Calculate an infection rate as a percentage from new infections and the population at risk, or per 1,000 patient-days or device-days for HAI rates.

Count each newly infected person once within the stated period.

Optional settings
Enter both comparison fields, or leave both blank.

Infection Rate Formula

The population mode calculates an incidence proportion: newly infected people divided by the population initially at risk during a defined period. Person-time mode instead divides new cases by accumulated time at risk. A person-time incidence rate is not a percentage risk. Reporting scales are selectable; the percentage formulas below describe population mode.

IR = (I) / (R) × 100
  • IR = infection rate as a percentage
  • I = number of infections
  • R = number of people at risk of infection

If you know the infection rate and population at risk, you can also solve for the number of infections. If you know infections and the rate, you can solve for the at-risk population.

I = (IR) / (100) × R
R = (I × 100) / (IR)

How to Calculate Infection Rate

  1. Choose a clearly defined time period.
  2. For a population proportion, count distinct newly infected people during that period, once per person.
  3. Count only the people who were actually at risk during that period.
  4. Divide infections by the at-risk population.
  5. Multiply by 100 to convert the result to a percent.

For accurate results, the numerator and denominator must describe the same population and the same timeframe.

Example Calculations

Example 1: If 1,500 infections occurred in a population of 100,000 people at risk, the infection rate is:

IR = (1500) / (100000) × 100 = 1.5%

Example 2: If the infection rate is 4% and the at-risk population is 25,000, the number of infections is:

I = (4) / (100) × 25000 = 1000

Example 3: If 320 infections occurred and the infection rate was 2%, the at-risk population is:

R = (320 × 100) / (2) = 16000

Quick Interpretation Guide

Infection Rate Equivalent Meaning Interpretation
0.1% 1 per 1,000 at risk One in 1,000 people at risk became infected.
1% 10 per 1,000 at risk One in 100 people at risk became infected.
5% 50 per 1,000 at risk One in 20 people at risk became infected.
10% 100 per 1,000 at risk One in 10 people at risk became infected.
25% 250 per 1,000 at risk One out of every four at-risk individuals became infected.

Some users also like to convert the result into infections per 1,000 people for easier communication.

Infections per 1,000 = (I) / (R) × 1000

When to Use This Calculator

  • Comparing infection levels across schools, workplaces, hospitals, or communities
  • Estimating expected infections from a known infection rate
  • Reviewing outbreak summaries for a specific group and time period
  • Expressing raw counts on a common denominator scale; this alone does not adjust for differences in population characteristics
  • Checking whether infection totals are reasonable relative to the exposed population

What Counts as “At Risk”?

The at-risk population should include only people who could realistically become infected during the time period being measured. Using a denominator that is too large will artificially lower the rate, while using one that is too small will inflate it.

  • Include: people exposed, susceptible, or otherwise eligible to become infected
  • Exclude: groups not exposed, not relevant to the event, or outside the study period
  • Be consistent: if infections were measured for one week, the at-risk population should match that same week

Common Input Mistakes

  • Using the total population instead of the population actually at risk
  • Mixing different time periods between infections and population counts
  • Entering a percent as a decimal in one place and a whole number in another
  • Counting infection events when the goal is to count infected individuals
  • Interpreting a count of infections as if it were already a rate

How to Check Your Result

  • If the infection rate is less than 1%, the infected group is a very small fraction of the at-risk population.
  • Check the case definition and at-risk denominator for every result; no universal high-risk threshold is supplied here.
  • A population proportion cannot exceed 100%. A person-time incidence rate can exceed 100 per selected time unit and must not be read as a percentage.

Comparing Groups Correctly

Raw infection counts can be misleading when population sizes differ. Compare matching case definitions and observation periods; the descriptive ratio does not establish causation or adjust for confounding.

Group Infections At-Risk Population Infection Rate
Group A 500 50,000 1%
Group B 200 5,000 4%

Even though Group A has more infections, Group B has the higher infection rate because infections make up a larger share of its at-risk population.

Standardized Infection Ratio (SIR)

Choose Standardized infection ratio (SIR) to divide observed infections by the risk-adjusted predicted infections from an NHSN report. Use counts for the same infection type, population, reporting period and baseline. The predicted count comes from the applicable NHSN model; it is not the population at risk or the number of patient-days.

SIR = observed infections ÷ predicted infections. For example, 8 observed infections and 10 predicted infections give an SIR of 0.8. Values below 1 mean fewer observed infections than predicted; values above 1 mean more, and 1 means the counts are equal. This dimensionless ratio is not an infection rate or percentage risk.

The calculator follows the NHSN minimum of 1 predicted infection and accepts zero observed infections. It does not estimate the predicted count, confidence interval or p-value. Being above or below 1 does not by itself establish statistical significance; use the uncertainty measures in your NHSN report. See the CDC NHSN SIR guide and CDC interpretation guidance.

Useful Notes

  • Population mode can report a percentage or a per-person scale. Person-time mode reports cases per the selected time unit; the unit label does not convert the entered person-time.
  • Enter the reported value at the selected scale: for example, choose per 100 and enter 2.5 for 2.5% in population mode. Reverse calculations may imply fractional counts.
  • Keep rounding consistent when reporting results, especially for very small rates.
  • Rates are most meaningful when the underlying case definition is clear and applied consistently.