are calibrated on data from the outbreak in the Czech Republic in the period March to June 2020. The simplest type of contact tracing follows just the family, the second tracing version tracks the family and all the work contacts, the third type finds all contacts with the family, work contacts and friends (leisure activities). The last one is a complete (digital) tracing capable of recalling any and all contacts. We evaluate the performance of these contact tracing strategies in four different environments. First, we consider an environment without any contact restrictions (benchmark); second with strict contact restriction (replicating the stringent non-pharmaceutical interventions employed in Czechia in the spring 2020); third environment, where the measures were substantially relaxed, and, finally an environment with weak contact restrictions and superspreader events (replicating the situation in Czechia in the summer 2020).

Findings: There are four main findings in our paper. 1. In general, local closures are more effective than any type of tracing. 2. In an environment with strict contact restrictions there are only small differences among the four contact tracing strategies. 3. In an environment with relaxed contact restrictions the effectiveness of the tracing strategies differs substantially. 4. In the presence of superspreader events only complete contact tracing can stop the epidemic.

Interpretation: In situations, where many other non-pharmaceutical interventions are in place, the specific extent of contact tracing may not have a large influence on their effectiveness. In a more relaxed setting with few contact restrictions and larger events the effectiveness of contact tracing depends heavily on their extent.

Keywords: Agent-based model; Epidemiological model; Network model; Non-pharmaceutical interventions.

Fig. 1

The fit of our model to the situation in the Czech Republic. Left: detected active cases. Centre: all infected individuals. Right top: Average times between the first symptoms and the test result. Right middle: Number of all tests (should be always underestimated in a model, we do not realize all tests, since we do not care about negative ones except those in quarantine). Right bottom: Ratio of all active cases and detected active cases. Number 1.0 stands for all detected, $>$1.0 undetected ill nodes.

Fig. 2

Comparison of different contact tracing strategies in environment $S$ (contacts restricted following first 60 days of the Czech scenario). Attack rate (top-left), $R_t$ (top-right), detected individuals (bottom-left), all actually infected individuals (bottom-right). The $x$-axis shows simulation days, the $y$-axis shows number of individuals, median values and interquartile ranges are plotted.

Fig. 3

Comparison of different contact tracing strategies in environment $B$ without any contact restrictions. Attack rate (top-left), $R_t$ (top-right), detected individuals (bottom-left), all actually infected individuals (bottom-right). The $x$-axis shows simulation days, the $y$-axis shows number of individuals, median values and interquartile ranges are plotted.

Fig. 4

Comparison of different contact tracing strategies for the environment $M$. Attack rate (top-left), $R_t$ (top-right), detected individuals (bottom-left), all actually infected individuals (bottom-right). The $x$-axis shows simulation days, the $y$-axis shows number of individuals, median values and interquartile ranges are plotted.

Fig. 5

Comparison of different contact tracing strategies for environment $W$ with weekly superspreader event. Attack rate (top-left), $R_t$ (top-right), detected individuals (bottom-left), all actually infected individuals (bottom-right). The $x$-axis shows simulation days, the $y$-axis shows number of individuals, median values and interquartile ranges are plotted.