Package 'covidprobability'

Calculate pretest probability change over time

Description

Calculates the pretest probability over time, assuming the individual does not develop symptoms, by taking into account the distribution of incubation periods (defined as the time from exposure to symptom onset).

Usage

adjust_pretest(pre0, asympt, days = 14, mu = 1.63, sigma = 0.5)

Arguments

pre0	Initial pretest probability (on day of exposure)
asympt	The proportion of positive patients who would be expected not to ever develop symptoms (true asymptomatic patients).
days	Days since exposure for calculation range
mu	The mean of a lognormal distribution that approximates the incubation period for COVID-19. E.g. 1.63 (see reference).
sigma	The standard deviation of a lognormal distribution that approximates the incubation period for COVID-19. E.g. 0.5 (see reference).

Value

pretest probability by day (time series)

References

See McAloon et al. https://bmjopen.bmj.com/content/10/8/e039652/

---

Calculate posttest probability from pretest probability and test characteristics

Description

Calculate posttest probability from pretest probability and test characteristics

Usage

calc_postest_prob(pretest_prob, sens, spec)

Arguments

pretest_prob	Pretest probability
sens	Test sensitivity
spec	Test specificity

Value

posttest probability

---

Calculate a time series of probability for an individual following exposure

Description

The probability that an individual has COVID-19 will change over time as new information is gleaned. The initial probability is the pretest probability (pre0) associated with the nature of the interaction/exposure. This probability will decrease with each passing day that the individual does not develop symptoms. When a test is done, the probability is the posttest probability; this reduces the probability based on the test characteristics at the time of testing. Subsequently, the probability will continue to decrease with each passing day that no symptoms develop. This function returns a time series including those 3 phases.

Usage

individual_probability(test_day, pre0, sens, spec, asympt, days, mu, sigma)

Arguments

test_day	Day of PCR test (days since exposure)
pre0	Pre-test probability of person on day of exposure
sens	A vector of sensitivities by day since exposure
spec	The specificity of the PCR test
asympt	The proportion of infected patients expected to remain asymptomatic throughout the course of infection
days	Days since exposure for calculation range
mu	The mean of a lognormal distribution that approximates the incubation period for COVID-19. E.g. 1.63 (see reference).
sigma	The standard deviation of a lognormal distribution that approximates the incubation period for COVID-19. E.g. 0.5 (see reference).

Value

A time series of probabilities

---

Calculate post-test probability if testing occurred on each day in a series

Description

Given an initial pretest probability, and assuming symptoms never arise, with each passing day the pretest probability will be lower, given the person did not experience symptoms. This returns a vector of posttest probabilities which takes all of the above into account, assuming a negative test on each day. Note this is not a time series, and does not reflect if serial testing were done each day and assumes testing was only done once.

Usage

posttest_series(pre0, asympt, days = 14, mu = 1.63, sigma = 0.5, sens, spec)

Arguments

pre0	The pretest probability on day 0 (at exposure)
asympt	The proportion of infected patients expected to remain asymptomatic throughout the course of infection
days	Days since exposure for calculation range
mu	The mean of a lognormal distribution that approximates the incubation period for COVID-19. E.g. 1.63 (see reference).
sigma	The standard deviation of a lognormal distribution that approximates the incubation period for COVID-19. E.g. 0.5 (see reference).
sens	A vector of sensitivities by day since exposure
spec	The test specificity

Value

A vector of posttest probabilities

---

Find the probability of any (at least one) event happening

Description

For an event that occurs with probability p, this function returns the probability of an occurrence given n repetitions. p is numeric and can be a vector.

Usage

probability_any(n, p)

Arguments

n	The number of times to repeat the event (independent)
p	The individual probability of the event happening

Details

The probability that any event p occurs with n repetitions is equal to the reciprocal of the probability that p never occurs. The probability that p never occurs with n repetitions is (1 - p) ^ n. Thus, the probability that any event p occurs after n repetitions is 1 - ( (1 - p) ^ n ).

Value

The probability of an event with the specified probability, after n repetitions

Examples

probability_any(1, 0.5)
probability_any(2, 0.5)
probability_any(2, c(0.5, 1/3, 0.25))

---

The remaining individuals who would not be expected to show symptoms yet

Description

Every day, a certain number of people are expected to show symptoms, based on the incubation period. This would typically lead to further investigation and ongoing suspicion of an outbreak. This function calculates the proportion of individuals on a given day that would not be expected to have developed symptoms yet. So if no one has developed symptoms, this proportion of people could still have undetected COVID-19.

Usage

prop_remaining(t, asympt, mu = 1.63, sigma = 0.5)

Arguments

t	day
asympt	The proportion of positive patients who would be expected not to ever develop symptoms (true asymptomatic patients).
mu	The mean of a lognormal distribution that approximates the incubation period for COVID-19. E.g. 1.63 (see reference).
sigma	The standard deviation of a lognormal distribution that approximates the incubation period for COVID-19. E.g. 0.5 (see reference).

Value

Proportion who would not be expected to show symptoms yet

References

See McAloon et al. https://bmjopen.bmj.com/content/10/8/e039652/

---

COVID-19 PCR sensitivity by days since exposure

Description

COVID-19 PCR sensitivity by days since exposure

Usage

sens

Format

A data frame with 21 rows and 3 variables:

point

point estimate of sensitivity

lower

lower 95% confidence interval of sensitivity

upper

upper 95% confidence interval of sensitivity

Source

https://github.com/HopkinsIDD/covidRTPCR

---

Calculate a time series of unit-wide probability following exposure

Description

To calculate the probability that any asymptomatic person has COVID-19, this function treats each person/exposure as independent events and calculates the probability time series using the individuals time series from individual_probability().

Usage

unit_probability(test_day, pre0, sens, spec, asympt, days, mu, sigma, n)

Arguments

test_day	Day of PCR test (days since exposure)
pre0	Pre-test probability of person on day of exposure
sens	A vector of sensitivities by day since exposure
spec	The specificity of the PCR test
asympt	The proportion of infected patients expected to remain asymptomatic throughout the course of infection
days	Days since exposure for calculation range
mu	The mean of a lognormal distribution that approximates the incubation period for COVID-19. E.g. 1.63 (see reference).
sigma	The standard deviation of a lognormal distribution that approximates the incubation period for COVID-19. E.g. 0.5 (see reference).
n	Number of exposed individuals

Value

The probability of an event with the specified probability, after n repetitions

---