The positivity rate, or percent positive, is the percentage of all coronavirus tests performed that are positive, divided by the total number of tests administered and multiplied by 100.

The availability of at-home test kits, which has led to a rapid increase in testing in the United States, has raised new challenges for interpreting the percent of positive COVID-19 cases. Because individuals are not required to report the results of home tests to public health agencies, public health officials cannot track the number of tests administered or the results.

When discussing the COVID-19 positivity rate, it is important to document the sample size and composition to better understand what the positivity rate represents. Caution is needed when using percent positive as a metric as it is prone to selection bias; those who feel sick, are worried about being infected, or those already hospitalized might be the ones who are tested, thus skewing the percent positive higher. Alternatively, increased screening of asymptomatic individuals (e.g., for travel or social functions) may result in more negative reported results. This metric, therefore, needs to be understood in the context of how much testing is being conducted in a community and who is being tested.

In the early months of the pandemic, hospitalization data was available quickly, but reflected only the most severe cases of infection and patients who were exposed to the virus weeks before admission. Increasingly, a distinction is being made between patients who are admitted for COVID-19 and those who are admitted with COVID-19. While hospitalization data can indicate community transmission, the spread of newer variants with different characteristics might mean they do not always accurately reflect virus severity.

The strain placed on hospitals and the health care sector in general has been of great concern since the onset of the pandemic. When considering whether to implement or roll back COVID-19 protections, the burden on the health care infrastructure is an important consideration, as past surges have stressed hospital systems and negatively affected health care and public health infrastructures. As part of the CDC’s Science Brief on Monitoring COVID-19 Community Levels, new COVID-19 admissions and the percent of staffed inpatient beds occupied indicate strain on the health care system. Factors such as the availability of regular beds, intensive care unit beds, staff, supplies, equipment, and finances at hospitals in a state or region signals the ability of hospitals to provide adequate and appropriate services to all patients in need.

These data are considered lagging indicators, rising and falling behind the trends in positive cases and hospitalizations. However, they are an important factor when evaluating how the pandemic has affected certain population sub-groups. As such, when making decisions at the state and local levels, it is important to consider reported confirmed COVID-19 deaths in relation to other measures in order to have a more contextualized view of the virus’ trajectory as these data are an indicator of the burden of the pandemic.

Tracking overall vaccination rates and “up to date” vaccination rates will likely be important for making decisions at the state and local levels going forward as new variants emerge and vaccine-induced immunity wanes. It will also be important to disaggregate vaccination rate data to better understand the level of protection within different populations.

COVID-19 vaccination coverage can inform community actions regarding vaccine outreach, campaigns, distribution, and equity, which in turn can inform local prevention decisions. Local information, such as community vaccination coverage and surveillance testing, can help inform decision making at the state and local levels.

Seroprevalence surveys use antibody tests to estimate the percentage of people in a population who have antibodies against COVID-19. They provide estimates of how many people in a specific population, at different points in time, or in different locations, may have been previously infected with COVID-19. Seroprevalence measures are helpful in that estimates can be made by testing a sample of the population rather than the entire population. The CDC has been leading a variety of seroprevalence surveys.

However, caution is needed when interpreting seroprevalence survey results, as these surveys are not based on a representative sample of the population. An ongoing infection survey based on a random sample of the national population, similar to the United Kingdom’s Office of National Statistics infection survey, would avoid these biases and improve seroprevalence-monitoring capabilities.

The CDC established the National Wastewater Surveillance System in September 2020 to track the presence of COVID-19 in wastewater samples collected across the country. This is a community-level approach that monitors sewers for the presence of SARS-CoV-2 in human urine and feces. Because this method can detect disease in individuals who are asymptomatic, pre-symptomatic, and symptomatic, it may be an early indicator of changes in COVID-19 trends. Wastewater surveillance is a targeted approach that is cost- and time-efficient because it pools a large population into one sample and does not depend on individuals getting tested. Conducting wastewater surveillance is uneven throughout the United States. Partnerships at state and local levels have established wastewater surveillance programs, such as the Sewershed Surveillance Project in Missouri. Wastewater surveillance has also been used on college campuses and in correctional facilities to identify cases early and control the spread of COVID-19.

Wastewater surveillance can also be a powerful tool in identifying coronavirus mutations and potential variants. However, many factors can influence the concentration of SARS-CoV-2 present in wastewater, which can impact the usefulness of the wastewater data. There is a need for further research to identify, quantify, and adjust for these factors.

Genome sequencing analyzes virus samples to better understand how the virus mutates, transmits, and spreads. In addition to tracking the spread of a virus, genomic sequencing is also used for detecting new variants and monitoring trends in already circulating variants. Genomic sequencing and surveillance is used to conduct surveillance and monitor genomic changes over time. For example, researchers in South Africa and Botswana were able to detect the beta and delta variants in December 2020 and May 2021, respectively, using genomic surveillance which allowed the Departments of Health to quickly take action.

Genome sequencing capacity has expanded in the United States through efforts by federal agencies, state and local public health laboratories, academic institutions, corporations, non-profit public health and research laboratories, and international collaboration.

Additionally, wastewater surveillance can be used in tandem with genome sequencing to identify mutations and track variants.

Conducting real-time analyses of disease data is often complicated by the lag in data reporting. Nowcasting methods are used for disease surveillance by estimating the number of occurred-but-not-yet-reported events, such as the prevalence of COVID-19 variants and the size of outbreaks and trends at the state and local levels.