Enter the testing data below to calculate the percentage of positive results relative to total tests performed.
The number of tests that returned a positive result.
The total number of samples collected and processed.
Positivity Rate
0.00%
How to Calculate Positivity Rate
The Positivity Rate (or percent positive) is a critical epidemiological metric used to understand the spread of a disease within a community and the adequacy of testing efforts. It represents the percentage of all tests performed that are actually positive.
The Positivity Rate Formula
Calculating the positivity rate involves a straightforward division of the number of positive cases by the total number of tests conducted, multiplied by 100 to get a percentage.
Positivity Rate (%) = ( Total Positive Tests ÷ Total Tests Performed ) × 100
Example Calculation
Let's look at a realistic scenario to understand the math:
Scenario: A laboratory processes 5,000 tests in a week.
Results: Out of those samples, 250 return a positive result.
Calculation: (250 ÷ 5,000) = 0.05
Conversion: 0.05 × 100 = 5.0%
In this example, the positivity rate is 5%.
Why is Positivity Rate Important?
The positivity rate gives public health officials two key pieces of information:
Transmission Levels: A high positivity rate suggests that the virus is spreading rapidly within the community.
Testing Adequacy: If the rate is very high, it likely means that only the sickest people are getting tested, and there are not enough tests being conducted to capture asymptomatic or mild cases. A lower rate generally indicates sufficient surveillance.
Interpreting the Thresholds
While specific thresholds vary by disease and health organization guidelines (such as the WHO or CDC), general interpretations often follow these tiers:
Below 5%: Often considered a target threshold indicating that the spread is under control or testing is widespread and adequate.
5% to 10%: Indicates moderate spread; caution is advised, and testing may need to be ramped up.
Above 10%: Suggests high community transmission and insufficient testing capacity relative to the outbreak size.
Factors Affecting Accuracy
When calculating positivity rates, it is important to consider data consistency. Are "Total Tests" counting the number of people tested or the number of samples processed? Are repeat tests for the same individual included? Consistent data reporting is essential for tracking trends over time accurately.