Frequently Asked Questions

Your likelihood of a Covid infection is estimated by combining epidemiological data with your individual test results using principles from probability and Bayes’ theorem. Here’s how the calculation works in different scenarios:

1. Initial Risk Based on Symptoms and Local Conditions

  • For Symptomatic Users: If you report having symptoms, the app sets your initial risk equal to the local positivity rate (default is 15%). This is because a high positivity rate among those being tested suggests a higher probability that your symptoms are due to Covid.
  • For Asymptomatic Users: When you do not have symptoms, we adjust the risk downward. The app uses the local Covid prevalence (default is 1%) and factors in that about 32% of currently infected individuals are non-symptomatic (see this meta-analysis). This means that if the overall prevalence is low, your chance of infection without symptoms is even lower.

2. Adjustment for Covid-Cautious Behavior

Your behavior also influences your risk. The app classifies Covid-related behavior into five distinct levels that not only describe the precautions you take but also quantify their impact on your everyday risk of infection relative to a typical American. This framework is designed to guide you in understanding how varying degrees of caution can significantly alter your risk profile.

  • Not at all: You take no special Covid precautions and live life as if the pandemic is over. Your everyday risk of getting infected is about the same as a typical American's.
  • Slightly: You take only minor precautions occasionally—masking or testing in special situations—but mostly live as if the pandemic is over. Your everyday risk of getting infected is about 20% less than a typical American's.
  • Moderately: You regularly employ some Covid precautions (like masking indoors and testing) while still engaging in most social and work activities. Your everyday risk of getting infected is about 50% of a typical American's.
  • Very: You maintain extensive Covid safety measures at all times—such as always wearing a high-quality mask and avoiding most indoor exposures—significantly altering your daily life. Your everyday risk of getting infected is about 10% of a typical American's.
  • Extremely: You adopt a near-zero risk tolerance for Covid, living in semi-isolation with maximal precautions—e.g., respirator-level masking and minimal in-person interaction—to completely minimize your exposure. Your everyday risk of getting infected is about 1% of a typical American's.

Note: If you do not find any of these predefined risk categories or operationalizations appropriate for your situation, you can manually enter your own prior probability in the Advanced settings.

3. Updating Risk with Test Results Using Bayes’ Theorem

For every test you enter, the app updates your risk using Bayes’ theorem. Each test has its own sensitivity (true positive rate) and specificity (true negative rate), which may differ based on whether you have symptoms.

  • Positive Test Result: Posterior Risk = (sensitivity * prior risk) / (sensitivity * prior risk + (1 - specificity) * (1 - prior risk))
  • Negative Test Result: Posterior Risk = ((1 - sensitivity) * prior risk) / ((1 - sensitivity) * prior risk + specificity * (1 - prior risk))

4. Advanced Settings: Incorporating Local Covid Prevalence and Positivity Rate

The advanced mode lets you adjust the local Covid prevalence and positivity rate. By default, these are assumed to be 1% and 15%, respectively—typical values throughout the year. Adjusting these values allows the risk calculations to reflect seasonal and regional variations in Covid spread.

Additionally, you can manually set your own prior probability in the advanced mode. If you do so, the app will not adjust the risk for symptom status, local prevalence, positivity rate, or your degree of Covid-cautiousness; only your test results update the risk using Bayes’ theorem.

The sensitivity of a test is the probability that the test is positive if the person is infected. The specificity of a test is the probability that the test is negative if the person is not infected. As the table below shows, the sensitivity and specificity of a given test can depend (sometimes dramatically) on whether it is administered to someone who is symptomatic or asymptomatic.

The table below presents performance data for various tests during the Omicron period specifically. This is because test sensitivities—especially those of RATs (Rapid Antigen Tests)—dramatically decreased with the emergence of Omicron. So, earlier testing data—e.g., data from the FDA in 2020-2021—has become much less relevant.

Test Symptomatic Sensitivity
(95% CI)		 Symptomatic Specificity
(95% CI)		 Asymptomatic Sensitivity
(95% CI)		 Asymptomatic Specificity
(95% CI)		 Notes
BinaxNOW 74.0%
(66.8%–80.4%)		 99.2%
(95.4%–99.9%)		 50.0%
(40.9%–59.1%)		 99.4%
(97.7%–99.9%)
  • This study found that BinaxNOW detected infections in 64 people out of 121 PCR-positive samples that included 113 asymptomatic people and 8 people whose symptoms started more than 7 days prior—i.e., a sensitivity of 52.5% (95% CI: 43.2%–61.6%) in this combined group. The study does not disaggregate sensitivity among the two sub-groups. However, even if BinaxNOW detected infections among all of the late-infection symptomatic individuals (whom the study noted had relatively low viral load), it would still have a sensitivity of at least 46.3% (95% CI: 40.5%–58.6%) for asymptomatic individuals. So, I think it’s reasonable to treat BinaxNOW having a sensitivity of about 50% for asymptomatic people (for which I calculate an adjusted 95% CI of 40.9%–59.1%).
CorDx (Covid & Flu) 89.1%
(81.9%–93.6%)		 99.8%
(99.1%–100%)		 25.2%
(21.5%–29.3%)		 99.8%
(99.3%–100)
  • FDA symptomatic testing data
  • I couldn’t find any data on CorDx (Covid & Flu)’s sensitivity or specificity for asymptomatic people during Omicron, so I assumed it was typical of RATs in the Omicron period. (See “Other RAT” row.)
FlowFlex (Covid-only) 84.7%
(79.7%–88.6%)		 99.8%
(99.3%–100%)		 27.5%
(21.3%–34.3%)		 99.8%
(99.3%–100%)
FlowFlex Plus (Covid & Flu) 90.6%
(85.4%–94.4%)		 99.3%
(98.2%–99.8%)		 27.5%
(21.3%–34.3%)		 99.8%
(99.3%–100%)
  • FDA symptomatic testing data
  • I couldn’t find data on FlowFlex Plus’s sensitivity or specificity for asymptomatic people during Omicron, but I assumed it’s the same as the Covid-only FlowFlex test.
iHealth (Covid-only) 72.5%
(63.6%–80.3%)		 98.4%
(96.8%–99.2%)		 25.2%
(21.5%–29.3%)		 99.8%
(99.6%–100%)
  • Symptomatic testing data
  • While the above study measured a specificity of 100.0% (95% CI: 89.1–100.0%), it was only based on a small sample of 32 PCR-negative tests. The actual specificity is likely very close to 100%. So, I will assume it is the same as the iHealth (Covid & Flu) test (see below).
  • I couldn’t find any data on iHealth (Covid-only)’s sensitivity or specificity for asymptomatic people during Omicron, so I assumed it was typical of RATs in the Omicron period. (See “Other RAT” row.)
iHealth (Covid & Flu) 84.2%
(75.6%–90.2%)		 98.4%
(96.8%–99.2%)		 25.2%
(21.5%–29.3%)		 99.8%
(99.6%–100%)
  • FDA symptomatic testing data
  • I couldn’t find any data on iHealth (Covid & Flu)’s sensitivity or specificity for asymptomatic people during Omicron, so I assumed it was typical of RATs in the Omicron period. (See “Other RAT” row.)
Lucira 88.3%
(80.2%–93.3%)		 99.9%
(99.6%–100%)		 84.3%
(76.2%–89.3%)		 98.2%
(95.5%–99.3%)
  • FDA symptomatic testing data
  • Although the linked study measured a specificity of 100% (95% CI: 99.6%–100%), no test is perfectly specific, so I am using 99.9% as the point estimate for its specificity.

    Additionally, although there is no published data on Lucira’s sensitivity or specificity for asymptomatic people in the Omicron era, the FDA study of the original Lucira “Check It” test (long since discontinued) found that the sensitivity for symptomatic infections was 94.1% (95% CI: 84.1%–98.0%) but only 90.1% (95% CI: 81.7%–94.9%) for asymptomatic infections. So, in line with this reduction, I am somewhat artificially reducing Lucira’s sensitivity by about 4 percentage points. The old study found a specificity of 98.2% (95% CI: 95.5%–99.3%) for asymptomatic people, so I am using that for the specificity for asymptomatic people.
Metrix 95.0%
(83.5%–98.6%)		 97.1%
(85.5%–99.5%)		 94.0%
(84.5%–100%)		 99.2%
(97.2%–99.8%)
  • FDA testing data
  • Although Metrix had a measured sensitivity of 100% among asymptomatic people in its FDA study, that study only involved 21 infected asymptomatic people. So, on purely statistical grounds, it is very unlikely that Metrix’s sensitivity for asymptomatic people is actually 100%. Moreover, there is evidence that the viral load in asymptomatic people tends to be lower than in symptomatic people. So, theoretically, infections should be more difficult to detect in asymptomatic people than in symptomatic people. For this reason, I am somewhat artificially using 94% as my point estimate of Metrix’s sensitivity for asymptomatic people. However, interested folks can still get interval-valued risk estimates (using the upper and lower bounds of the CIs) by clicking the “More info” button after calculating risk.
OSOM (Covid & Flu) 59.7%
(51.6%–67.4%)		 99.1%
(97.9%–99.6%)		 25.2%
(21.5%–29.3%)		 99.8%
(99.3%–100%)
  • FDA symptomatic data
  • I couldn’t find any data on OSOM (Covid & Flu)’s sensitivity or specificity for asymptomatic people during Omicron, so I assumed it was typical of RATs in the Omicron period. (See “Other RAT” row.)
Other RAT (Rapid Antigen Test) 87.5%
(80.8%–92.1%)		 99.9%
(99.7%–100%)		 25.2%
(21.5%–29.3%)		 99.8%
(99.6%–100%)
Pluslife 98.7%
(93.1%–99.8%)		 99.3%
(98.7%–99.6%)		 97.3%
(86.2%–99.5%)		 99.3%
(98.7%–99.6%)
  • In my opinion, this is the most comprehensive clinical study of Pluslife and one that was done in a way that’s most directly comparable to the rigorous FDA studies of various other test brands. It found a sensitivity of 98.3% (95% CI: 93.9%–99.5%) and specificity of 99.3% (95% CI: 98.7%–99.6%). However, as with every other published Pluslife study, it does not break down testing data by symptom status. (Unlike these other studies, it does indicate that it included both symptomatic and asymptomatic participants.) Since specificity generally does not vary much by symptom status, it’s reasonable to treat this specificity as that for both symptomatic and asymptomatic people. But, since asymptomatic infections are generally more difficult to detect than symptomatic infections, it’s reasonable to treat the sensitivity for symptomatic infections as greater than 98.3% and the sensitivity for asymptomatic infections as less than 98.3%. So, while it’s somewhat artificial, I think it’s reasonable to put Pluslife’s sensitivity for symptomatic people at about 99%. Since the aforementioned study involved 115 PCR-positive individuals, if we make the assumption that about 32% of currently infected individuals do not show symptoms, it follows that about 78 of them were symptomatic and 37 of them were not. This yields a sensitivity of 98.7% (95% CI: 93.1%–99.8%) for symptomatic people and a sensitivity of 97.3% (95% CI: 86.2%–99.5%) for asymptomatic individuals.
WELLLife (Covid & Flu) 87.5%
(80.7%–92.2%)		 99.7%
(98.9%–99.9%)		 25.2%
(21.5%–29.3%)		 99.8%
(99.6%–100%)
  • FDA symptomatic testing data
  • I couldn’t find any data on WELLLife (Covid & Flu)’s sensitivity or specificity for asymptomatic people during Omicron, so I assumed it was typical of RATs in the Omicron period. (See “Other RAT” row.)

If you have any questions, comments, or encounter any issues with the site, please reach out at yourcovidrisk@gmail.com. Your feedback is important and will help improve the tool.