COVID-19 Detection Tools Raise Issues About Privacy in Public Health-Related Monitoring

Screenshot of the disease visualization tool illustrating how infection hotspots have developed and resolved over time

Effectively combating the COVID-19 pandemic depends on the detection and containment of concentrated areas of infection as well as intervention efforts that prevent widespread community transmission. To that end, the U.S. government is adopting technologies to support the public health response to the COVID-19 pandemic and to increase national preparedness for infectious diseases more broadly. A key component of disease outbreak management is timely and targeted information on the incident location, affected persons, infection transmission time, and other associated disease risk factors. 

Public health activities such as COVID-19 case identification, contract tracing, and vaccination implementation performed across an entire jurisdiction can be a daunting task, especially with limited resources. In response, researchers at the Centers for Disease Control and Prevention have developed a tool for use by public health officials that enables hotspot detection of COVID-19 outbreaks at the granular level. That is, pinpointing specific residential areas with critical COVID-19 concern. Using the tool – whose documentation was released June 30, 2022 – public health authorities can tailor their efforts to a particular neighborhood or congregate living facility instead of a whole zip code area. 

As pictured above, this tool is an interactive, real-time, street-level visualization of when and where COVID-19 cases are developing and resolving. The algorithmically-defined alerts generated can trigger government responses to public health threats and disease outbreaks in a timely and targeted fashion. In addition to displaying COVID-19 hotspots, the automated tool generates contextually rich illustrations of patterns of change and notable trends across the course of the pandemic. For example, it analyzes inputted data to create a representation of the distribution of sociodemographic and socioeconomic characteristics among COVID-19 outcomes in specific geographic areas. Social determinants like race and income are factors that collectively influence the health of an individual and inequities in the provision of societal resources can have disproportionate impacts on certain populations/people groups . On that account, the sociodemographic and socioeconomic characteristics of COVID-19 hotspots provided serve as critical contextual knowledge that might help to inform the types of public health measures developed by government officials. The tool provides government officials with predictive power when it comes to assessing future pandemic-related risks and needs. With these capabilities, it has the potential to become distinctly influential in high-impact decisions made by public health officials. However, in the event of a malfunction, offloading a considerable amount of decision making to the tool could lead stakeholders astray and cost them precious time and resources. 

Given the prolonged nature of the COVID-19 pandemic and our uncertainty with what the future holds, the tool represents a step toward adapting to the challenges of our new reality. In fact, COVID-19 could very well become a standard seasonal disease just like the flu. The CDC anticipates that the tool will not only prove useful for the public health department with respect to the COVID-19 pandemic but eventually to other public health threats including chronic diseases as well. 

Source data including patient demographics (age, race, ethnicity, sex), street address of residence, test results, and test date from regional public health systems informs this tool. Sociodemographic and socioeconomic context was generated from the Census Bureau’s decennial census and the American Community Survey’s data and details on the prevalence of chronic diseases was derived from county-level data from the Behavioral Risk Factor Surveillance System. While rapid and informative, the tool is largely restricted by publicly available sources of data that do not provide information at smaller units of geography. Inconsistencies in government reporting of statistics and the inaccessibility of useful datasets further intensifies the challenges in quality and consistency of data collection that are used to inform these systems. 

While imperfect datasets and selection bias raise questions about the accuracy of this tool, it is worth noting that tracking infection at this intended level of granularity presents privacy and confidentiality concerns. This highlights how the utility of this tool must be balanced with appropriate accountability. During the Ebola outbreaks in West Africa in 2014 to 2016, personal phone data was used to monitor individual’s mobility and behaviors. There is potential for COVID-19 tools to also tend toward the questionable usage of personal data in monitoring individual mobility, social-distancing measures, and quarantine adherence.

Enabling the creation and legitimization of government surveillance tools during a period of national emergency can present both benefits and risks. Although useful in the present time, these tools are likely to persist after the pandemic for non-emergency purposes. State governments could, for instance, duplicate and deploy their own versions of the tool to track other types of health data that is of interest to them. Instead of monitoring COVID cases, the tool’s automated capabilities might be leveraged to track, for instance, pregnancy cases. In a state with anti-abortion laws, the tool’s use would have serious implications for women. Detection tools open up potential avenues for government analyses of health that may drastically improve existing measures. But in doing so, they raise serious concerns over privacy rights.

As tools like these continue to evolve over the course of the pandemic, the ongoing collection of data from private citizens merits rigorous investigation and routine checks of these digital technologies. Increasingly accurate epidemiological models that have the power to predict future outcomes require legal and ethical considerations, greater transparency, and meaningful public participation, knowledge, and scrutiny. 

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