Key Takeaways

• Nonprobability surveys can effectively track COVID-19 infections when institutional testing is inconsistent or unavailable.
• Survey data provided a reliable estimate of COVID-19 cases even after the widespread use of rapid at-home tests, which were not recorded institutionally.
• Survey results correlated highly with wastewater analysis, suggesting they captured infection trends more accurately after institutional testing was scaled down.
• This approach has implications for equitable health monitoring, especially in communities with limited access to healthcare infrastructure.

Introduction

Monitoring new COVID-19 infections was a critical challenge throughout the pandemic. With institutional testing efforts inconsistent at times and the introduction of rapid at-home tests, public health authorities struggled to capture the true spread of the virus. A recent study published in JAMA Network Open has shown that nonprobability survey data, such as those collected through the COVID States Project, can serve as a vital tool for tracking infections, particularly when traditional testing methods fall short.

Using Surveys to Track COVID-19 Infections

The study conducted by Santillana and colleagues utilized internet-based surveys to estimate COVID-19 case counts across all 50 U.S. states and the District of Columbia from June 2020 to January 2023. The data covered 17 waves of surveys, capturing insights from over 306,799 respondents. Notably, survey data were compared against institutionally reported COVID-19 cases and viral concentrations from wastewater (WW), offering a comprehensive view of their accuracy over different periods of the pandemic.

From April 2020 to January 2022, the survey data closely correlated with official numbers from Johns Hopkins University (JHU), suggesting that nonprobability surveys were reliable in estimating infection rates during the height of institutional testing (Pearson correlation, r = 0.96, P < .001). However, when rapid at-home tests became widely available in January 2022, institutional testing dropped off sharply, leading to substantial underreporting of cases. Despite this, the survey data and wastewater analysis continued to capture the trends effectively, indicating their value in tracking infection spread when institutional efforts were reduced.

Correlation With Wastewater Data

The study highlighted the role of wastewater monitoring as another independent and reliable source for tracking infections. The correlation between survey estimates and wastewater data remained strong both before and after the distribution of at-home tests (r = 0.92 before, and r = 0.89 after January 2022). These findings suggest that survey-based methods, alongside wastewater analysis, provide a more accurate picture of community-level transmission, especially when traditional testing data become unreliable.

The Role of At-Home Tests and Underreporting

The widespread distribution of at-home rapid tests, starting in early 2022, posed significant challenges for public health tracking. Without a centralized reporting system for these tests, official numbers reported by health agencies became much less reliable. The study estimates that approximately 54 million COVID-19 cases went unreported from February 2022 to January 2023 due to the reliance on at-home tests, highlighting a major gap in institutional data.

These findings underline the importance of developing public health systems that can adapt to new testing modalities. Survey data, in particular, could bridge the gap by providing real-time insights into infection trends, especially in the absence of formal test reporting. Such approaches can be particularly beneficial for ensuring that underserved and marginalized communities are accounted for in public health planning, as traditional institutional data often miss those without access to healthcare facilities.

Public Health Implications and Future Directions

1. Integrating Surveys Into Routine Public Health Surveillance

This study supports the integration of nonprobability surveys into routine public health surveillance, particularly during health crises that involve rapid shifts in testing strategies. Surveys provide the flexibility needed to adapt to new circumstances, allowing public health officials to continue monitoring infection rates accurately.

2. Bridging the Gaps in Health Equity

Equitable public health surveillance requires that every community’s infection trends be accurately tracked, including those without easy access to formal healthcare or testing facilities. Surveys, when designed with representative quotas for age, race, and socioeconomic status, can help bridge this gap by ensuring that the data reflect the experiences of all communities. By capturing data from diverse respondents, surveys can also highlight disparities in infection rates and guide targeted interventions to those most at risk.

3. The Importance of Complementary Data Streams

One of the key insights from the study is the importance of combining different data streams—such as survey data, wastewater analysis, and institutional testing. Each method has its limitations, but together they can offer a more complete understanding of public health threats. For example, while surveys are useful for capturing trends when self-testing is common, wastewater data can provide a more objective indicator of viral prevalence in a community, unaffected by individuals’ willingness to report or participate.

Lessons for Future Pandemic Preparedness

The COVID-19 pandemic has underscored the need for a resilient public health infrastructure that can adapt to changes in testing availability and public behavior. The results of this study point to several lessons for future pandemic preparedness:

• Centralized Reporting for At-Home Tests: Developing a streamlined reporting system for at-home test results would improve the reliability of data collected during pandemics. This is crucial for keeping track of infection rates accurately and making informed decisions about public health interventions.
• Utilizing Multiple Data Sources: Public health authorities should integrate survey data with other data streams, such as hospitalizations, syndromic surveillance, and environmental monitoring, to provide a multi-faceted view of disease spread.
• Focusing on Underserved Communities: Ensuring that marginalized populations are included in surveys and other data collection methods is essential for an equitable response. This can help to direct resources and interventions where they are most needed, reducing health disparities.

Conclusion

The study by Santillana and colleagues demonstrates the value of nonprobability surveys as a complementary tool for tracking COVID-19 infections, particularly in situations where traditional institutional testing is limited. By leveraging survey data and integrating it with other forms of monitoring like wastewater analysis, public health officials can obtain a more accurate and inclusive understanding of disease spread. This approach is crucial not only for managing ongoing public health crises but also for building resilient systems that promote health equity and protect vulnerable populations.

As we move forward, investing in diverse and adaptable monitoring systems will be key to managing future health crises effectively, ensuring that everyone—regardless of their location or access to healthcare—can benefit from timely and accurate public health responses.

The image for this article was gathered from Wikimedia Commons.