Contact Tracing Behavior: Why is it unlikely that citizens will install the contact tracing app on their own?

Uptake of contact tracing app is a strategic interaction between Public Health Officials and Citizens because of the social costs associated with COVID19

Image via Manish Singh / TechCrunch

By: Isha Jain and Nishan Gantayat

As we continue to tackle the COVID-19 crisis, the importance of a robust monitoring strategy becomes necessary for India to reopen. Marred with a big population and weak public health system, the involvement of several stakeholders with multiple objectives has supplemented the problem. In the absence of health resources required for contact tracing, the uptake of technology can serve as an important social signal to facilitate contact tracing and reduce the burden on public health. Thus, emerges the need for a monitoring strategy that accounts for human behavior.

The low success of India’s contact tracing application (“Aarogya Setu”) can be attributed to the general stigma associated with the disease and the app’s privacy concerns. There are concerns regarding data usage and government surveillance, the changing privacy guidelines have not helped its cause. From the authority side, there are no strong rules or incentives for app installation apart from few employers guiding their employees to use it. The requirement of having the application for railway and flight travel is also not uniform across states.

There are two primary stakeholders in this interaction — the local administrators and citizens, each with different preference sets. The citizen has private information regarding their behavior which creates an asymmetry of information between them and the administrator. This gap in information makes it an interesting case to explore their preference sets and design incentives for cooperative behavior.

Let’s model app installation behavior

The citizen is at home, with the self-assessed information of whether they are “High-risk” or “Low-risk” individuals. High-risk individuals display symptoms for COVID-19 or have taken risks such as travel abroad, met someone with the disease, or not practiced social distancing. Low-risk citizens have taken adequate care to prevent infection. Each type is faced with the decision of whether to install the application or not. They decide based on the effort (cost) associated with downloading it and how morally responsible they feel. And use it as a signal to intimate their risk status.

Figure: The Game and the payoffs

Local administrators do not know whether they are dealing with a high-risk individual or a low-risk individual. They have to decide whether to test or not-test the citizen after they have seen the citizen’s signal regarding their installation status and deciphered this signal based on their beliefs and preferences.

The payoffs presented are indicative of the preferences of the players with respect to the various outcomes. And each of them has different costs associated with their actions.

Citizen Costs: Stigma, institutional quarantine, expected physiological pain, anticipated pain from testing, and anticipated infringement of privacy.

Administrator Costs: Anticipated public health cost, cost of judging the signal which carries the risk of false positives and service availability, or false negatives and infection spread, and direct cost of testing.

What is the best response?

Given the beliefs and the preference settings and performing a Bayesian analysis, we see that the best response for the citizen is “Does not install contact tracing app” irrespective of whether they are High-risk or Low-risk, to the best response set of the local administration.

Figure: The Best Response

The best response set is described below:

  • Citizen: chooses not to install the app with probability 1 when they have engaged in risky behavior and use the same signal when they have engaged in “safe” behavior.
  • Local Administration: if they see the app installed, they believe the type of individual they are interacting with is risky but are not sure and choose to “test” with probability 1.
  • Local Administration: if they don’t see the app installed, then they are half sure that they might be interacting with a safe individual and choose “no test” with probability 1.

The absence of a discriminating signal creates a situation where High-risk and Low-risk individuals get involved in mimicking. This compels the local administration to evaluate this mimicking and giving way to the possibility that they might choose not to test individuals who are potential risks.

Implications: change the payoffs

For contact tracing applications to serve their purpose, the game must move to the desired state by altering preferences and reducing associated costs. Messaging and norms can play an important role in course correction, some of them include:

Build trust and reduce fear: Creating visible cues such as asking for only a mobile phone number as opposed to personal identifiers such as gender or age could lead to the individuals being more comfortable with sharing information. Countries such as New Zealand led a “national conversation” asking ordinary people what made them comfortable about sharing their data.

Appeal to minority groups: These groups have to tackle existing stigma as well as that of the disease. The migrants and at-risk populations who do not want to reveal their identities or location would go to great lengths to avoid sharing health data. Community engagement is recognized as being a key vehicle for achieving social license.

Increase shared interest and coordination: Promoting messaging and subtle norms regarding social interdependence and awareness of collective agency help in creating a sense of collective action, which has worked in countries such as Taiwan. The installation of the application could increase through two routes: creating norms around everyone installing the application or imposing indirect community-based sanctions against those who don’t.

Build trust with the system: In order to encourage initial adoption, local administrators could increase using a combination of physical and digital contact tracing where it is a best practice is to have a contact tracer that’s as close as possible to the community they’re interacting with. This would allow for rapport and empathy, and in turn, improve quarantine adherence.

Contact tracing plays a key role in re-opening India. Examples such as the West Africa Ebola prove that although contact-tracing efforts couldn’t keep up with the disease initially, it eventually became a key factor in ending the outbreak. Keeping in mind a resurgence of COVID-19, future pandemics, or even commonplace communicable diseases, it is important to optimize this technique. As the world builds preparedness for future outbreaks, it is worth considering the costs each stakeholder has to incur and the various strategic interactions that pan out in the healthcare sector.

Final Mile brings unique and proven capabilities in addressing complex behavioral challenges. As one of the first Behavioral Science & Design consultancies, Final Mile has had the opportunity to bring these to practice in a wide variety of sectors and contexts. We have executed highly complex behavior change projects across a wide variety of areas covering Global Health (HIV, TB, Maternal Health, WASH), Financial Inclusion, Safety across Africa, Asia, Europe, and the US.

Final Mile is also building a pandemic playbook that can be used as a potential toolkit by policymakers and implementors in mitigating Covid19 and future such pandemics.

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