Scenario: effect of track&trace apps

Similarly to several other countries, the Netherlands is considering employing track and trace apps as part of the exit strategy for the current coronavirus related strategy. Taking a participatory approach, the ministry of Health, Welfare and Sport organised a public tender for the development of track and trace apps that could support the work of the GGD (public health services). Over 700 teams registered their plans for such a app, of which 7 were selected for further development during an ‘appathon’ during the weekend of 18-19 April 2020. The whole initiative was met with much criticism about the process, the quality of the selected apps, and their alignment with fundamental rights, including a open letter to the Dutch government signed by a group of over 180 scientists, co-initiated by members of the ASSOCC team. Based on the results of the simulations described below we gave a presentation (in Dutch) to start a discussion led by the Health Council of The Netherlands that led to an independent advice to the Dutch government.

In this scenario, we focused on analyzing the potential impact of such apps. For the experiments, we assume a perfect app, aligned with all functional, legal and ethical requirements, and we study the effectiveness of such app by performing three experiments:

  1. effect of the app depending on different percentages of the population using the app,
  2. comparing the effect of using the app with that of randomly testing a percentage of the population
  3. effect of the app depending on the characteristics of the users (percentage of risk-avoidance agents that use that app). 
Data breach found in candidate corona app in the Netherlands ...

Scenario description

Our aim is to get insight how high the percentage of app use among the population needs to be for the app to have effect, in comparison with no app use and with the effect of random testing. We have also considered the expected type of users in the case that the use of the app is voluntary. For this, we hypothesize that risk avoidance people are more likely to download such app. At the same time, such agents are also more likely to be more careful and thus already adhering to distancing and isolation recommendations.

Simulation settings / Model configurations

We test the spreading of the coronavirus, using the standard NetLogo model under the following conditions:

  • percentage of app users = (0%, 60%, 80% or 100%)
  • percentage of app users = 0.0 and percentage of population tested randomly daily = (0% or 20%)
  • percentage of app users = 60% and percentage of risk avoidance app users = (0%, 30% or 60%)

See here for a description of the disease and contagion models used.

Results
Experiment 1: Differing amounts of population using the app

Using the app results in a lower infection peak. However the differences are not significant in a test using 15 randomised runs for each setting, as depicted in figure 1 comparing the settings for no app users, 60% app users, 80% app users and 100% app users, with a population of 1000 agents.

Figure 1: Impact of app use on number infected agents

However, as depicted in Figure 2, increasing the number of users results in a sharp increase of testing given that all those that are alerted of being in contact with an infected agent will need to be tested (or required to quarantine themselves).

Figure 2: Amount of agents to be tested under different app use configurations

These results left us with the question how does the usage of the app compare with a similar amount of random testing. This gave the basis for experiment 2.

Experiment 2: comparing tests performed through app with random testing

This experiment shows the difference between random testing and app use. Given the characteristics of the app (alert those who have been in contact with an infected agent when this agent is found to be infected), infection awareness is always a few steps behind the actual contact. Random testing raises infection awareness even when the tested agent had no reason to suspect infection. The differences on number of infected agents under different conditions is shown if Figure 3.

Figure 3: comparing app use with random testing

Figure 4 shows the difference in number of tests.

Figure 4: Amount of tests under different conditions
Experiment 3: effect of the type of app users. 

We hypothesized that the people who are most likely to use the app are probably those that are more risk averse. However, in initial tests, we were not able to see a significant difference under this condition. More experiments are needed, varying the number of risk averse agents in the population.

Figure 5: influence of risk averse agents
Discussion & Conclusion

The above experiments show that the effectiveness of tracking and tracing apps on lowering the number of infected agents is limited and lower than that of random testing. At the same time, the use of the app results in a sharp increase on the number of agents that need to be tested, which may be above the capacity available in the system.

We therefore conclude from this data that the app (with around 60% use) makes no significant contribution to a virus-free Netherlands.


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