Processing age to print its distribution

Description

Processing age to print its distribution

Usage

age.process(age)

Arguments

Value

each age is either repeated 10 times or expanded to 10 numbers (for example, 30s is expanded to 30, 31, ..., 39).

A package for analyzing early epidemic data

Description

The BETSbets.covid19 package provides likelihood inference for early epidemic data with four key epidemiological events: Beginning of exposure, End of exposure, time of Transmission, and time of Symptom onset. It jointly estimates the epidemic doubling time and incubation period and is able to correct for different kinds of sample selection.

References

Qingyuan Zhao, Nianqiao Ju, Sergio Bacallado, and Rajen Shah. "BETS: The dangers of selection bias in early analyses of the coronavirus disease (COVID-19) pandemic", 2020. arXiv:2004.07743.

Likelihood inference

Description

Likelihood inference

Usage

bets.inference(
  data,
  likelihood = c("conditional", "unconditional"),
  ci = c("lrt", "point", "bootstrap"),
  M = Inf,
  r = NULL,
  L = NULL,
  level = 0.95,
  bootstrap = 1000,
  mc.cores = 1
)

Arguments

Details

The confidence interval is either not computed ("point"), or computed by inverting the likelihood ratio test ("lrt") or basic bootstrap ("bootstrap")

Value

Results of the likelihood inference, including maximum likelihood estimators and individual confidence intervals for the model parameters based on inverting the likelihood ratio test.

Examples

data(wuhan_exported)

data <- subset(wuhan_exported, Location == "Hefei")
data$B <- data$B - 0.75
data$E <- data$E - 0.25
data$S <- data$S - 0.5

# Conditional likelihood inference
bets.inference(data, "conditional")
bets.inference(data, "conditional", "bootstrap", bootstrap = 100, level = 0.5)

# Unconditional likelihood inference
bets.inference(data, "unconditional", L = 54)

# Conditional likelihood inference for data with right truncation
bets.inference(subset(data, S <= 60), "conditional", M = 60)

# Conditional likelihood inference with r fixed at 0 (not recommended)
bets.inference(data, "conditional", r = 0)

(Profile) Likelihood function

Description

(Profile) Likelihood function

Usage

bets.likelihood(
  params,
  data,
  likelihood = c("conditional", "unconditional"),
  M = Inf,
  r = NULL,
  L = NULL,
  params_init = NULL
)

Arguments

Details

Non-default values of M and r are only available for conditional likelihood.

Value

Log-likelihood function if params has all four entries, rho, r, ip_q50, ip_q95 (or three entires—r, ip_q50, ip_q95—if computing the conditional likelihood). Otherwise returns the profile likelihood for the parameters in params.

Examples

data(wuhan_exported)

data <- wuhan_exported
data$B <- data$B - 0.75
data$E <- data$E - 0.25
data$S <- data$S - 0.5

params <- c(r = 0.2,
            ip_q50 = 5,
            ip_q95 = 12)

# Conditional likelihood
bets.likelihood(params, data)

# Conditional likelihood with right truncation
bets.likelihood(params, subset(data, S <= 60), M = 60)

# Conditional likelihood with fixed r (not recommended)
bets.likelihood(params, data, r = 0)

# Unconditional likelihood
params["rho"] <- 1
bets.likelihood(params, data, likelihood = "unconditional", L = 54)

# Profile conditional likelihood
bets.likelihood(c(r = 0.2), data, params_init = params)

(Profile) Conditional likelihood given B and E

Description

(Profile) Conditional likelihood given B and E

Usage

bets.likelihood.conditional(
  params,
  data,
  M = Inf,
  r = NULL,
  params_init = NULL
)

Arguments

Value

Conditional log-likelihood.

Approximate profile likelihood

Description

Approximate profile likelihood

Usage

bets.likelihood.unconditional(params, data, params_init = NULL, L = NULL)

Arguments

Value

When params contains all the parameters (rho, r, ip_q50, ip_q95), returns the approximate log-likelihood of data. When params contains some but not all the parameters, returns the profile log-likelihood.

Confirmed cases of COVID-19

Description

A dataset containing the trajectory of cases of COVID-19.

Usage

covid19_data

Format

A data frame with 1091 rows and 20 variables:

Case

Label of the case, in the format of Country-Case number.

Nationality/Residence

Nationality or residence of the patient.

Gender

Male (M) or Female (F).

Age

Age of the patient, either an integer or age by decade (for example, 40s).

Cluster

Other confirmed cases that this patient had contacts with.

Known Contact

Whether the case has contact with earlier confirmed cases or visited Hubei province.

Outside

Was the patient infected outside Wuhan? Yes (Y), Likely (L), or No (empty string and the default).

Begin_Wuhan

Begin of stay in Wuhan.

End_Wuhan

End of Stay in Wuhan.

Infected

When was the patient infected? Can be an interval or multiple dates.

Arrived

When did the patient arrive in the country where he/she was confirmed a 2019-nCoV case?

Symptom

When did the patient first show symptoms of 2019-nCoV (cough, fever, fatigue, etc.)?

Initial

After developing symptoms, when was the patient first went to (or taken to) a medical institution?

Hospital

If the patient was not admitted to or isolated in a hospital after the initial medical visit, when was the patient finally admitted or isolated?

Confirmed

When was the patient confirmed as a case of 2019-nCoV?

Discharged

When was the patient discharged from hospital?

Death

When did the patient die?

Verified

Has this information been verified by another data collector?

Source

URLs to the information recorded (usually government websites or news reports).

Transform date to numeric

Description

Transform date to numeric

Usage

date.process(date)

Arguments

Value

a vector of days since December 1st, 2019 (or example, 23-Jan is converted to 23+31 = 52).

Parse the infected date

Description

Deprecated. Used in the previous analysis, now replaced by preprocess.data.

Usage

parse.infected(data)

Arguments

Value

the data frame with two new columns, Infected_first and Infected_last

Parse infected date (basic)

Description

Parse infected date (basic)

Usage

parse.one.infected(infected)

Arguments

Value

A vector of length 2 for the infection window

Prepare data frame for analysis

Description

Prepare data frame for analysis

Usage

preprocess.data(
  data,
  infected_in = c("Wuhan", "Outside"),
  symptom_impute = FALSE
)

Arguments

Details

A summary of the procedures:

1. Convert all dates to number of days since 1-Dec-2019.

2. Separates data into those returned from Wuhan and those infected outside of wuhan.

3. Restrict to cases with a known symptom onset date.

4. Parse column 'Infected' into two columns: Infected_first and Infected_last.

5. For all cases, set Infected_first to 1 if it is missing.

6. For outside cases, set Infected_last to be no later than symptom onset.

7. For Wuhan-exported cases, set Infected_last to no later than symptom onset and end of Wuhan stay.

Value

A data frame

Author(s)

Nianqiao Ju <nju@g.harvard.edu>, Qingyuan Zhao <qyzhao@statslab.cam.ac.uk>

Examples

data(covid19_data)
head(data <- preprocess.data(covid19_data))

 ## This is how the wuhan_exported data frame is created
data <- subset(data, Symptom < Inf)
data <- subset(data, Arrived <= 54)
data$Location <- do.call(rbind, strsplit(as.character(data$Case), "-"))[, 1]
wuhan_exported <- data.frame(Location = data$Location,
                             B = data$Begin_Wuhan,
                             E = data$End_Wuhan,
                             S = data$Symptom)
## devtools::use_data(wuhan_exported)

Simulate case information from the generative BETS model

Description

Simulate case information from the generative BETS model

Usage

## S3 method for class 'case'
simulate(
  n = 1e+07,
  params = c(pi = 0.1, lambda_w = 5e-04, lambda_v = 0.001, kappa = 4e-08, r = 0.2, alpha
    = 9, beta = 1.5, L = 53.5)
)

COVID-19 exported from Wuhan

Description

Constructed from covid19_data, see example(preprocess.data).

Usage

wuhan_exported

Format

A data frame with 378 rows and 4 variables:

Location

Where the case is confirmed.

Gender

Gender of the patient.

Age

Age of the patient.

B

Beginning of stay in Wuhan.

E

End of stay in Wuhan.

S

Symptom onset.