Nothing
README.md
In SEIRfansy: Extended Susceptible-Exposed-Infected-Recovery Model
R package SEIRfansy
Extended Susceptible-Exposed-Infected-Recovery Model
Overview
This R package fits Extended Susceptible-Exposed-Infected-Recovery
(SEIR) Models for handling high false negative rate and symptom based
administration of diagnostic tests.
Installation
If the devtools package is not yet installed, install it first:
install.packages('devtools')
# install SEIRfansy from Github:
devtools::install_github('umich-biostatistics/SEIRfansy')
Once installed, load the package:
library(SEIRfansy)
#> Registered S3 methods overwritten by 'car':
#> method from
#> influence.merMod lme4
#> cooks.distance.influence.merMod lme4
#> dfbeta.influence.merMod lme4
#> dfbetas.influence.merMod lme4
Example Usage
For this example, we use the built-in package data set covid19, which
contains dailies and totals of cases, recoveries, and deaths from the
COVID-19 outbreak in India from January 30 to September 21 of 2020.
Setup
You will need the dplyr package for this example.
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
Training data set:
For training data, we use cases from April 1 to June 30
train = covid19[which(covid19$Date == "01 April "):which(covid19$Date == "30 June "),]
Testing data set:
For testing data, we use cases from July 1 to July 31
test = covid19[which(covid19$Date == "01 July "):which(covid19$Date == "31 July "),]
Data format for multinomial and Poisson distribution:
train_multinom =
train %>%
rename(Confirmed = Daily.Confirmed,
Recovered = Daily.Recovered,
Deceased = Daily.Deceased) %>%
dplyr::select(Confirmed, Recovered, Deceased)
test_multinom =
test %>%
rename(Confirmed = Daily.Confirmed,
Recovered = Daily.Recovered,
Deceased = Daily.Deceased) %>%
dplyr::select(Confirmed, Recovered, Deceased)
train_pois =
train %>%
rename(Confirmed = Daily.Confirmed) %>%
dplyr::select(Confirmed)
Initialize parameters:
N = 1341e6 # population size of India
data_initial = c(2059, 169, 58, 424, 9, 11)
pars_start = c(c(1,0.8,0.6,0.4,0.2), c(0.2,0.2,0.2,0.25,0.2))
phases = c(1,15,34,48,62)
SEIRfansy()
If interest is in model estimation but not prediction, then use
SEIRfansy(). Otherwise, use SEIRfansy.predict() (see below).
?SEIRfansy
cov19est = SEIRfansy(data = train_multinom, init_pars = pars_start,
data_init = data_initial, niter = 1e3, BurnIn = 1e2,
model = "Multinomial", N = N, lambda = 1/(69.416 * 365),
mu = 1/(69.416 * 365), period_start = phases, opt_num = 1,
auto.initialize = TRUE, f = 0.15)
#> Finding MLE
#> 1 MLE run finished!
#>
#> MLE estimates :
#> beta = ( 0.18, 0.1, 0.25, 0.18, 0.18 )
#> r = ( 0.112, 0.531, 0.544, 0.65, 0.688 )
#>
#> MCMC:
#> Iter 100 A = 0 : 0.1772 0.1095 0.2517 0.1817 0.1749 0.1116 0.5091 0.5628
#> 0.6397 0.6707
#> Iter 200 A = 4e-04 : 0.1737 0.1133 0.2494 0.1829 0.1758 0.1117 0.4871 0.573
#> 0.6399 0.6686
#> Iter 300 A = 0.0026 : 0.1695 0.1169 0.2439 0.1847 0.177 0.1089 0.4676 0.5837
#> 0.6392 0.668
#> Iter 400 A = 9.7852 : 0.1669 0.1206 0.2406 0.189 0.1753 0.1101 0.4521 0.5945
#> 0.6368 0.6587
#> Iter 500 A = 0 : 0.1677 0.1241 0.232 0.1904 0.1777 0.1121 0.4379 0.597
#> 0.6393 0.652
#> Iter 600 A = 0 : 0.1688 0.1271 0.2261 0.19 0.1775 0.1147 0.4269 0.5992
#> 0.6358 0.6582
#> Iter 700 A = 0 : 0.1698 0.1299 0.2167 0.1931 0.1774 0.1163 0.4169 0.5985
#> 0.6415 0.6561
#> Iter 800 A = 96.4675 : 0.1682 0.134 0.2131 0.1941 0.1767 0.1155 0.4023
#> 0.6021 0.6432 0.6468
#> Iter 900 A = 0 : 0.1666 0.1409 0.2037 0.1955 0.178 0.1165 0.3833 0.5982
#> 0.6422 0.6415
#> Iter 1000 A = 0 : 0.1684 0.1432 0.1976 0.196 0.1792 0.1176 0.3724 0.5994
#> 0.6399 0.6398
#> Iter 1100 A = 4e-04 : 0.169 0.1467 0.1945 0.1969 0.1791 0.1154 0.3597 0.5875
#> 0.6358 0.6282
Inspect the results:
names(cov19est)
class(cov19est$mcmc_pars)
names(cov19est$plots)
Plot the results:
plot(cov19est, type = "trace")
plot(cov19est, type = "boxplot")
SEIRfansy.predict()
If interest is in model estimation and prediction, then use
SEIRfansy.predict(), which first runs SEIRfansy() internally, and
then predicts.
?SEIRfansy.predict
cov19pred = SEIRfansy.predict(data = train_multinom, init_pars = pars_start,
data_init = data_initial, T_predict = 60, niter = 1e3,
BurnIn = 1e2, data_test = test_multinom, model = "Multinomial",
N = N, lambda = 1/(69.416 * 365), mu = 1/(69.416 * 365),
period_start = phases, opt_num = 1,
auto.initialize = TRUE, f = 0.15)
#> Estimating ...
#>
#> Finding MLE
#> 1 MLE run finished!
#>
#> MLE estimates :
#> beta = ( 0.18, 0.1, 0.25, 0.18, 0.18 )
#> r = ( 0.112, 0.531, 0.544, 0.65, 0.688 )
#>
#> MCMC:
#> Iter 100 A = 0 : 0.176 0.1073 0.2544 0.1814 0.1747 0.11 0.5132 0.5471 0.6516
#> 0.6921
#> Iter 200 A = 1.683785e+12 : 0.1725 0.1105 0.2518 0.1851 0.1755 0.1104 0.4926
#> 0.5624 0.6498 0.6774
#> Iter 300 A = 0 : 0.1696 0.1153 0.2439 0.1879 0.1762 0.1119 0.4711 0.5755
#> 0.6533 0.6663
#> Iter 400 A = 0 : 0.1712 0.1188 0.2364 0.188 0.177 0.1119 0.4573 0.5834
#> 0.6465 0.6702
#> Iter 500 A = 0 : 0.1715 0.1215 0.2279 0.188 0.1772 0.111 0.4472 0.5995
#> 0.6534 0.6777
#> Iter 600 A = 498.6388 : 0.1699 0.1239 0.2246 0.1926 0.1756 0.1132 0.435
#> 0.6094 0.6533 0.6732
#> Iter 700 A = 0.0217 : 0.1689 0.1283 0.2174 0.1941 0.1781 0.1128 0.4204
#> 0.6056 0.655 0.6553
#> Iter 800 A = 407.4618 : 0.1703 0.1327 0.2114 0.1931 0.179 0.1147 0.4011
#> 0.6058 0.6508 0.6497
#> Iter 900 A = 1947.649 : 0.1703 0.1363 0.2038 0.195 0.1782 0.1166 0.3894
#> 0.6122 0.6487 0.6557
#> Iter 1000 A = 0 : 0.1696 0.1394 0.2005 0.1945 0.1784 0.1156 0.3812 0.61
#> 0.6448 0.6528
#> Iter 1100 A = 0 : 0.1737 0.1424 0.1936 0.1941 0.18 0.1164 0.3638 0.6017
#> 0.6499 0.6422
#>
#> Predicting ...
Inspect the results:
names(cov19pred)
class(cov19pred$prediction)
class(cov19pred$mcmc_pars)
names(cov19pred$plots)
Plot the results:
plot(cov19pred, type = "trace")
plot(cov19pred, type = "boxplot")
plot(cov19pred, type = "panel")
plot(cov19pred, type = "cases")
Current Suggested Citation
Ritwik Bhaduri, Ritoban Kundu, Soumik Purkayastha, Mike Kleinsasser,
Lauren J Beesley, Bhramar Mukherjee. “EXTENDING THE
SUSCEPTIBLE-EXPOSED-INFECTED-REMOVED(SEIR) MODEL TO HANDLE THE HIGH
FALSE NEGATIVE RATE AND SYMPTOM-BASED ADMINISTRATION OF COVID-19
DIAGNOSTIC TESTS: SEIR-fansy.” medRxiv 2020.09.24.20200238; doi:
https://doi.org/10.1101/2020.09.24.20200238
Try the SEIRfansy package in your browser
Any scripts or data that you put into this service are public.
SEIRfansy documentation built on Sept. 27, 2021, 5:10 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
R package SEIRfansy
Extended Susceptible-Exposed-Infected-Recovery Model
Overview
This R package fits Extended Susceptible-Exposed-Infected-Recovery
(SEIR) Models for handling high false negative rate and symptom based
administration of diagnostic tests.
Installation
If the devtools package is not yet installed, install it first:
install.packages('devtools')
# install SEIRfansy from Github:
devtools::install_github('umich-biostatistics/SEIRfansy')
Once installed, load the package:
library(SEIRfansy)
#> Registered S3 methods overwritten by 'car':
#> method from
#> influence.merMod lme4
#> cooks.distance.influence.merMod lme4
#> dfbeta.influence.merMod lme4
#> dfbetas.influence.merMod lme4
Example Usage
For this example, we use the built-in package data set covid19, which
contains dailies and totals of cases, recoveries, and deaths from the
COVID-19 outbreak in India from January 30 to September 21 of 2020.
Setup
You will need the dplyr package for this example.
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
Training data set:
For training data, we use cases from April 1 to June 30
train = covid19[which(covid19$Date == "01 April "):which(covid19$Date == "30 June "),]
Testing data set:
For testing data, we use cases from July 1 to July 31
test = covid19[which(covid19$Date == "01 July "):which(covid19$Date == "31 July "),]
Data format for multinomial and Poisson distribution:
train_multinom =
train %>%
rename(Confirmed = Daily.Confirmed,
Recovered = Daily.Recovered,
Deceased = Daily.Deceased) %>%
dplyr::select(Confirmed, Recovered, Deceased)
test_multinom =
test %>%
rename(Confirmed = Daily.Confirmed,
Recovered = Daily.Recovered,
Deceased = Daily.Deceased) %>%
dplyr::select(Confirmed, Recovered, Deceased)
train_pois =
train %>%
rename(Confirmed = Daily.Confirmed) %>%
dplyr::select(Confirmed)
Initialize parameters:
N = 1341e6 # population size of India
data_initial = c(2059, 169, 58, 424, 9, 11)
pars_start = c(c(1,0.8,0.6,0.4,0.2), c(0.2,0.2,0.2,0.25,0.2))
phases = c(1,15,34,48,62)
SEIRfansy()
If interest is in model estimation but not prediction, then use
SEIRfansy(). Otherwise, use SEIRfansy.predict() (see below).
?SEIRfansy
cov19est = SEIRfansy(data = train_multinom, init_pars = pars_start,
data_init = data_initial, niter = 1e3, BurnIn = 1e2,
model = "Multinomial", N = N, lambda = 1/(69.416 * 365),
mu = 1/(69.416 * 365), period_start = phases, opt_num = 1,
auto.initialize = TRUE, f = 0.15)
#> Finding MLE
#> 1 MLE run finished!
#>
#> MLE estimates :
#> beta = ( 0.18, 0.1, 0.25, 0.18, 0.18 )
#> r = ( 0.112, 0.531, 0.544, 0.65, 0.688 )
#>
#> MCMC:
#> Iter 100 A = 0 : 0.1772 0.1095 0.2517 0.1817 0.1749 0.1116 0.5091 0.5628
#> 0.6397 0.6707
#> Iter 200 A = 4e-04 : 0.1737 0.1133 0.2494 0.1829 0.1758 0.1117 0.4871 0.573
#> 0.6399 0.6686
#> Iter 300 A = 0.0026 : 0.1695 0.1169 0.2439 0.1847 0.177 0.1089 0.4676 0.5837
#> 0.6392 0.668
#> Iter 400 A = 9.7852 : 0.1669 0.1206 0.2406 0.189 0.1753 0.1101 0.4521 0.5945
#> 0.6368 0.6587
#> Iter 500 A = 0 : 0.1677 0.1241 0.232 0.1904 0.1777 0.1121 0.4379 0.597
#> 0.6393 0.652
#> Iter 600 A = 0 : 0.1688 0.1271 0.2261 0.19 0.1775 0.1147 0.4269 0.5992
#> 0.6358 0.6582
#> Iter 700 A = 0 : 0.1698 0.1299 0.2167 0.1931 0.1774 0.1163 0.4169 0.5985
#> 0.6415 0.6561
#> Iter 800 A = 96.4675 : 0.1682 0.134 0.2131 0.1941 0.1767 0.1155 0.4023
#> 0.6021 0.6432 0.6468
#> Iter 900 A = 0 : 0.1666 0.1409 0.2037 0.1955 0.178 0.1165 0.3833 0.5982
#> 0.6422 0.6415
#> Iter 1000 A = 0 : 0.1684 0.1432 0.1976 0.196 0.1792 0.1176 0.3724 0.5994
#> 0.6399 0.6398
#> Iter 1100 A = 4e-04 : 0.169 0.1467 0.1945 0.1969 0.1791 0.1154 0.3597 0.5875
#> 0.6358 0.6282
Inspect the results:
names(cov19est)
class(cov19est$mcmc_pars)
names(cov19est$plots)
Plot the results:
plot(cov19est, type = "trace")
plot(cov19est, type = "boxplot")
SEIRfansy.predict()
If interest is in model estimation and prediction, then use
SEIRfansy.predict(), which first runs SEIRfansy() internally, and
then predicts.
?SEIRfansy.predict
cov19pred = SEIRfansy.predict(data = train_multinom, init_pars = pars_start,
data_init = data_initial, T_predict = 60, niter = 1e3,
BurnIn = 1e2, data_test = test_multinom, model = "Multinomial",
N = N, lambda = 1/(69.416 * 365), mu = 1/(69.416 * 365),
period_start = phases, opt_num = 1,
auto.initialize = TRUE, f = 0.15)
#> Estimating ...
#>
#> Finding MLE
#> 1 MLE run finished!
#>
#> MLE estimates :
#> beta = ( 0.18, 0.1, 0.25, 0.18, 0.18 )
#> r = ( 0.112, 0.531, 0.544, 0.65, 0.688 )
#>
#> MCMC:
#> Iter 100 A = 0 : 0.176 0.1073 0.2544 0.1814 0.1747 0.11 0.5132 0.5471 0.6516
#> 0.6921
#> Iter 200 A = 1.683785e+12 : 0.1725 0.1105 0.2518 0.1851 0.1755 0.1104 0.4926
#> 0.5624 0.6498 0.6774
#> Iter 300 A = 0 : 0.1696 0.1153 0.2439 0.1879 0.1762 0.1119 0.4711 0.5755
#> 0.6533 0.6663
#> Iter 400 A = 0 : 0.1712 0.1188 0.2364 0.188 0.177 0.1119 0.4573 0.5834
#> 0.6465 0.6702
#> Iter 500 A = 0 : 0.1715 0.1215 0.2279 0.188 0.1772 0.111 0.4472 0.5995
#> 0.6534 0.6777
#> Iter 600 A = 498.6388 : 0.1699 0.1239 0.2246 0.1926 0.1756 0.1132 0.435
#> 0.6094 0.6533 0.6732
#> Iter 700 A = 0.0217 : 0.1689 0.1283 0.2174 0.1941 0.1781 0.1128 0.4204
#> 0.6056 0.655 0.6553
#> Iter 800 A = 407.4618 : 0.1703 0.1327 0.2114 0.1931 0.179 0.1147 0.4011
#> 0.6058 0.6508 0.6497
#> Iter 900 A = 1947.649 : 0.1703 0.1363 0.2038 0.195 0.1782 0.1166 0.3894
#> 0.6122 0.6487 0.6557
#> Iter 1000 A = 0 : 0.1696 0.1394 0.2005 0.1945 0.1784 0.1156 0.3812 0.61
#> 0.6448 0.6528
#> Iter 1100 A = 0 : 0.1737 0.1424 0.1936 0.1941 0.18 0.1164 0.3638 0.6017
#> 0.6499 0.6422
#>
#> Predicting ...
Inspect the results:
names(cov19pred)
class(cov19pred$prediction)
class(cov19pred$mcmc_pars)
names(cov19pred$plots)
Plot the results:
plot(cov19pred, type = "trace")
plot(cov19pred, type = "boxplot")
plot(cov19pred, type = "panel")
plot(cov19pred, type = "cases")
Current Suggested Citation
Ritwik Bhaduri, Ritoban Kundu, Soumik Purkayastha, Mike Kleinsasser, Lauren J Beesley, Bhramar Mukherjee. “EXTENDING THE SUSCEPTIBLE-EXPOSED-INFECTED-REMOVED(SEIR) MODEL TO HANDLE THE HIGH FALSE NEGATIVE RATE AND SYMPTOM-BASED ADMINISTRATION OF COVID-19 DIAGNOSTIC TESTS: SEIR-fansy.” medRxiv 2020.09.24.20200238; doi: https://doi.org/10.1101/2020.09.24.20200238
Try the SEIRfansy package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.