The secondary attack rate (sometimes searched as “secondary action rate”) is a measure used in epidemiology to calculate how easily an infection spreads among people who have been in close contact with someone who is already sick. It’s expressed as a percentage: the number of new cases among contacts divided by the total number of contacts, multiplied by 100. A higher secondary attack rate means the disease spreads more readily in close-contact settings like households, barracks, or schools.
How the Secondary Attack Rate Is Calculated
The formula is straightforward. You take the number of new infections that appear among contacts of a known case and divide it by the total number of people who were exposed. If five people live in a household with one infected person (the “primary case”), the denominator is four, the remaining household members. If two of those four get sick, the secondary attack rate for that household is 50%.
A key detail: the denominator typically subtracts the primary case from the total. You’re measuring how many susceptible people caught the infection, not counting the person who brought it home. In practice, epidemiologists often pool data across many households or closed settings to get a more reliable estimate. The simplest statistical approach models secondary infections in a household as a probability: given a certain number of susceptible people and one primary case, what fraction become infected?
Technically, some researchers prefer calling it the “secondary attack risk” rather than “rate,” since it represents a probability rather than a true rate measured over time. But the term “secondary attack rate” remains the standard in most public health literature.
How It Differs From the Reproduction Number
The secondary attack rate and the basic reproduction number (R0) both describe disease transmission, but they measure different things. R0 estimates how many people, on average, a single infected person will infect in a fully susceptible population. It’s a broad, population-level number. The secondary attack rate, by contrast, zooms in on a specific group of contacts and asks what proportion actually got infected.
R0 can be quite high without the secondary attack rate in a given household being high, or vice versa. During a hospital outbreak of SARS-CoV-2, for instance, the secondary attack rate on individual wards ranged from 3% to 50%, while the overall reproduction number shifted dramatically between early and late phases of the outbreak. The two metrics complement each other: R0 tells you about a pathogen’s broad spreading potential, while the secondary attack rate tells you what happens in the specific settings where people actually interact.
Real-World Numbers for Common Diseases
Secondary attack rates vary enormously depending on the disease and the circumstances. During the COVID-19 pandemic, these rates became one of the most closely watched metrics. Household secondary attack rates for the Delta variant of SARS-CoV-2 were roughly 26% to 58%, depending on the study population and vaccination coverage. When Omicron replaced Delta, those numbers climbed sharply. One study in Spain found the household secondary attack rate jumped from 58% during Delta dominance to 81% during Omicron dominance. A separate Spanish study put the overall secondary attack rate at 39% for Omicron compared with 26% for Delta, a roughly 50% increase.
For seasonal influenza, household secondary attack rates typically fall in the range of 5% to 15%, considerably lower than most SARS-CoV-2 variants. Measles, one of the most contagious diseases known, has historically shown secondary attack rates above 90% in susceptible household contacts. These benchmarks help public health teams quickly gauge how aggressively a new pathogen spreads compared to familiar threats.
What Drives the Rate Up or Down
Several factors influence how high a secondary attack rate climbs in any given setting. On the pathogen side, the amount of virus a person sheds and how long they’re infectious both matter. Omicron’s high secondary attack rate has been partly attributed to the concentration of contagion events during the presymptomatic period, when infected people don’t yet know they’re sick and aren’t isolating. One study found that half of Omicron transmission events occurred before symptom onset.
Environmental conditions play a major role as well. Overcrowding, poor ventilation, and prolonged close contact all push the rate higher. This is why diseases that spread through respiratory droplets hit hardest in households, schools, prisons, and refugee camps. Conversely, improving airflow and reducing the time people spend in close quarters can meaningfully lower transmission.
Vaccination status matters too, though its effect can vary by variant. During the Delta wave, unvaccinated household contacts were more than five times as likely to become infected as vaccinated contacts. But that protective effect faded over time and largely disappeared with Omicron, where vaccinated and unvaccinated index cases appeared to transmit at similar rates.
Why Superspreading Complicates the Picture
One important caveat about secondary attack rates: they represent an average, and averages can be misleading when transmission is highly uneven. With SARS-CoV-2, roughly 10% of infected people were responsible for about 80% of all transmission. This pattern, called overdispersion, means most infected people spread the virus to few or no others, while a small number cause large clusters.
Epidemiologists use a value called the dispersion parameter (k) to quantify this unevenness. A low k, around 0.1 for SARS-CoV-2, indicates extreme variation in how many people each case infects. When k is low, a single secondary attack rate for a population can mask the reality that most households see zero secondary cases while a few see explosive spread. This distinction has practical consequences: public health strategies that target large, non-repetitive gatherings (where superspreading is most likely) are far more effective when overdispersion is high than strategies focused on regular, repeated contacts like workplaces.
How Public Health Teams Use This Metric
During outbreak investigations, the secondary attack rate serves as a rapid, practical tool. It helps officials compare how contagious a new variant is relative to its predecessors, evaluate whether vaccines or other interventions are reducing transmission, and decide how aggressively to implement quarantine and contact tracing.
When Spain’s contact tracing system detected that the Omicron secondary attack rate in social settings nearly doubled compared with Delta (from about 16% to 31%), it signaled that existing containment strategies were falling behind. The finding that half of Omicron transmission happened before symptoms appeared also explained why traditional containment measures, isolating symptomatic people and tracing their contacts, were struggling to keep up.
At a more local level, comparing secondary attack rates between households, schools, and workplaces helps direct resources where they’ll have the most impact. A high household rate combined with a lower workplace rate might argue for better home ventilation guidance and rapid testing for household contacts rather than broad workplace closures.